LATER: the neurophysiology of decision-making
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
2023
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| Schriftenreihe: | Cambridge medicine
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Auf der Rückseite: neural model LATER (linear approach to theshold ergodic rate) |
| Beschreibung: | x, 179 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108827041 |
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Datensatz im Suchindex
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| adam_text | Contents Preface ix Acknowledgements 1 x 1 1.1 Saccades 2 1.1.1 The Step Task 4 1.2 Procrastination 5 1.3 Analysing the Variability of Reaction Time 8 1.3.1 Kinds of Histograms 9 1.4 The Recinormal Distribution 13 1.4.1 Reciprobit Plots 14 1.4.2 A Gallery of Reciprobits 15 1.4.3 But Are Saccades the Result of a Decision? 19 1.4.4 Smooth Pursuit 20 3.6 The Benefits of Procrastination The Slowness of Reaction Time 2 LATER as a Model of Latency 22 2.1 Linear Rise-to-Threshold 22 2.2 Early Responses 25 2.2.1 Multiple Early Units 28 2.2.2 Express Responses 28 2.3 Manual Responses 30 3 32 3.1 An Ideal Decision-Maker 33 3.2 What Is Probability? 33 3.2.1 Frequency 34 3.2.2 Equipossibility 34 3.2.3 Propensity 35 3.2.4 Logical Probability 35 3.2.5 Subjective 36 3.2.6 Terminology 37 3.2.7 Probability and Information 37 3.3 Bayes’ Law 39 3.3.1 The Dominance of Priors 40 3.4 LATER as a Bayesian Decision Device 41 3.5 Behavioural Tests of LATER 42 3.5.1 Expectation 42 3.5.2 Urgency 44 3.5.3 Information Supply 45 LATER as a Model of Decision 4 46 Complex Decisions: Multiple LATER Units 49 4.1 Altering Prior Probability 49 4.2 Cuing Tasks 50 4.2.1 Foreperiod as Cue 51 4.2.2 Sequences 51 4.2.3 Task-Switching 51 4.3 Races and Choices 52 4.4 Lateral Inhibition 55 4.5 Asynchronous Tasks 56 4.5.1 Precedence 56 4.5.2 Gap and Overlap 58 4.6 Global Evaluation of Extended Stimuli 58 4.6.1 Judgements Based on the Scene as a Whole 58 4.6.2 Two Stages of Judgement: Detection and Decision 59 4.7 Stimulus Factors 64 4.7.1 Duration 64 4.7.2 Contrast 65 4.7.3 Why a Linear Rise? 65 4.8 Scanning and
Searching 66 4.8.1 Reading Text 66 4.8.2 Reading Music 68 4.8.3 Optokinetic Nystagmus 69 4.8.4 Relation between Evoked and Spontaneous Saccades 69 4.8.5 The Origin of the Rightward Shift 70 4.8.6 The Increase in Early Responses 71 4.8.7 The LATEST Model 72 v
vi Contents 4.9 Stop Signals and Cancellation 74 4.9.1 Countermanding 74 4.9.2 Wheeless 75 4.9.3 Go I NoGo and Errors 76 4.9.4 Antisaccades 77 5 6 80 5.1 The Cerebral Hierarchy 81 5.1.1 Superior Colliculus 83 5.1.2 Basal Ganglia 84 5.1.3 Habenula 85 5.1.4 Frontal Eye Fields 86 5.1.5 Supplementary Eye Field 87 5.1.6 Lateral Intraparietal Area 88 5.1.7 Cingulate Cortex 88 5.2 The Need for Ascending Control 89 5.3 The Source of Randomness 91 5.3.1 Implementing Randomness 92 5.3.2 Cellular 92 5.3.3 Externally Generated Randomness 93 5.3.4 Emergent Chaotic Behaviour 93 5.4 Attention 93 6.6 Probability. The Language of the Brain 111 6.6.1 Interpreting Neural Networks 112 LATER and the Brain Larger Implications 95 6.1 What Is a Stimulus? 95 6.2 Multidimensional Inference 96 6.2.1 Complex Likelihood Ratios 97 6.2.2 Probability Vectors 99 6.3 What Is Randomness? 100 6.3.1 Benefits of Randomness 100 6.3.2 Game Theory 101 6.3.3 Imagination and Creativity 102 6.4 Reward and Utility 103 6.4.1 Information as Intrinsic Reward 104 6.5 Free Will and Consciousness 105 Appendix 1: Mathematical 115 App 1.1 Notation 115 App 1.1.1 General 115 App 1.1.2 Specific Terminology for Inference 115 App 1.2 Properties of the Recinormal Distribution 117 App 1.3 Models for Latency Distributions 119 App 1.3.1 Counting Models 119 App 1.3.2 La Berge Distribution 120 App 1.3.3 Van den Berg model 120 App 1.3.4 Difference Counting 122 App 1.4 Pragmatic Functions 122 App 1.4.1 Normex 122 App 1.4.2 Weibull 123 App 1.5 Other Theoretical Distributions 123 App 1.5.1 Poisson Distribution 123 App 1.5.2 Audley
