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Response times: their role in inferring elementary mental organization

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Bibliographic Details
Main Author:	Luce, R. Duncan 1925-2012 (Author)
Format:	Book
Language:	English
Published:	New York Oxford Univ. Press u.a. 1986
Edition:	1. print.
Series:	Oxford psychology series 8.
Subjects:	Cognitieve processen Psychologie expérimentale Reactietijd Réaction, Temps de - Modèles mathématiques Mathematisches Modell Mental Processes Models, Theoretical Psychology, Experimental Reaction Time Reaction time > Mathematical models Physiologische Psychologie Experimentelle Psychologie Informationsverarbeitung Reaktionszeit Antwortverhalten Kognition Mensch
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=005780120&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Literaturverz. S. 521 - 545
Physical Description:	XV, 562 S. graph. Darst.
ISBN:	0195036425

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adam_text	Titel: Response times Autor: Luce, Robert Duncan Jahr: 1986 Contents 1. Representing Response Times as Random Variables 1 1.1. The Study of Response Times 1 1.1.1 Why? 1 1.1.2 How? 2 1.1.3 Organization 4 1.2 Random Variables 7 1.2.1 Distribution and Density Functions 8 * 1.2.2 A Somewhat More General Treatment of Random Variables 11 1.2.3 Hazard Functions 13 1.2.4 On Contemplating Distributions 17 1.3 Several Random Variables 20 1.3.1 Joint and Conditional Distributions 20 1.3.2 Bayes Theorem 21 1.3.3 Independence 22 1.3.4 Expectation and Variance 25 1.3.5 Random Samples 26 1.3.6 Random Samples of Random Size 29 1.4 Generating Functions 30 1.4.1 Convolution 30 1.4.2 The Moment Generating Function 32 1.4.3 Three Examples: Exponential, Gaussian, and Ex-Gaussian 34 1.4.4 The Characteristic Function 36 1.4.5 Cumulants 37 1.4.6 Discrete Generating Functions 38 1.4.7 Three Examples: Binomial, Poisson, and Geometric 39 1.5 Elementary Concepts of Stochastic Processes 41 1.5.1 Basic Definitions 41 1.5.2 Some Important Constraints 42 Part I. DETECTION PARADIGMS 47 2. Simple Reaction Times: Basic Data 49 2.1 The Problem 49 2.2 The Simple Reaction-Time Paradigm 51 2.2.1 Reaction Signals 51 2.2.2 Trials and Reaction Time 52 2.2.3 Warning Signals and Foreperiods 53 2.2.4 Random Foreperiods 54 2.2.5 Constant Foreperiods and Catch Trials 55 2.2.6 Anticipations and False Alarms 55 2.2.7 Speed-Accuracy Tradeoff 56 2.2.8 Information Feedback and Payoffs 57 2.3 Effect of Signal Properties on Reaction Time 58 2.3.1 Intensity and the Mean Time 58 2.3.2 Intensity and the Standard Deviation of Time 64 2.3.3 Duration and the Mean Time 65 2.3.4 Subjective Intensity and the Mean Time 68 2.4 Effect of Foreperiod on Reaction Time 71 2.4.1 Constant Foreperiods 72 2.4.2 Equally Likely Foreperiods 73 2.4.3 Exponentially Distributed Foreperiods 75 2.4.4 Interaction of Foreperiod, Intensity, and Presentation Probability 80 2.5 Speed-Accuracy Tradeoff 81 2.5.1 The Phenomenon in Simple Reactions 81 2.5.2 A Representation of Criterion Effects 82 2.5.3 Payoffs and Reaction Time 87 2.6 The Response and Reaction Time 90 2.7 What Faces a Model Builder? 94 3. Decomposition into Decision and Residual Latencies 96 3.1 Independent, Additive Stage Latencies 96 3.2 Specific Assumptions 99 3.2.1 One Variable Gaussian, the Other Exponential 99 3.2.2 One Variable Bounded, the Other Exponential in the Tail 103 3.2.3 One Variable Gamma 105 3.2.4 Responding to the First Spike: A Successful Analysis of an Unsuccessful Model 105 3.2.5 Spline Approximations 113 3.3 Manipulation of Stages 114 3.3.1 Negligible Decision Latencies to Intense Signals 114 3.3.2 Is the Residual Latency Affected by Signal Intensity? 