Computational modeling in cognition: principles and practice
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles [u.a.]
Sage
2011
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | X, 359 S. Ill., graph. Darst. |
| ISBN: | 9781412970761 |
Staff View
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| 264 | 1 | |a Los Angeles [u.a.] |b Sage |c 2011 | |
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Record in the Search Index
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|---|---|
| adam_text | IMAGE 1
CONTENTS
PREFACE IX
1. INTRODUCTION 1
1.1 MODELS AND THEORIES IN SCIENCE 1
1.2 WHY QUANTITATIVE MODELING? 3
1.3 QUANTITATIVE MODELING IN COGNITION 5
1.3.1 MODELS AND DATA 5
1.3.2 FROM IDEAS TO MODELS 8
1.3.3 SUMMARY 10
1.4 THE IDEAS UNDERLYING MODELING AND ITS DISTINCT APPLICATIONS 10
1.4.1 ELEMENTS OF MODELS 10
1.4.2 DATA DESCRIPTION 11
1.4.3 PROCESS CHARACTERIZATION 16
1.4.4 PROCESS EXPLANATION 19
1.4.5 CLASSES OF MODELS 25
1.5 WHAT CAN WE EXPECT FROM MODELS? 25
1.5.1 CLASSIFICATION OF PHENOMENA 25
1.5.2 EMERGENCE OF UNDERSTANDING 26
1.5.3 EXPLORATION OF IMPLICATIONS 27
1.6 POTENTIAL PROBLEMS 28
1.6.1 SCOPE AND TESTABILITY 28
1.6.2 IDENTIFICATION AND TRUTH 32
2. FROM WORDS TO MODELS: BUILDING A TOOLKIT 35
2.1 WORKING MEMORY 35
2.1.1 THE PHONOLOGICAL LOOP 36
2.1.2 THEORETICAL ACCOUNTS OF THE WORD LENGTH EFFECT 37 2.2 THE
PHONOLOGICAL LOOP: 144 MODELS OF WORKING MEMORY 39 2.2.1 DECAY PROCESS
40
2.2.2 RECALL PROCESS 41
2.2.3 REHEARSAL 41
IMAGE 2
2.3 BUILDING A SIMULATION 42
2.3.1 MATLAB 42
2.3.2 THE TARGET METHODOLOGY 43
2.3.3 SETTING UP THE SIMULATION 44
2.3.4 SOME UNANTICIPATED RESULTS 49
2.3.5 VARIABILITY IN DECAY 51
2.4 WHAT CAN WE LEARN FROM THESE SIMULATIONS? 53
2.4.1 THE PHONOLOGICAL LOOP MODEL INSTANTIATED 53
2.4.2 TESTABILITY AND FALSIFIABILITY 54
2.4.3 REVISITING MODEL DESIGN: FOUNDATIONAL DECISIONS 55 2.5 THE BASIC
TOOLKIT 56
2.5.1 PARAMETERS 57
2.5.2 DISCREPANCY FUNCTION 61
2.5.3 PARAMETER ESTIMATION AND MODEL-FITTING TECHNIQUES 63 2.6 MODELS
AND DATA: SUFFICIENCY AND EXPLANATION 64
2.6.1 SUFFICIENCY OF A MODEL 65
2.6.2 VERISIMILITUDE VERSUS TRUTH 67
2.6.3 THE NATURE OF EXPLANATIONS 68
3. BASIC PARAMETER ESTIMATION TECHNIQUES 71
3.1 FITTING MODELS TO DATA: PARAMETER ESTIMATION 71
3.1.1 VISUALIZING MODELING 72
3.1.2 AN EXAMPLE 74
3.1.3 INSIDE THE BOX: PARAMETER ESTIMATION TECHNIQUES 82 3.2 CONSIDERING
THE DATA: WHAT LEVEL OF ANALYSIS? 96
3.2.1 IMPLICATIONS OF AVERAGING 97
3.2.2 FITTING INDIVIDUAL PARTICIPANTS 100
3.2.3 FITTING SUBGROUPS OF DATA 103
3.2.4 FITTING AGGREGATE DATA 103
3.2.5 HAVING YOUR CAKE AND EATING IT: MULTILEVEL MODELING 104 3.2.6
RECOMMENDATIONS 105
4. MAXIMUM LIKELIHOOD ESTIMATION 109
4.1 BASICS OF PROBABILITIES 110
4.1.1 DENNING PROBABILITY 110
4.1.2 PROPERTIES OF PROBABILITIES 111
4.1.3 PROBABILITY FUNCTIONS 112