Distribution 124 App 1.5.3 Kintsch Distribution 124 App 1.5.4 Random Walk 125 App 1.5.5 Mieko 126 App 1.5.6 Flat 127 App 1.5.7 Gaussian 127 App 1.5.8 Logistic 127
Contents App 1.6 Combinations of Elements 129 App 1.6.1 Fixed Delay in Series 129 App 1.6.2 Two Recinormal Processes in Series 130 App 1.6.3 Prior Dichotomy 130 App 1.7 Scales for Encoding Probability 131 App 1.7.1 Non-linear Scales 132 App 1.7.2 Encoding Firmness of Belief 133 App 1.7.3 Simple Log Scale 135 App 1.7.4 Odds 136 App 1.7.5 Log Odds 136 App 1.7.6 Information and Probability 136 App 1.7.7 Surprise 137 App 1.7.8 Single Hypotheses 137 App 1.8 Modelling Choice 137 App 1.8.1 Pre-emption 137 App 1.8.2 Races between Cooperative Pairs of LATER Units 138. App 1.8.3 Races between Antagonistic Pairs of LATER Units 140 App 1.8.4 Bayesian Races 141 App 1.8.5 Races between Many LATER Units 141 App 1.9 Learning 142 App 1.9.1 Hebbian Synapses as Bayesian Computers 142 App 1.10 Information and Probability 144 App 1.10.1 Uncertainty as Lack of Information 144 App 1.10.2 Information in Extended Displays 144 Appendix 2 Clinical 146 App 2.1 Degenerative Conditions 146 vii App 2.1.1 Parkinson’s Disease 146 App 2.1.2 Deep Brain Stimulation 146 App 2.1.3 Huntington’s Disease 147 App 2.1.4 Progressive Supranuclear Palsy 147 App 2.1.5 Amyotrophic Lateral Sclerosis 147 App 2.1.6 Dementia 147 App 2.2 General Neurology 147 App 2.2.1 Anaesthetics 147 App 2.2.2 Endarterectomy 148 App 2.2.3 Migraine 148 App 2.2.4 Traumatic Brain Injury 149 App 2.2.5 Hepatic Encephalopathy 149 App 2.3 Miscellaneous 150 App 2.3.1 Psychiatric Disorders 150 App 2.3.2 Metabolic 150 App 2.3.3 Storage Diseases 150 App 2.3.4 Ageing 150 Appendix 3 Practical 151 App 3.1 Measuring Saccadic Latency Distributions 151 App
3.2 Recording 151 App 3.2.1 The Saccadometer 151 App 3.2.2 The Oculometer 152 App 3.2.3 Using the Saccadometer for Manual Responses 152 App 3.3 Generating Visual Stimuli 153 App 3.3.1 Saccadometer 153 App 3.3.2 Oculometer 153 App 3.3.3 ViSaGe and SPIC 154
viii Contents App 3.4 Protocol Design 154 App 3.4.1 Interleaving 154 App 3.4.2 Speeding Things Up 154 App 3.4.3 Alertness 154 App 3.4.4 How Many Trials? 155 App 3.4.5 Standardisation 155 App 3.5 Analysing Distributions 155 App 3.5.1 How to Create a Reciprobit Plot 156 Bibliography 158 Index 176
Why is deciding to do something sometimes so slow and difficult? How do we make decisions when lacking key information? When making decisions, the higher areas of the brain deliberately suppress lower areas capable of generating much faster but ill-considered responses while they develop ones that are more sophisticated, based on what can be gained in return. In this engaging book, the authors explore the increasingly popular neural model that may explain these mechanisms: the linear approach to threshold ergodic rate (LATER). Presenting a detailed description of the neurophysiological processes involved in decision-making and how these link to the LATER model, this is the first major resource covering the applications in describing human behaviour. With over 100 illustrations and a thorough discussion of the mathematics supporting the model, this is a rigorous yet accessible resource for psychologists, cognitive neuroscientists and neurophysiologists interested in decision-making.