118 3.3.3 Is the Residual Latency Affected by Foreperiod Duration? 118 3.3.4 Independent Manipulation of the Stages 119 3.4 Conclusions 121 4. Distributions of Simple Decision Latencies 122 4.1 Empirical Distributions 122 4.1.1 Estimating Hazard Functions 122 4.1.2 Signals Well Above Threshold 124 4.1.3 Signals Near Threshold 131 4.2 Discrete-Time, Information-Accumulation Detectors 139 4.2.1 The General Schema 139 4.2.2 Binary Sampling and Fixed Criterion: Recruitment Model 141 4.2.3 Binary Events at Almost Periodic Times 142 4.2.4 Variable Accumulation and Fixed Criterion: Random Walk Model 144 4.2.5 Variable Accumulation and Fixed Probability Boundary 147 4.3 Continuous-Time, Information-Accumulation Detectors 149 4.3.1 Deterministic Accumulation and Variable Criterion 149 4.3.2 Deterministic, Aggregated Accumulation: Cascade Model 152 4.3.3 Stochastic Accumulation: Linear, Dynamic, Stochastic Model 154 4.3.4 Binary Accumulation and Fixed Criterion: Counting Processes 155 4.3.5 Binary Decisions with Separable Hazard Functions 156 4.3.6 Binary Increments and Decrements and Fixed Criterion: Differences of Counting Processes 158 4.4 Race Between Level and Change Detectors 159 4.4.1 Change and Level Detectors 159 4.4.2 The Hazard Function for Noise 164 4.4.3 Level Detection as Comparison of an Estimated Local Rate with a Criterion 165 4.4.4 Change Detection as Comparison of Two Estimated Local Rates 167 4.4.5 Change Detection as Neural Post Inhibitory Rebound 167 4.4.6 Why Two Types of Detector? 172 4.5 Conclusions 173 5. Detection of Signals Presented at Irregular Times 175 5.1 Introduction 175 5.2 Vigilance at Low Signal Rates 176 5.3 Vigilance at High Signal Rates 178 5.3.1 Pseudo-Random Presentations and Pseudo-Hazard Functions 178 5.3.2 Signal-Queuing Model for Random Presentations 180 5.4 Interaction of Two Signals in Close Succession 185 5.4.1 The Major Phenomenon: Psychological Refractoriness 185 5.4.2 Signal Queuing: The Single-Channel Hypothesis 188 5.4.3 Data Relevant to the Single-Channel Model 191 5.4.4 Signal Grouping 195 5.4.5 Quantized Perception 196 5.4.6 Perceptual and/or Response Preparation 197 5.4.7 Capacity Sharing 198 5.5 Conclusions 200 Part II. IDENTIFICATION PARADIGMS 203 6. Two-Choice Reaction Times: Basic Ideas and Data 205 6.1 General Considerations 205 6.1.1 Experimental Design 205 6.1.2 Response Measures 207 6.2 Relations to Simple Reaction Times 208 6.2.1 Means and Standard Deviations 208 6.2.2 Donders Subtraction Idea 212 6.2.3 Subtraction with Independence 214 6.2.4 Varying Signal Intensity 217 6.3 A Conceptual Scheme for Tradeoffs 218 6.3.1 Types of Parameters 218 6.3.2 Formal Statement 220 6.3.3 An Example: The Fast Guess Model 221 6.3.4 Discrimination of Color by Pigeons 223 6.4 Discriminability and Accuracy 224 6.4.1 Varying the Response Criterion: Various ROC Curves 224 6.4.2 Varying the Discriminability of the Signals 229 6.4.3 Are Errors Faster, the Same as, or Slower than the Corresponding Correct Responses? 233 6.5 Speed-Accuracy Tradeoff 236 6.5.1 General Concept of a Speed-Accuracy Tradeoff Function (SATF) 236 6.5.2 Use of the SATF 240 6.5.3 Empirical Representations of SATFs 241 I 6.5.4 Conditional Accuracy Function (CAF) 245 6.5.5 Use of the CAF 247 6.5.6 Can CAFs Be Pieced Together to Get the SATF? 248 6.5.7 Conclusions about SAFTs 252 6.6 Sequential Effects 253 6.6.1 Stimulus Controlled Effects on the Mean 254 6.6.2 Facilitation and Expectancy 262 6.6.3 Stimulus-Response Controlled Events 264 6.6.4 Are the Sequential Effects a Speed-Accuracy Tradeoff? 