Fiedler K. (Ed), Juslin P. (Ed)0511344430
Table of contents :
Cover……Page 1
Half-title……Page 3
Title……Page 5
Copyright……Page 6
Contents……Page 7
List of Contributors……Page 9
Part I Introduction……Page 11
1 Taking the Interface between Mind and Environment Seriously……Page 13
the naïve intuitive statistician……Page 14
Sampling — a universal aspect of psychological theorizing……Page 16
Sampling Constraints in the Internal and External Environments……Page 17
A taxonomy of sampling processes……Page 19
Degree of Conditionality……Page 21
Unit Size: Elementary versus Complex……Page 23
Applying the sampling taxonomy……Page 24
Organization of the present volume……Page 28
Concluding remarks……Page 33
References……Page 35
Part II the psychological law of large numbers……Page 41
2 Good Sampling, Distorted Views: The Perception of Variability……Page 43
The statistical argument……Page 45
Experimental evidence……Page 46
Experiment 1 – Is Variability Noted and Remembered Veridically?……Page 47
Experiment 2 – Perception of Variability: Sample-Based
versus Population-Based……Page 49
Experiment 3 – Perception of Variability: Relationship
to Working-Memory Capacity……Page 52
Experiment 4 – Variability as Observed, or Corrected for Sample Size?
Evidence from Choices and Confidence in Them……Page 54
Experiment 5 – Sample Composition: A Primacy Effect……Page 57
General discussion……Page 59
References……Page 61
3 Intuitive Judgments about Sample Size……Page 63
Answer alternatives (both versions):……Page 64
Associative learning and sample-based judgments……Page 68
PASS: Basic Mechanisms……Page 69
Accuracy and Sample Size……Page 70
Confidence and Sample Size……Page 71
Implicit Sample-Size Information: The Role
of Representational Format……Page 73
When to expect biased sample-based judgments……Page 74
Unbiased Input and Biased Responses……Page 75
Sampling Distribution Tasks……Page 77
References……Page 79
4 The Role of Information Sampling in Risky Choice……Page 82
Introduction……Page 83
Decisions from experience in monetary gambles……Page 85
Sample Size Matters……Page 89
Sampling Order Matters……Page 93
Information integration in decisions from experience: the value-updating model……Page 94
Conclusions……Page 96
A Mere Mention Lends Weight……Page 97
Small Samples Show Less Variability……Page 98
Epilogue……Page 99
References……Page 100
Introduction……Page 102
Small samples and early detection of correlation……Page 104
Can less knowledge be an advantage?……Page 106
Disinterested Inquiry……Page 107
Deciding on a Course of Action……Page 109
Detection of Correlation in Simulated Environments……Page 111
Can less never be more? a new look at advantages of small samples……Page 116
Looking for Alternative Assumptions Leading
to Small-Sample Advantage……Page 117
Peter Juslin……Page 125
Nick Chater……Page 126
Klaus Fiedler……Page 127
Appendix a……Page 129
Appendix b……Page 130
Appendix c……Page 131
References……Page 132
Part III Biased and Unbiased Judgments from Biased Samples……Page 135
6 Subjective Validity Judgments as an Index of Sensitivity to Sampling Bias……Page 137
The sampling approach to judgment biases……Page 138
The information search paradigm……Page 139
The subjective validity paradigm……Page 141
Sensitivity to Information Generation versus Information Integration……Page 143
Sensitivity by Design……Page 145
The Matter-of-Factness of Sample Statistics……Page 148
Feedback on the Appropriateness of Sampling Procedures……Page 149
Sensitivity by Experience……Page 150
Status of the svp as an experimental tool……Page 152
References……Page 155
7 An Analysis of Structural Availability Biases, and a Brief Study……Page 157
Overview of the small study……Page 159
Materials……Page 160
Discussion……Page 161
References……Page 162
Introduction……Page 163
Phenomena of subjective confidence……Page 166
What Happened to Overconfidence in Two-Choice Questions?……Page 168
Overconfidence in interval estimates i: naïve sampling……Page 171
The Naïve Sampling Model……Page 173
Why Should Assessment Format Matter with Nave Sampling?……Page 174
Overconfidence in interval estimates ii: biased sampling and interpretation……Page 178
Why Should Assessment Format Matter with Biased Sampling?……Page 180
Differences among Methods of Eliciting Subjective Intervals……Page 182
Discussion……Page 184
References……Page 189
Introduction……Page 193
Overview of the procedure and analysis……Page 197
General Procedure……Page 198
Source Monitoring Analysis……Page 199
Biased group impressions from trivariate samples……Page 202
Simplistic Reasoning or Pseudo-Contingency……Page 203
Analysis of Interindividual Differences……Page 206
Testing new predictions of the pseudo-contingency account……Page 207
Group Judgments on the Basis of a New Stimulus Distribution……Page 208
Group Judgments on the Basis of Incomplete Trivariate Information……Page 213
Discussion……Page 216
Summary……Page 217
References……Page 218
10 Mental Mechanisms: Speculations on Human Causal Learning and Reasoning……Page 220
Mental mechanisms versus logical representation……Page 222
Reasoning with and without mental mechanisms: sampling in the monty hall problem……Page 227
Learning mental mechanisms from data……Page 237