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| adam_txt |
Contents Preface ix Acknowledgements 1 x 1 1.1 Saccades 2 1.1.1 The Step Task 4 1.2 Procrastination 5 1.3 Analysing the Variability of Reaction Time 8 1.3.1 Kinds of Histograms 9 1.4 The Recinormal Distribution 13 1.4.1 Reciprobit Plots 14 1.4.2 A Gallery of Reciprobits 15 1.4.3 But Are Saccades the Result of a Decision? 19 1.4.4 Smooth Pursuit 20 3.6 The Benefits of Procrastination The Slowness of Reaction Time 2 LATER as a Model of Latency 22 2.1 Linear Rise-to-Threshold 22 2.2 Early Responses 25 2.2.1 Multiple Early Units 28 2.2.2 Express Responses 28 2.3 Manual Responses 30 3 32 3.1 An Ideal Decision-Maker 33 3.2 What Is Probability? 33 3.2.1 Frequency 34 3.2.2 Equipossibility 34 3.2.3 Propensity 35 3.2.4 Logical Probability 35 3.2.5 Subjective 36 3.2.6 Terminology 37 3.2.7 Probability and Information 37 3.3 Bayes’ Law 39 3.3.1 The Dominance of Priors 40 3.4 LATER as a Bayesian Decision Device 41 3.5 Behavioural Tests of LATER 42 3.5.1 Expectation 42 3.5.2 Urgency 44 3.5.3 Information Supply 45 LATER as a Model of Decision 4 46 Complex Decisions: Multiple LATER Units 49 4.1 Altering Prior Probability 49 4.2 Cuing Tasks 50 4.2.1 Foreperiod as Cue 51 4.2.2 Sequences 51 4.2.3 Task-Switching 51 4.3 Races and Choices 52 4.4 Lateral Inhibition 55 4.5 Asynchronous Tasks 56 4.5.1 Precedence 56 4.5.2 Gap and Overlap 58 4.6 Global Evaluation of Extended Stimuli 58 4.6.1 Judgements Based on the Scene as a Whole 58 4.6.2 Two Stages of Judgement: Detection and Decision 59 4.7 Stimulus Factors 64 4.7.1 Duration 64 4.7.2 Contrast 65 4.7.3 Why a Linear Rise? 65 4.8 Scanning and
Searching 66 4.8.1 Reading Text 66 4.8.2 Reading Music 68 4.8.3 Optokinetic Nystagmus 69 4.8.4 Relation between Evoked and Spontaneous Saccades 69 4.8.5 The Origin of the Rightward Shift 70 4.8.6 The Increase in Early Responses 71 4.8.7 The LATEST Model 72 v
vi Contents 4.9 Stop Signals and Cancellation 74 4.9.1 Countermanding 74 4.9.2 Wheeless 75 4.9.3 Go I NoGo and Errors 76 4.9.4 Antisaccades 77 5 6 80 5.1 The Cerebral Hierarchy 81 5.1.1 Superior Colliculus 83 5.1.2 Basal Ganglia 84 5.1.3 Habenula 85 5.1.4 Frontal Eye Fields 86 5.1.5 Supplementary Eye Field 87 5.1.6 Lateral Intraparietal Area 88 5.1.7 Cingulate Cortex 88 5.2 The Need for Ascending Control 89 5.3 The Source of Randomness 91 5.3.1 Implementing Randomness 92 5.3.2 Cellular 92 5.3.3 Externally Generated Randomness 93 5.3.4 Emergent Chaotic Behaviour 93 5.4 Attention 93 6.6 Probability. The Language of the Brain 111 6.6.1 Interpreting Neural Networks 112 LATER and the Brain Larger Implications 95 6.1 What Is a Stimulus? 95 6.2 Multidimensional Inference 96 6.2.1 Complex Likelihood Ratios 97 6.2.2 Probability Vectors 99 6.3 What Is Randomness? 100 6.3.1 Benefits of Randomness 100 6.3.2 Game Theory 101 6.3.3 Imagination and Creativity 102 6.4 Reward and Utility 103 6.4.1 Information as Intrinsic Reward 104 6.5 Free Will and Consciousness 105 Appendix 1: Mathematical 115 App 1.1 Notation 115 App 1.1.1 General 115 App 1.1.2 Specific Terminology for Inference 115 App 1.2 Properties of the Recinormal Distribution 117 App 1.3 Models for Latency Distributions 119 App 1.3.1 Counting Models 119 App 1.3.2 La Berge Distribution 120 App 1.3.3 Van den Berg model 120 App 1.3.4 Difference Counting 122 App 1.4 Pragmatic Functions 122 App 1.4.1 Normex 122 App 1.4.2 Weibull 123 App 1.5 Other Theoretical Distributions 123 App 1.5.1 Poisson Distribution 123 App 1.5.2 Audley