266 6.6.5 Two Types of Decision Strategy 269 6.7 Conclusions 271 7. Mixture Models 273 7.1 Two-State Mixtures 274 7.1.1 Linear Functions 275 7.1.2 Fixed Points 276 •7.1.3 Statistical Test 277 7.2 A Linear Operator Model for Sequential Effects 278 7.2.1 The Model 278 7.2.2 Parameter Estimation 279 7.3 Data With Few Response Errors 281 7.3.1 Linear Functions 281 7.3.2 Fixed Points 283 7.3.3 Estimation of Parameters and Fitting Sequential Data 285 7.4 The Fast Guess Account of Errors 286 7.4.1 The Simple Fast-Guess Model 286 7.4.2 Two Predictions 289 7.4.3 Estimation of the Mean Identification Time 291 7.4.4 Generalized Fast-Guess Model 292 7.4.5 Sequential Effects 292 7.4.6 A Donderian Assumption 293 7.5 A Three-State, Fast-Guess, Memory Model 294 7.5.1 Memory Transitions 294 7.5.2 Responses and Response Times 297 7.5.3 A Strategy for Data Analysis 297 7.6 Data With Response Errors: Highly Discriminable Signals 299 7.6.1 Identification of Color, Auditory Frequency, and Auditory Intensity 299 7.6.2 Identification of Length, Orientation, and Digits 302 7.6.3 Persistent Strategies in the Location of a Light 304 7.6.4 Sequential Effects in the Identification of Orientation 306 7.7 Data With Response Errors: Confusable Signals 308 7.7.1 Identification of Auditory Frequency 308 7.7.2 Detection of Auditory Intensity 309 7.7.3 Persistent Strategies in the Identification of Orientation 311 7.8 Conclusions 316 8. Stochastic Accumulation of Information in Discrete Time 319 8.1 Accumulator Models 320 8.1.1 The Simple Accumulator 320 8.1.2 The Runs Model 322 8.1.3 The Strength Accumulator 325 8.2 Random Walks With Boundaries 326 8.2.1 The Model 327 8.2.2 The Assumption of Small Steps and Wald s Identity 328 8.2.3 Response Probabilities and Mean Decision Latencies 329 8.2.4 Gaussian Steps 331 8.2.5 Distributions of Decision Latencies 333 8.2.6 Proofs 335 8.3 Restrictions on the Random Walk Model 340 8.3.1 The Classical Optimum Model: Sequential Probability Ratio Test (SPRT) 340 8.3.2 On Testing SPRT 341 8.3.3 Symmetric Stimulus Representation (SSR) 342 8.3.4 On Testing SSR 344 8.3.5 Restrictions on Boundaries 346 8.3.6 Proofs 348 8.4 Modifications of the Random Walk 349 8.4.1 Linear Boundary Changes: Biased SPRT Model 350 8.4.2 Premature Sampling 351 8.5 Data 352 8.5.1 Attempts to Vary Accumulation of Information 353 8.5.2 Accumulator Model Analyses of Attempts to Manipulate the Response Parameters 357 8.5.3 SPRT Analysis of Attempts to Manipulate the Response Parameters 357 8.5.4 SSR Analysis with Signal-Dependent Residuals 359 8.5.5 SSR Analysis with Response-Dependent Residuals 363 8.5.6 A Gaussian Analysis 365 8.6 Conclusions 366 9. Stochastic Accumulation of Information in Continuous Time 367 9.1 Introduction 367 9.2 Additive Processes 368 9.2.1 Diffusion Processes 369 9.2.2 An Alternative Derivation 370 9.2.3 Generalizations 373 9.2.4 Poisson Processes 374 9.2.5 Diffusion Boundaries and Poisson Accumulation 375 9.2.6 A Flash-Rate Experiment 376 9.3 Renewal Processes 379 9.3.1 A Counting Model 381 9.3.2 A Timing Model 382 9.3.3 Plash-Rate Data 384 9.3.4 Deadline Data 384 9.4 Conclusions 388 10. Absolute Identification of More Than Two Signals 389 10.1 Types of Designs with More than Two Signals 389 10.2 Experiments with Few Errors: Mean Times 390 10.2.1 The Stimulus Information Hypothesis 390 10.2.2 Alternative Accounts of Hick s Logarithmic Relation 393 10.2.3 Practice and Stimulus-Response Compatibility 395 10.3 Experiments with Few Errors: Sequential Effects 399 10.3.1 First-Order Stimulus Effects 399 10.3.2 Higher-Order Stimulus Effects 407 10.3.3 First-Order Response Effects 408 10.4 Experiments With Errors 410 10.4.1 Empirical Speed-Accuracy Tradeoff Functions 410 10.4.2 A Response Discard Model 412 