Preface The authors are interested in the way that connectionism (the study of artificial neural systems) has affected and is likely to affect arguments in cognitive science (the science of thought). The point of view taken is that the addition of neural systems will extend older models and that connectionism will redirect the course of cognitive science. The book takes the somewhat unusual stance of trying to discuss existing cognitive science and neural systems from the perspective of a common model. The model designed for this purpose is the 'Neural State Machine Model' (NSMM) . Chapter 1. Introduction: The Stuff that Mind is Made Of An overview of the issues covered by the book. 1.1 String, Sealing Wax, Neurons and Symbols 1.2 Connectionist Euphoria: The Return of Neurons 1.3 Artificial Intelligence - The House of Symbols A simplified model of the artificial neuron is first presented. The model will be expanded in a later chapter , but we start out slowly. 1.4 Cognition: A Symbolic Science? 1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Preface The authors are interested in the way that connectionism (the study of artificial neural systems) has affected and is likely to affect arguments in cognitive science (the science of thought). The point of view taken is that the addition of neural systems will extend older models and that connectionism will redirect the course of cognitive science.
The book takes the somewhat unusual stance of trying to discuss existing cognitive science and neural systems from the perspective of a common model. The model designed for this purpose is the 'Neural State Machine Model' (NSMM) .
Chapter 1. Introduction: The Stuff that Mind is Made Of An overview of the issues covered by the book. 1.1 String, Sealing Wax, Neurons and Symbols 1.2 Connectionist Euphoria: The Return of Neurons 1.3 Artificial Intelligence - The House of Symbols A simplified model of the artificial neuron is first presented. The model will be expanded in a later chapter , but we start out slowly. 1.4 Cognition: A Symbolic Science? 1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 1. Introduction: The Stuff that Mind is Made Of An overview of the issues covered by the book.
1.1 String, Sealing Wax, Neurons and Symbols 1.2 Connectionist Euphoria: The Return of Neurons 1.3 Artificial Intelligence - The House of Symbols A simplified model of the artificial neuron is first presented. The model will be expanded in a later chapter , but we start out slowly. 1.4 Cognition: A Symbolic Science? 1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.1 String, Sealing Wax, Neurons and Symbols
1.2 Connectionist Euphoria: The Return of Neurons 1.3 Artificial Intelligence - The House of Symbols A simplified model of the artificial neuron is first presented. The model will be expanded in a later chapter , but we start out slowly. 1.4 Cognition: A Symbolic Science? 1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.2 Connectionist Euphoria: The Return of Neurons
1.3 Artificial Intelligence - The House of Symbols A simplified model of the artificial neuron is first presented. The model will be expanded in a later chapter , but we start out slowly. 1.4 Cognition: A Symbolic Science? 1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.3 Artificial Intelligence - The House of Symbols A simplified model of the artificial neuron is first presented. The model will be expanded in a later chapter , but we start out slowly.
1.4 Cognition: A Symbolic Science? 1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.4 Cognition: A Symbolic Science?
1.5 Automata: The Ghosts of All Machines 1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.5 Automata: The Ghosts of All Machines
1.6 The Great Debate 1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.6 The Great Debate
1.7 The Illusion in the Divide 1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.7 The Illusion in the Divide
1.8 Language and Neurons 1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.8 Language and Neurons
1.9 Seeing and Thinking 1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.9 Seeing and Thinking
1.10 Artificial Consciousness: A Framework for Cognitive Science Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
1.10 Artificial Consciousness: A Framework for Cognitive Science
Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks. 2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 2. Artificial Neural Nets The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks.
2.1 How to Get Rid of the Frills 2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.1 How to Get Rid of the Frills
2.2 The Minimal Neuron 2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.2 The Minimal Neuron
2.3 Multi-layer Networks 2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.3 Multi-layer Networks
2.4 Feedback: The Mechanism of the Inner Image 2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.4 Feedback: The Mechanism of the Inner Image
2.5 A General Neural Unit 2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.5 A General Neural Unit
2.6 The Simple Retrieval of Internal States 2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.6 The Simple Retrieval of Internal States
2.7 Input Sequences - The Beginnings of Language 2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.7 Input Sequences - The Beginnings of Language
2.8 Learning to Act 2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.8 Learning to Act
2.9 The Cognitive Properties of the General Neural Unit Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
2.9 The Cognitive Properties of the General Neural Unit
Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field. 3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 3. Artificial Intelligence This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field.