4.2 WHAT IS A LIKELIHOOD? 117
4.2.1 INVERSE PROBABILITY AND LIKELIHOOD 124
4.3 DEFINING A PROBABILITY FUNCTION 125
4.3.1 PROBABILITY FUNCTIONS SPECIFIED BY THE PSYCHOLOGICAL MODEL 126
4.3.2 PROBABILITY FUNCTIONS VIA DATA MODELS 127
IMAGE 3
4.3.3 TWO TYPES OF PROBABILITY FUNCTIONS 132
4.3.4 EXTENDING THE DATA MODEL 133
4.3.5 EXTENSION TO MULTIPLE DATA POINTS AND MULTIPLE PARAMETERS 136
4.4 FINDING THE MAXIMUM LIKELIHOOD 138
4.5 MAXIMUM LIKELIHOOD ESTIMATION FOR MULTIPLE PARTICIPANTS 142 4.5.1
ESTIMATION FOR INDIVIDUAL PARTICIPANTS 143
4.5.2 ESTIMATING A SINGLE SET OF PARAMETERS 143
4.6 PROPERTIES OF MAXIMUM LIKELIHOOD ESTIMATORS 145
5. PARAMETER UNCERTAINTY AND MODEL COMPARISON 149
5.1 ERROR ON MAXIMUM LIKELIHOOD ESTIMATES 150
5.1.1 STANDARD ERRORS ACROSS PARTICIPANTS 150
5.1.2 CURVATURE OF THE LIKELIHOOD SURFACE 153
5.1.3 BOOTSTRAPPING 163
5.1.4 WHAT DO CONFIDENCE LIMITS TELL US? 170
5.2 INTRODUCTION TO MODEL SELECTION 170
5.3 THE LIKELIHOOD RATIO TEST 177
5.4 INFORMATION CRITERIA AND MODEL COMPARISON 179
5.4.1 KULLBACK-LEIBLER DISTANCE AND AKAIKE S INFORMATION CRITERION 179
5.4.2 BAYESIAN INFORMATION CRITERION 183
5.4.3 MODEL COMPARISON WITH THE AIC AND BIC 184
5.4.4 AN EXAMPLE OF MODEL COMPARISON USING AIC AND BIC 189
5.4.5 CHOOSING BETWEEN AIC AND BIC 191
5.5 CONCLUSION 192
6. NOT EVERYTHING THAT FITS IS GOLD: INTERPRETING THE MODELING 197
6.1 PSYCHOLOGICAL DATA AND THE VERY BAD GOOD FIT 198
6.1.1 OVERFILLING 198
6.1.2 GENERALIZABILITY VERSUS GOODNESS OF FIT 200
6.2 PARAMETER IDENTIFIABILITY AND MODEL TESTABILITY 204
6.2.1 IDENTIFIABILITY 205
6.2.2 TESTABILITY 211
6.2.3 PUTTING IT ALL TOGETHER: DECIDING THE IDENTIFIABILITY AND
TESTABILITY OF A MODEL 212
6.2.4 MODELS THAT ARE IDENTIFIABLE BUT NOT TESTABLE 213
6.3 DRAWING LESSONS AND CONCLUSIONS FROM MODELING 219
6.3.1 EXPLORATIONS OF A MODEL: EFFECTS OF PARAMETERS 220 6.3.2
DEMONSTRATIONS OF SUFFICIENCY 222
6.3.3 DEMONSTRATIONS OF NECESSITY 226
6.3.4 SUMMARY 232
IMAGE 4
7. DRAWING IT ALL TOGETHER: TWO EXAMPLES 235
7.1 WITNESS: SIMULATING EYEWITNESS IDENTIFICATION 235
7.1.1 WITNESS: ARCHITECTURE 236
7.1.2 WITNESS AND VERBAL OVERSHADOWING 238
7.1.3 WITNESS IN MATLAB 240
7.1.4 WITNESS SIMULATION RESULTS 252
7.2 EXEMPLAR VERSUS BOUNDARY MODELS: CHOOSING BETWEEN CANDIDATES 257
7.2.1 MODELS OF CATEGORIZATION 258
7.2.2 A PROBABILISTIC FEEDBACK EXPERIMENT 263
7.2.3 MATLAB CODE FOR ML PARAMETER ESTIMATION FOR GCM, DEM, AND GRT 265
7.2.4 FITTING THE MODELS TO DATA 269
7.2.5 WHAT HAVE WE LEARNED ABOUT CATEGORIZATION? 279 7.3 CONCLUSION 281
8. MODELING IN A BROADER CONTEXT 283
8.1 BAYESIAN THEORIES OF COGNITION 284
8.2 NEURAL NETWORKS 288
8.2.1 BASIC ARCHITECTURE AND OPERATION 288
8.2.2 HEBBIAN MODELS 290
8.2.3 BACKPROPAGATION MODELS 293
8.2.4 SUMMARY 297
8.3 NEUROSCIENTIFIC MODELING 298
8.3.1 THE ALLURE OF NEUROSCIENCE 299