10.4.3 A Generalization of SPRT 418 10.4.4 Accelerating Cycle Model 419 10.5 Conclusions 421 Part III. MATCHING PARADIGMS 423 11. Memory Scanning, Visual Search, and Same-Difference Designs 425 11.1 Memory Scanning and Visual Search 425 11.1.1 The Experimental Designs 425 11.1.2 Typical Memory Scanning Data 426 11.2 The Serial, Exhaustive Search Model 427 11.2.1 The Model 427 11.2.2 Reactions to the SES Model 429 11.2.3 Failures of Prediction 429 11.3 Alternative Models 431 11.3.1 A Serial, Self-Terminating Search Model: Push-Down Stack 432 11.3.2 Comparison of Serial-Exhaustive and Self-Terminating Models 432 11.3.3 A Parallel, Exhaustive Search, Capacity Reallocation Model 435 11.3.4 An Optimal, Capacity-Reallocation Model 435 *11.3.5 Proof of Eq. 11.46 437 11.3.6 A Parallel, Self-Terminating Search Model Based on a Continuous Random Walk 438 11.3.7 Distributive Memory Model 440 11.4 Same-Different experiments 445 11.4.1 The Basic Data 445 11.4.2 Modified Error-Free Stage Models 447 11.4.3 Serial, Exhaustive, Imperfect Search 448 11.4.4 A Continuous Random Walk Model 451 11.5 Conclusions 454 12. Processing Stages and Strategies 456 12.1 Introduction 456 12.2 Serial-Parallel Equivalences 457 12.2.1 General Nomenclature 457 12.2.2 Mimicking Overall Decision Times 458 12.2.3 Mimicking Intercompletion Times by Serial and Parallel Exhaustive Searches 459 12.2.4 Mimicking Intercompletion Times and Order by Serial and Parallel Exhaustive Searches 459 12.2.5 Mimicking Intercompletion Times and Order by Physically Realizable Searches 460 12.2.6 Subconclusion 464 12.3 Simultaneous Accounts of Several Experiments 464 12.3.1 A Serial or Parallel, Exhaustive or Self-Terminating Testing Paradigm 464 12.3.2 How Distinct are the Predictions? 466 12.3.3 An Experiment 467 12.3.4 A Variant on the Paradigm 469 12.4 Selective Manipulation of Stages 472 12.4.1 The Additive-Factors Method 473 12.4.2 Illustrative Applications of the Model 477 12.4.3 Can a Parallel System Mimic the Means of a Serial System? 480 12.4.4 Cautions and Concerns 481 12.4.5 Speed-Accuracy Tradeoff 483 12.4.6 Critical Path Generalization of the Additive-Factors Method 485 12.4.7 An Application of the Critical Path Method 488 12.4.8 Inferring the Processing Network from Comparability Data 489 12.5 Conclusions 490 APPENDIXES 493 A. Asymptotic Results for Independent Random Variables 495 A.I Serial Models: Sum of Random Variables 495 A.I.I Identical Random Variables 495 A.1.2 Distribution of Counts in a Renewal Process 497 A. 1.3 Nearly Identical Random Variables 499 A.1.4 Identical Gaussians 500 A.1.5 Identical Exponentials 500 A.2 Parallel Models: Maxima of Identical Random Variables 502 A.2.1 Form of Asymptotic Distributions 502 A.2.2 Conditions on Distributions Leading to Each Asymptotic Distribution 504 B. Properties of Continuous Distributions for Simple Reaction Times 507 B.I Theoretically Based Distributions 507 B.2 Ad Hoc Distributions 510 C. Experimental Data 512 C.I Yellott (1971), Experiment 3 Data 512 C.2 Oilman (1970), Experiment 2 Data 514 C.3 Laming (1968), Experiment 2 Data 515 C.4 Link (1975) Data 515 C.5 Carterette, Friedman, and Cosmides (1965) Data 516 C.6 Green and Luce (1973) Data 517 C.7 Green, Smith, and von Gierke (1983) Data 519 C.8 Laming (1968), Experiment 6 Data 520 References 521 Author Index 547 Subject Index 554
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id	DE-604.BV008752814
illustrated	Illustrated
indexdate	2024-12-20T09:26:18Z
institution	BVB
isbn	0195036425
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-005780120
oclc_num	12341681
open_access_boolean
owner	DE-824
owner_facet	DE-824
physical	XV, 562 S. graph. Darst.