Distribution 124 App 1.5.3 Kintsch Distribution 124 App 1.5.4 Random Walk 125 App 1.5.5 Mieko 126 App 1.5.6 Flat 127 App 1.5.7 Gaussian 127 App 1.5.8 Logistic 127
Contents App 1.6 Combinations of Elements 129 App 1.6.1 Fixed Delay in Series 129 App 1.6.2 Two Recinormal Processes in Series 130 App 1.6.3 Prior Dichotomy 130 App 1.7 Scales for Encoding Probability 131 App 1.7.1 Non-linear Scales 132 App 1.7.2 Encoding Firmness of Belief 133 App 1.7.3 Simple Log Scale 135 App 1.7.4 Odds 136 App 1.7.5 Log Odds 136 App 1.7.6 Information and Probability 136 App 1.7.7 Surprise 137 App 1.7.8 Single Hypotheses 137 App 1.8 Modelling Choice 137 App 1.8.1 Pre-emption 137 App 1.8.2 Races between Cooperative Pairs of LATER Units 138. App 1.8.3 Races between Antagonistic Pairs of LATER Units 140 App 1.8.4 Bayesian Races 141 App 1.8.5 Races between Many LATER Units 141 App 1.9 Learning 142 App 1.9.1 Hebbian Synapses as Bayesian Computers 142 App 1.10 Information and Probability 144 App 1.10.1 Uncertainty as Lack of Information 144 App 1.10.2 Information in Extended Displays 144 Appendix 2 Clinical 146 App 2.1 Degenerative Conditions 146 vii App 2.1.1 Parkinson’s Disease 146 App 2.1.2 Deep Brain Stimulation 146 App 2.1.3 Huntington’s Disease 147 App 2.1.4 Progressive Supranuclear Palsy 147 App 2.1.5 Amyotrophic Lateral Sclerosis 147 App 2.1.6 Dementia 147 App 2.2 General Neurology 147 App 2.2.1 Anaesthetics 147 App 2.2.2 Endarterectomy 148 App 2.2.3 Migraine 148 App 2.2.4 Traumatic Brain Injury 149 App 2.2.5 Hepatic Encephalopathy 149 App 2.3 Miscellaneous 150 App 2.3.1 Psychiatric Disorders 150 App 2.3.2 Metabolic 150 App 2.3.3 Storage Diseases 150 App 2.3.4 Ageing 150 Appendix 3 Practical 151 App 3.1 Measuring Saccadic Latency Distributions 151 App
3.2 Recording 151 App 3.2.1 The Saccadometer 151 App 3.2.2 The Oculometer 152 App 3.2.3 Using the Saccadometer for Manual Responses 152 App 3.3 Generating Visual Stimuli 153 App 3.3.1 Saccadometer 153 App 3.3.2 Oculometer 153 App 3.3.3 ViSaGe and SPIC 154
viii Contents App 3.4 Protocol Design 154 App 3.4.1 Interleaving 154 App 3.4.2 Speeding Things Up 154 App 3.4.3 Alertness 154 App 3.4.4 How Many Trials? 155 App 3.4.5 Standardisation 155 App 3.5 Analysing Distributions 155 App 3.5.1 How to Create a Reciprobit Plot 156 Bibliography 158 Index 176