3.1 The Fairground Machinery 3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.1 The Fairground Machinery
3.2 The Turing Legacy 3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.2 The Turing Legacy
3.3 Intelligence and the Playing of Games 3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.3 Intelligence and the Playing of Games
3.4 Mechanical Problem Solving 3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.4 Mechanical Problem Solving
3.5 Logic and the Meaning of Sentences 3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.5 Logic and the Meaning of Sentences
3.6 Scripts in One's Head 3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.6 Scripts in One's Head
3.7 The Wisdom of Experts 3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.7 The Wisdom of Experts
3.8 The Artificial Act of Seeing 3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.8 The Artificial Act of Seeing
3.9 Criticisms 3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.9 Criticisms
3.10 A Neural Perspective on AI Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
3.10 A Neural Perspective on AI
Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field. 4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 4. Cognitive Science This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field.
4.1 How New Is the Mind's New Science? 4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.1 How New Is the Mind's New Science?
4.2 The Philosophy of Cognition 4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.2 The Philosophy of Cognition
4.3 Psychology as Science 4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.3 Psychology as Science
4.4 Cognitive Science - Logical Metaphors 4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.4 Cognitive Science - Logical Metaphors
4.5 Representing Knowledge 4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.5 Representing Knowledge
4.6 The Mind Machine 4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.6 The Mind Machine
4.7 Memories Are Made of This 4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.7 Memories Are Made of This
4.8 Solving More Problems 4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.8 Solving More Problems
4.9 A Postscript on Cognitive Science Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
4.9 A Postscript on Cognitive Science
Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting. 5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 5. Neural Automata This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting.
5.1 Introduction: Automata - Not Zombies but Logical Machines 5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.1 Introduction: Automata - Not Zombies but Logical Machines
5.2 Neural Logic 5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.2 Neural Logic
5.3 The Formal Automaton 5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.3 The Formal Automaton
5.4 Synchonicity and Probabilism 5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.4 Synchonicity and Probabilism
5.5 Automata and Formal Languages 5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.5 Automata and Formal Languages
5.6 Non-Determinism and Other Little Problems 5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.6 Non-Determinism and Other Little Problems
5.7 Working with Automata: From Identification to the Nature of Dreams 5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.7 Working with Automata: From Identification to the Nature of Dreams
5.8 Genetic Automata 5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.8 Genetic Automata
5.9 Standing Back Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
5.9 Standing Back
Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7. 6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 6. The Great Debate This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7.
6.1 Introduction: An Alien's Tale 6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.1 Introduction: An Alien's Tale
6.2 The Distributed Memory Model 6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.2 The Distributed Memory Model
6.3 The Initial Skirmish: A Question of Levels 6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.3 The Initial Skirmish: A Question of Levels
6.4 Smolensky's Subsymbolic World 6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.4 Smolensky's Subsymbolic World
6.5 Cognitive Edifices Need Symbolic Bricks 6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.5 Cognitive Edifices Need Symbolic Bricks
6.6 A Few Points from the Arbiters 6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.6 A Few Points from the Arbiters
6.7 And the World Said ... 6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.7 And the World Said ...
6.8 And, Philosophically Speaking ... 6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.8 And, Philosophically Speaking ...
6.9 Looking Back Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
6.9 Looking Back
Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science. 7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 7. The Divide is Illusory This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science.
