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How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics. In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to be prediction machines - devices that…mehr
How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics. In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation.
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Andy Clark is Professor of Logic and Metaphysics in the School of Philosophy, Psychology and Language Sciences, at Edinburgh University in Scotland. He is the author of Being There (1997), Mindware (2001), Natural-Born Cyborgs (2003), and Supersizing the Mind (2008). His interests include artificial intelligence, embodied cognition, robotics, and the predictive mind. In 2018 he was profiled in The New Yorker.
Inhaltsangabe
Table of Contents Preface: Meat That Predicts Acknowledgements Introduction: Guessing Games Part I: The Power of Prediction Chapter 1: Prediction Machines 1.1 Two Ways to Sense the Coffee 1.2 Adopting the Animal's Perspective 1.3 Learning in Bootstrap Heaven 1.4 Multi-Level Learning 1.5 Decoding Digits 1.6 Dealing With Structure 1.7 Predictive Processing 1.8 Signaling the News 1.9 Predicting Natural Scenes 1.10 Binocular Rivalry 1.11 Dampening and Sharpening 1.12 Encoding, inference, and the Bayesian Brain 1.13 Getting the Gist 1.14 Predictive Processing in the Brain 1.15 Is Silence Golden? 1.16 Expecting Faces 1.17 When Prediction Misleads 1.18 Mind Turned Upside Down Chapter Two: Adjusting The Volume (Noise, Signal, Attention) 2.1 Signal Spotting 2.2 Hearing Bing 2.3 The Delicate Dance between Top-down and Bottom-up. 2.4 Attention, Biased Competition, and Signal Enhancement 2.5 Sensory Integration and Coupling 2.6 A Taste of Action 2.7 Gaze Allocation: Doing What Comes Naturally 2.8 Circular Causation in the Perception-Attention-Action Loop 2.9 Mutual Assured Misunderstanding 2.10 Some Worries About Precision 2.11 The Unexpected Elephant 2.12 Some Pathologies of Precision 2.13 Beyond the Spotlight Chapter 3: The Imaginarium 3.1 The Many Benefits of Controlled Hallucination 3.2 Simple Seeing 3.3 Cross-Modal and Multi-Modal effects 3.4 Meta-Modal Effects 3.5 Perceiving Omissions 3.6 Expectations and Conscious Perception 3.7 The Perceiver as Imaginer 3.8 'Brain Reading' During Imagery and Perception 3.9 Inside the Dream Factory 3.10 PIMMS and the Past 3.11 Towards Mental Time Travel 3.12 A Cognitive Package Deal Part II: Embodying Prediction Chapter 4: Prediction for Action 4.1 Staying Ahead of the Break 4.2 Ticklish Tales 4.3 Forward Models 4.4 Optimal Feedback Control 4.5 Action-oriented Predictive Processing 4.6 Simplifying Control 4.7 Beyond Efference Copy 4.7 Doing Without Cost Functions 4.8 Action-oriented Predictions 4.9 Predictive Robotics 4.10 Perception-Action-Understanding Machines Chapter 5: Sculpting the Flow 5.1 Double Agents 5.2 Towards Maximal Context-Sensitivity 5.3 Hierarchy Reconsidered 5.4 Sculpting Effective Connectivity 5.5 Soft Modularity 5.6 Understanding Action 5.7 Making Mirrors 5.8 Whodunit? 5.9 Robot Futures 5.10 The Restless, Rapidly Responsive, Brain 5.11 Precision Engineering Chapter 6: Engaging the world 6.1 Expecting the World 6.2 Controlled Hallucinations and Virtual Realities. 