8.3.2 THE TRUE PROMISE OF NEUROSCIENCE 303
8.4 COGNITIVE ARCHITECTURES 306
8.4.1 COGNITIVE ARCHITECTURES: CONVERGENCE TO A STANDARD 307 8.4.2
COGNITIVE ARCHITECTURES: SUCCESSES, PROBLEMS, AND SOLUTIONS 308
8.4.3 MARRYING ARCHITECTURES AND NEUROSCIENCE 310
8.4.4 ARCHITECTURES AND MODELS 311
8.5 CONCLUSION 313
8.5.1 MEMORY 314
8.5.2 LANGUAGE 314
8.5.3 PERCEPTION AND ACTION 315
8.5.4 CHOICE AND DECISION MAKING 315
8.5.5 IDENTIFICATION AND CATEGORIZATION 316
REFERENCES 319
AUTHOR INDEX 345
SUBJECT INDEX 353
ABOUT THE AUTHORS 359
|
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| language | English |
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| spelling | Lewandowsky, Stephan Verfasser aut Computational modeling in cognition principles and practice Stephan Lewandowsky ; Simon Farrell Los Angeles [u.a.] Sage 2011 X, 359 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Modellierung (DE-588)4170297-9 gnd rswk-swf Kognitiver Prozess (DE-588)4140177-3 gnd rswk-swf Kognitive Psychologie (DE-588)4073586-2 gnd rswk-swf Kognition (DE-588)4031630-0 gnd rswk-swf Kognition (DE-588)4031630-0 s Kognitiver Prozess (DE-588)4140177-3 s Kognitive Psychologie (DE-588)4073586-2 s Modellierung (DE-588)4170297-9 s DE-604 Farrell, Simon 1976- Verfasser (DE-588)1012639738 aut SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022558004&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lewandowsky, Stephan Farrell, Simon 1976- Computational modeling in cognition principles and practice Modellierung (DE-588)4170297-9 gnd Kognitiver Prozess (DE-588)4140177-3 gnd Kognitive Psychologie (DE-588)4073586-2 gnd Kognition (DE-588)4031630-0 gnd |
| subject_GND | (DE-588)4170297-9 (DE-588)4140177-3 (DE-588)4073586-2 (DE-588)4031630-0 |
| title | Computational modeling in cognition principles and practice |
| title_auth | Computational modeling in cognition principles and practice |
| title_exact_search | Computational modeling in cognition principles and practice |
| title_full | Computational modeling in cognition principles and practice Stephan Lewandowsky ; Simon Farrell |
| title_fullStr | Computational modeling in cognition principles and practice Stephan Lewandowsky ; Simon Farrell |
| title_full_unstemmed | Computational modeling in cognition principles and practice Stephan Lewandowsky ; Simon Farrell |
| title_short | Computational modeling in cognition |
| title_sort | computational modeling in cognition principles and practice |
| title_sub | principles and practice |
| topic | Modellierung (DE-588)4170297-9 gnd Kognitiver Prozess (DE-588)4140177-3 gnd Kognitive Psychologie (DE-588)4073586-2 gnd Kognition (DE-588)4031630-0 gnd |
| topic_facet | Modellierung Kognitiver Prozess Kognitive Psychologie Kognition |
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