publishDate	1986
publishDateSearch	1986
publishDateSort	1986
publisher	Oxford Univ. Press u.a.
record_format	marc
series	Oxford psychology series
series2	Oxford psychology series
spellingShingle	Luce, R. Duncan 1925-2012 Response times their role in inferring elementary mental organization Oxford psychology series Cognitieve processen gtt Psychologie expérimentale Reactietijd gtt Réaction, Temps de - Modèles mathématiques Mathematisches Modell Mental Processes Models, Theoretical Psychology, Experimental Reaction Time Reaction time Mathematical models Physiologische Psychologie (DE-588)4076126-5 gnd Experimentelle Psychologie (DE-588)4016005-1 gnd Informationsverarbeitung (DE-588)4161678-9 gnd Reaktionszeit (DE-588)4048656-4 gnd Antwortverhalten (DE-588)4122814-5 gnd Kognition (DE-588)4031630-0 gnd Mathematisches Modell (DE-588)4114528-8 gnd Mensch (DE-588)4038639-9 gnd
subject_GND	(DE-588)4076126-5 (DE-588)4016005-1 (DE-588)4161678-9 (DE-588)4048656-4 (DE-588)4122814-5 (DE-588)4031630-0 (DE-588)4114528-8 (DE-588)4038639-9
title	Response times their role in inferring elementary mental organization
title_auth	Response times their role in inferring elementary mental organization
title_exact_search	Response times their role in inferring elementary mental organization
title_full	Response times their role in inferring elementary mental organization R. Duncan Luce
title_fullStr	Response times their role in inferring elementary mental organization R. Duncan Luce
title_full_unstemmed	Response times their role in inferring elementary mental organization R. Duncan Luce
title_short	Response times
title_sort	response times their role in inferring elementary mental organization
title_sub	their role in inferring elementary mental organization
topic	Cognitieve processen gtt Psychologie expérimentale Reactietijd gtt Réaction, Temps de - Modèles mathématiques Mathematisches Modell Mental Processes Models, Theoretical Psychology, Experimental Reaction Time Reaction time Mathematical models Physiologische Psychologie (DE-588)4076126-5 gnd Experimentelle Psychologie (DE-588)4016005-1 gnd Informationsverarbeitung (DE-588)4161678-9 gnd Reaktionszeit (DE-588)4048656-4 gnd Antwortverhalten (DE-588)4122814-5 gnd Kognition (DE-588)4031630-0 gnd Mathematisches Modell (DE-588)4114528-8 gnd Mensch (DE-588)4038639-9 gnd
topic_facet	Cognitieve processen Psychologie expérimentale Reactietijd Réaction, Temps de - Modèles mathématiques Mathematisches Modell Mental Processes Models, Theoretical Psychology, Experimental Reaction Time Reaction time Mathematical models Physiologische Psychologie Experimentelle Psychologie Informationsverarbeitung Reaktionszeit Antwortverhalten Kognition Mensch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=005780120&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV000005639
work_keys_str_mv	AT lucerduncan responsetimestheirroleininferringelementarymentalorganization

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