6.3 The Surprising Scope of Structured Probabilistic Learning 6.4 Sensing-Thinking-Acting 6. 5 Implementing Affordance Competition 6.6 Ready for Action 6.7 Hello World 6.8 'Not-Indirect' Perception 6.9 Hallucination as Uncontrolled Perception 6.10 Putting Illusions in Their Place 6.11 Safer Penetration 6.12 Who Estimates the Estimators? 6.13 Beyond Fantasy Chapter 7: Expecting Ourselves 7.1 The Space of Human Experience 7.2 Warning Lights 7.3 The Spiral of Inference and Experience 7.4 Schizophrenia and Smooth Pursuit Eye Movements 7.5 Simulating Smooth Pursuit 7.6 Disturbing the Network (Smooth Pursuit) 7.7 Tickling Redux 7.8 Less Sense, More Action 7.9 Disturbing the Network (Sensory Attenuation) 7.10 'Psychogenic Disorders' and Placebo Effects 7.11 Disturbing the Network ('Psychogenic' Effects) 7.12 Autism, Noise, and Signal 7.13 Conscious Presence 7.14 Emotion 7.15 Fear in the Night 7.16 A Nip of the Hard Stuff Part III: Scaffolding Prediction Chapter 8: The Lazy Predictive Brain 8.1 Surface Tensions 8.2 Productive Laziness 8.3 Ecological Balance, and Baseball 8.4 Embodied Flow 8.5 Befriending the Bayesian Brain 8.6 Beyond the Model-Based/Model-Free Divide 8.7 Balancing Accuracy and Complexity 8.8.Back to Baseball 8.9 Extended Predictive Minds 8.10 Escape from the Darkened Room 8.11 Self-Organized Instability 8.12 Fast, Cheap, but Model-Rich Too Chapter 9: Being Human 9.1 Putting Prediction in its Place 9.2 Reprise: Self-Organizing Around Prediction Error 9.3 Efficiency and " The Lord's Prior " 9.4 Chaos and Spontaneous Cortical Activity 9.5 Designer Environments 9.6 White Lines 9.7 Innovating for Innovation 9.8 Words as Artificial Contexts 9.9 Predicting With Others 9.10 Enacting Our Worlds 9.11 Representations: Breaking Good 9.12 Prediction in the Wild Chapter 10: The Future of Prediction 10.1 Attractions 10.2 Problems, Puzzles, and Pitfalls Appendix 1: Bare Bayes Appendix 2: The Free Energy Formulation References Index
Table of Contents Preface: Meat That Predicts Acknowledgements Introduction: Guessing Games Part I: The Power of Prediction Chapter 1: Prediction Machines 1.1 Two Ways to Sense the Coffee 1.2 Adopting the Animal's Perspective 1.3 Learning in Bootstrap Heaven 1.4 Multi-Level Learning 1.5 Decoding Digits 1.6 Dealing With Structure 1.7 Predictive Processing 1.8 Signaling the News 1.9 Predicting Natural Scenes 1.10 Binocular Rivalry 1.11 Dampening and Sharpening 1.12 Encoding, inference, and the Bayesian Brain 1.13 Getting the Gist 1.14 Predictive Processing in the Brain 1.15 Is Silence Golden? 1.16 Expecting Faces 1.17 When Prediction Misleads 1.18 Mind Turned Upside Down Chapter Two: Adjusting The Volume (Noise, Signal, Attention) 2.1 Signal Spotting 2.2 Hearing Bing 2.3 The Delicate Dance between Top-down and Bottom-up. 2.4 Attention, Biased Competition, and Signal Enhancement 2.5 Sensory Integration and Coupling 2.6 A Taste of Action 2.7 Gaze Allocation: Doing What Comes Naturally 2.8 Circular Causation in the Perception-Attention-Action Loop 2.9 Mutual Assured Misunderstanding 2.10 Some Worries About Precision 2.11 The Unexpected Elephant 2.12 Some Pathologies of Precision 2.13 Beyond the Spotlight Chapter 3: The