Discussion……Page 241
References……Page 243
Part IV What Information Contents are Sampled?……Page 247
Preview……Page 249
Who samples?……Page 250
Why sampling?……Page 252
Study Ideal Types, Not Samples……Page 253
Convenience Samples……Page 254
Random Samples……Page 255
Do researchers sample participants?……Page 256
Do researchers sample objects?……Page 258
Do researchers sample variables?……Page 260
When Is It Adaptive Not to Sample?……Page 261
Random Sampling……Page 263
Sequential Sampling……Page 264
Does the mind sample variables?……Page 265
Take the Best……Page 266
What’s in a sample?……Page 267
References……Page 268
12 Assessing Evidential Support in Uncertain Environments……Page 271
Support Theory……Page 272
ESAM……Page 274
Normative Benchmark……Page 279
General Method……Page 281
Typical Results……Page 282
Testing ESAM’s Assumptions……Page 283
ESAM’s Accuracy……Page 284
Environments……Page 285
Accuracy……Page 289
ESAM’s Parameters……Page 292
ESAM’s Performance……Page 293
Other models……Page 304
Summary……Page 306
References……Page 307
13 Information Sampling in Group Decision Making: Sampling Biases and Their Consequences……Page 309
Biased sampling in favor of shared information……Page 310
Collective Information Sampling……Page 311
Sequential Entry of Shared and Unshared Information into the Discussion……Page 313
Repeating Shared and Unshared Information……Page 315
Mutual Enhancement……Page 316
Biased sampling in favor of preference-consistent information……Page 319
The Bias toward Discussing Preference-Consistent Information……Page 320
Preference-Consistent Framing of Information……Page 322
Biased Group Search for External Information……Page 323
The consequences of biased information sampling in groups……Page 325
Group-Level Explanations for the Failure of Groups to Solve Hidden Profiles……Page 327
Biased Information Evaluation as an Explanation for the Failure to Solve Hidden Profiles……Page 328
Conclusion……Page 331
References……Page 333
Background……Page 337
Aggregation by individual dms and confidence in the aggregate……Page 339
A model of the aggregation process and confidence……Page 342
Empirical tests of the model……Page 345
Discussion of the model theoretical and practical implications……Page 353
Future directions……Page 357
References……Page 359
15 Self as Sample……Page 363
Collectivism and individualism in social psychology……Page 364
Self-stereotyping……Page 366
Category Salience……Page 367
Attribute Valence……Page 368
Threat to Self……Page 369
Response Time……Page 370
Response Variability……Page 371
Sampling the self in the laboratory……Page 373
Social categorization……Page 375
A projection model of in-group bias……Page 377
Conclusion……Page 381
References……Page 382
Part V Vicissitudes of Sampling in the Researchers Minds and Methods……Page 389
16 Which World Should Be Represented in Representative Design?……Page 391
Brunswik’s critique of psychologists’ way of conducting business……Page 392
Probabilistic Functionalism and Representative Design……Page 393
Do Judgment Policies Differ in Representative versus Systematic Designs?……Page 396
How Rational Do People Appear in Representative and Systematic Designs? The Case of Overconfidence and Hindsight Bias……Page 398
Representative design and size of the reference class……Page 402
Study 1: Over-/Underconfidence Depends on the Size
of the Reference Class……Page 404
Study 2: Policy Capturing and the Size of the Reference Class……Page 407
Selection of the Reference Class: A Time-Honored
and Ubiquitous Problem……Page 411
Selection of the Reference Class in Psychological Theory and Experimental Practice……Page 412
References……Page 414
17 “I’m m n Confident That I’m Correct”: Confidence in Foresight and Hindsight as
a Sampling Probability……Page 419
Trust in One’s Reasoning……Page 421
Trust in One’s Senses……Page 424
Over- and underestimation in hindsight……Page 427
Why Is Representative Design Essential to Studies of Hindsight Bias?……Page 429
I Never Would Have Known That: A Reversal of the Hindsight Bias……Page 434
What Does Random Sampling of Items Do to the Hindsight Bias?……Page 436
“I Was Well Calibrated All Along!”……Page 441
Are There Global Hindsight Effects?……Page 442
Conclusions……Page 444
References……Page 446
18 Natural Sampling of Stimuli in (Artificial) Grammar Learning……Page 450
Agl as a representative design for natural grammar learning study……Page 453
How natural sampling of experimental stimuli can bring back the old agenda of agl……Page 455
The impact of the frequency distribution of learning exemplars on the learnability of the grammar: a simulation……Page 456
Discussion……Page 462
References……Page 464
19
Is Confidence in Decisions Related to Feedback?
Evidence from Random Samples of
Real-World Behavior……Page 466
Participants……Page 470
Procedure……Page 472
Checks on Data……Page 474
ESM Questionnaire Results……Page 475
Current Activities and Domains of Decisions……Page 476
Orientation……Page 479
Confidence in the “Right” Decision……Page 481
Feedback……Page 482
Confidence, Feedback, and Time……Page 486
discussion……Page 487
conclusions……Page 490
References……Page 493
Index……Page 495
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