Why is deciding to do something sometimes so slow and difficult? How do we make decisions when lacking key information? When making decisions, the higher areas of the brain deliberately suppress lower areas capable of generating much faster but ill-considered responses while they develop ones that are more sophisticated, based on what can be gained in return. In this engaging book, the authors explore the increasingly popular neural model that may explain these mechanisms: the linear approach to threshold ergodic rate (LATER). Presenting a detailed description of the neurophysiological processes involved in decision-making and how these link to the LATER model, this is the first major resource covering the applications in describing human behaviour. With over 100 illustrations and a thorough discussion of the mathematics supporting the model, this is a rigorous yet accessible resource for psychologists, cognitive neuroscientists and neurophysiologists interested in decision-making. |
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| index_date | 2024-07-03T23:04:04Z |
| indexdate | 2024-07-10T10:06:05Z |
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| series2 | Cambridge medicine |
| spelling | Carpenter, Roger H. S. 1945-2017 Verfasser (DE-588)122789393 aut LATER the neurophysiology of decision-making Roger H. S. Carpenter, Imran Noorani Cambridge, United Kingdom Cambridge University Press 2023 x, 179 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Cambridge medicine Auf der Rückseite: neural model LATER (linear approach to theshold ergodic rate) Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Neurophysiologie (DE-588)4041897-2 gnd rswk-swf Entscheidungsfindung (DE-588)4113446-1 s Neurophysiologie (DE-588)4041897-2 s DE-604 Noorani, Imran Verfasser (DE-588)1252413203 aut Erscheint auch als Online-Ausgabe 978-1-108-92080-3 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034729042&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034729042&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Carpenter, Roger H. S. 1945-2017 Noorani, Imran LATER the neurophysiology of decision-making Entscheidungsfindung (DE-588)4113446-1 gnd Neurophysiologie (DE-588)4041897-2 gnd |
| subject_GND | (DE-588)4113446-1 (DE-588)4041897-2 |
| title | LATER the neurophysiology of decision-making |
| title_auth | LATER the neurophysiology of decision-making |
| title_exact_search | LATER the neurophysiology of decision-making |
| title_exact_search_txtP | LATER the neurophysiology of decision-making |
| title_full | LATER the neurophysiology of decision-making Roger H. S. Carpenter, Imran Noorani |
| title_fullStr | LATER the neurophysiology of decision-making Roger H. S. Carpenter, Imran Noorani |
| title_full_unstemmed | LATER the neurophysiology of decision-making Roger H. S. Carpenter, Imran Noorani |
| title_short | LATER |
| title_sort | later the neurophysiology of decision making |
| title_sub | the neurophysiology of decision-making |
| topic | Entscheidungsfindung (DE-588)4113446-1 gnd Neurophysiologie (DE-588)4041897-2 gnd |
| topic_facet | Entscheidungsfindung Neurophysiologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034729042&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034729042&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT carpenterrogerhs latertheneurophysiologyofdecisionmaking AT nooraniimran latertheneurophysiologyofdecisionmaking |