Imaginarium 3.1 The Many Benefits of Controlled Hallucination 3.2 Simple Seeing 3.3 Cross-Modal and Multi-Modal effects 3.4 Meta-Modal Effects 3.5 Perceiving Omissions 3.6 Expectations and Conscious Perception 3.7 The Perceiver as Imaginer 3.8 'Brain Reading' During Imagery and Perception 3.9 Inside the Dream Factory 3.10 PIMMS and the Past 3.11 Towards Mental Time Travel 3.12 A Cognitive Package Deal Part II: Embodying Prediction Chapter 4: Prediction for Action 4.1 Staying Ahead of the Break 4.2 Ticklish Tales 4.3 Forward Models 4.4 Optimal Feedback Control 4.5 Action-oriented Predictive Processing 4.6 Simplifying Control 4.7 Beyond Efference Copy 4.7 Doing Without Cost Functions 4.8 Action-oriented Predictions 4.9 Predictive Robotics 4.10 Perception-Action-Understanding Machines Chapter 5: Sculpting the Flow 5.1 Double Agents 5.2 Towards Maximal Context-Sensitivity 5.3 Hierarchy Reconsidered 5.4 Sculpting Effective Connectivity 5.5 Soft Modularity 5.6 Understanding Action 5.7 Making Mirrors 5.8 Whodunit? 5.9 Robot Futures 5.10 The Restless, Rapidly Responsive, Brain 5.11 Precision Engineering Chapter 6: Engaging the world 6.1 Expecting the World 6.2 Controlled Hallucinations and Virtual Realities. 6.3 The Surprising Scope of Structured Probabilistic Learning 6.4 Sensing-Thinking-Acting 6. 5 Implementing Affordance Competition 6.6 Ready for Action 6.7 Hello World 6.8 'Not-Indirect' Perception 6.9 Hallucination as Uncontrolled Perception 6.10 Putting Illusions in Their Place 6.11 Safer Penetration 6.12 Who Estimates the Estimators? 6.13 Beyond Fantasy Chapter 7: Expecting Ourselves 7.1 The Space of Human Experience 7.2 Warning Lights 7.3 The Spiral of Inference and Experience 7.4 Schizophrenia and Smooth Pursuit Eye Movements 7.5 Simulating Smooth Pursuit 7.6 Disturbing the Network (Smooth Pursuit) 7.7 Tickling Redux 7.8 Less Sense, More Action 7.9 Disturbing the Network (Sensory Attenuation) 7.10 'Psychogenic Disorders' and Placebo Effects 7.11 Disturbing the Network ('Psychogenic' Effects) 7.12 Autism, Noise, and Signal 7.13 Conscious Presence 7.14 Emotion 7.15 Fear in the Night 7.16 A Nip of the Hard Stuff Part III: Scaffolding Prediction Chapter 8: The Lazy Predictive Brain 8.1 Surface Tensions 8.2 Productive Laziness 8.3 Ecological Balance, and Baseball 8.4 Embodied Flow 8.5 Befriending the Bayesian Brain 8.6 Beyond the Model-Based/Model-Free Divide 8.7 Balancing Accuracy and Complexity 8.8.Back to Baseball 8.9 Extended Predictive Minds 8.10 Escape from the Darkened Room 8.11 Self-Organized Instability 8.12 Fast, Cheap, but Model-Rich Too Chapter 9: Being Human 9.1 Putting Prediction in its Place 9.2 Reprise: Self-Organizing Around Prediction Error 9.3 Efficiency and " The Lord's Prior " 9.4 Chaos and Spontaneous Cortical Activity 9.5 Designer Environments 9.6 White Lines 9.7 Innovating for Innovation 9.8 Words as Artificial Contexts 9.9 Predicting With Others 9.10 Enacting Our Worlds 9.11 Representations: Breaking Good 9.12 Prediction in the Wild Chapter 10: The Future of Prediction 10.1 Attractions 10.2 Problems, Puzzles, and Pitfalls Appendix 1: Bare Bayes Appendix 2: The Free Energy Formulation References Index
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