7.1 Introduction: No Weights, Just State Machines 7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.1 Introduction: No Weights, Just State Machines
7.2 The Neural Automaton Revisited 7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.2 The Neural Automaton Revisited
7.3 The Weightless/Weighted Distinction 7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.3 The Weightless/Weighted Distinction
7.4 Emergent Properties 7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4 Emergent Properties
7.4.1 Pattern Recognition 7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.1 Pattern Recognition
7.4.2 Re-entrant States 7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.2 Re-entrant States
7.4.3 Attractors 7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.3 Attractors
7.4.4 Memory 7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.4 Memory
7.4.5 Input Temporal Sensitivity 7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.5 Input Temporal Sensitivity
7.4.6 Output Sequences 7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.6 Output Sequences
7.4.7 Unsupervised Learning 7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.7 Unsupervised Learning
7.4.8 Capacity 7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.4.8 Capacity
7.5 Cognitive Competence 7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.5 Cognitive Competence
7.5.1 Pattern Recognition 7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.5.1 Pattern Recognition
7.5.2 Problem Solving 7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.5.2 Problem Solving
7.6 A Cognitive Neural Automaton 7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.6 A Cognitive Neural Automaton
7.6.1 Base Structure 7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.6.1 Base Structure
7.6.2 Explorative Learning Rules 7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.6.2 Explorative Learning Rules
7.6.3 Learning Under Instruction and Pre-Programming 7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.6.3 Learning Under Instruction and Pre-Programming
7.6.4 Some Implications 7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.6.4 Some Implications
7.7 Where Does This Leave Fodor and Pylyshyn's Attack? 7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.7 Where Does This Leave Fodor and Pylyshyn's Attack?
7.7.1 Association Leads to Structure I 7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.7.1 Association Leads to Structure I
7.7.2 Association Leads to Structure II 7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.7.2 Association Leads to Structure II
7.7.3 Learning is More Important than Appreciating Probabilities 7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.7.3 Learning is More Important than Appreciating Probabilities
7.7.4 Neural Systems Transcend the 'Implementation Level' I 7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.7.4 Neural Systems Transcend the 'Implementation Level' I
7.7.5 Neural Systems Transcend the 'Implementation Level' II 7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.7.5 Neural Systems Transcend the 'Implementation Level' II
7.8 The Neural Extension to Cognitive Science 7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.8 The Neural Extension to Cognitive Science
7.8.1 Learning 7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.8.1 Learning
7.8.2 Mental Imagery 7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.8.2 Mental Imagery
7.8.3 Cognitive Architectures 7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.8.3 Cognitive Architectures
7.9 A Plethora of Algorithms? Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
7.9 A Plethora of Algorithms?
Chapter 8. Language and Neurons 8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 8. Language and Neurons
8.1 Introduction: Flexing the Linguistic Muscles of the NSMM 8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.1 Introduction: Flexing the Linguistic Muscles of the NSMM
8.2 Symbolic versus Mental Views of Language 8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.2 Symbolic versus Mental Views of Language
8.3 Experiments in the Past Tense 8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.3 Experiments in the Past Tense
8.4 Who or What Broke the Window? 8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.4 Who or What Broke the Window?
8.5 I Thought I Saw a Pussy Cat ... 8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.5 I Thought I Saw a Pussy Cat ...
8.6 Composability 8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.6 Composability
8.7 Hidden Representations 8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.7 Hidden Representations
8.8 The Language of thought 8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.8 The Language of thought
8.9 Looking Back: Language and Connectionism and Icons Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
8.9 Looking Back: Language and Connectionism and Icons
Chapter 9. Seeing and Thinking 9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
Chapter 9. Seeing and Thinking
9.1 Introduction: Why Vision? 9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.1 Introduction: Why Vision?
9.2 The Physics of Seeing and the Chemistry of Experience 9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.2 The Physics of Seeing and the Chemistry of Experience
9.3 Binding Forces 9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.3 Binding Forces
9.4 A Rose is a Rose is ... 9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.4 A Rose is a Rose is ...
9.5 Learning From Deficits 9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.5 Learning From Deficits
9.6 Going to the Movies 9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.6 Going to the Movies
9.7 I Remember You ... 9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.7 I Remember You ...
9.7.1 Short- and Long-Term Memories 9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.7.1 Short- and Long-Term Memories
9.7.2 Memory Capacity 9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.7.2 Memory Capacity
9.7.3 Plasticity and Interference 9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.7.3 Plasticity and Interference
9.8 Mental Imagery: The Measurements 9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.8 Mental Imagery: The Measurements
9.9 Mental Imagery: Another Great Debate 9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.9 Mental Imagery: Another Great Debate
9.9.1 Cognitive Penetrability 9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.9.1 Cognitive Penetrability
9.9.2 Qualia 9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.9.2 Qualia
9.9.3 Economy 9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.9.3 Economy
9.9.4 Emotions and Moods 9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.9.4 Emotions and Moods
9.9.5 Interpretation 9.10 Looking Back at Seeing Things
9.9.5 Interpretation
9.10 Looking Back at Seeing Things