Information Sampling and Adaptive Cognition

Author: Klaus Fiedler; Peter Juslin  

Publisher: Cambridge University Press‎

Publication year: 2005

E-ISBN: 9780511343209

P-ISBN(Paperback): 9780521831598

Subject: B842 心理过程与心理状态

Keyword: 发展心理学(人类心理学)

Language: ENG

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Information Sampling and Adaptive Cognition

Description

A 'sample' is not only a concept from statistics that has penetrated common sense but also a metaphor that has inspired much research and theorizing in current psychology. The sampling approach emphasizes the selectivity and the biases that are inherent in the samples of information input with which judges and decision makers are fed. As environmental samples are rarely random, or representative of the world as a whole, decision making calls for censorship and critical evaluation of the data given. However, even the most intelligent decision makers tend to behave like 'näive intuitive statisticians': quite sensitive to the data given but uncritical concerning the source of the data. Thus, the vicissitudes of sampling information in the environment together with the failure to monitor and control sampling effects adequately provide a key to re-interpreting findings obtained in the last two decades of research on judgment and decision making.

Chapter

Experiment 3 – Perception of Variability: Relationship to Working-Memory Capacity

Experiment 4 – Variability as Observed, or Corrected for Sample Size? Evidence from Choices and Confidence in Them

Experiment 5 – Sample Composition: A Primacy Effect

General discussion

References

3 Intuitive Judgments about Sample Size

Different kinds of sample-size tasks

Frequency Distribution Tasks versus Sampling Distribution Tasks

Introductory text (both versions):

Sampling distribution version:

Frequency distribution version:

Answer alternatives (both versions):

Associative learning and sample-based judgments

PASS: Basic Mechanisms

Accuracy and Sample Size

Confidence and Sample Size

Implicit Sample-Size Information: The Role of Representational Format

When to expect biased sample-based judgments

Biased Input and Biased Responses

Unbiased Input and Biased Responses

The Impact of Encoding Processes

Deliberative versus Intuitive Judgments

Sampling Distribution Tasks

Summary and conclusion

References

4 The Role of Information Sampling in Risky Choice

Introduction

Decisions from experience in monetary gambles

Information search in decisions from experience

Sample Size Matters

Sampling Order Matters

Information integration in decisions from experience: the value-updating model

Conclusions

A Mere Mention Lends Weight

Small Samples Show Less Variability

Primacy and Recency Join Forces

Epilogue

References

5 Less Is More in Covariation Detection - Or Is It?

Introduction

Small samples and early detection of correlation

Can less knowledge be an advantage?

Disinterested Inquiry

Deciding on a Course of Action

Detection of Correlation in Simulated Environments

Interim Conclusion 1

Can less never be more? a new look at advantages of small samples

Looking for Alternative Assumptions Leading to Small-Sample Advantage

Interim Conclusion 2

Conclusions (separately by the authors)

Peter Juslin

Nick Chater

Klaus Fiedler

Appendix a

Appendix b

Appendix c

References

Part III Biased and Unbiased Judgments from Biased Samples

6 Subjective Validity Judgments as an Index of Sensitivity to Sampling Bias

The sampling approach to judgment biases

The information search paradigm

The subjective validity paradigm

Sensitivity to Information Generation versus Information Integration

Sensitivity by Design

The Matter-of-Factness of Sample Statistics

Feedback on the Appropriateness of Sampling Procedures

Sensitivity by Experience

Status of the svp as an experimental tool

Author Note

References

7 An Analysis of Structural Availability Biases, and a Brief Study

Overview of the small study

Materials

Results

Discussion

References

8 Subjective Confidence and the Sampling of Knowledge

Introduction

Phenomena of subjective confidence

What Happened to Overconfidence in Two-Choice Questions?

Overconfidence in interval estimates i: naïve sampling

The Naïve Sampling Model

Why Should Assessment Format Matter with Nave Sampling?

Overconfidence in interval estimates ii: biased sampling and interpretation

Why Should Assessment Format Matter with Biased Sampling?

Differences among Methods of Eliciting Subjective Intervals

Discussion

References

9 Contingency Learning and Biased Group Impressions

Introduction

Overview of the procedure and analysis

General Procedure

Source Monitoring Analysis

Biased group impressions from trivariate samples

Simplistic Reasoning or Pseudo-Contingency

Analysis of Interindividual Differences

Testing new predictions of the pseudo-contingency account

Group Judgments on the Basis of a New Stimulus Distribution

Group Judgments on the Basis of Incomplete Trivariate Information

Discussion

Summary

References

10 Mental Mechanisms: Speculations on Human Causal Learning and Reasoning

Mental mechanisms versus logical representation

Reasoning with and without mental mechanisms: sampling in the monty hall problem

Learning mental mechanisms from data

Discussion

References

Part IV What Information Contents are Sampled?

11 Whats in a Sample? A Manual for Building Cognitive Theories

Preview

Who samples?

Whats in a sample?

Why sampling?

How to sample?

Study Ideal Types, Not Samples

Convenience Samples

Random Samples

Sequential Sampling

Do researchers sample participants?

Do researchers sample objects?

Do researchers sample variables?

Do minds sample objects?

When Is It Adaptive Not to Sample?

Convenience Sampling

Random Sampling

Sequential Sampling

Does the mind sample variables?

Take the Best

Tallying

What's in a sample?

References

12 Assessing Evidential Support in Uncertain Environments

The model

Support Theory

ESAM

Normative Benchmark

Experimental tests

General Method

Typical Results

Fitting ESAM to the Data

Testing ESAM's Assumptions

ESAM's Accuracy

Simulations

Environments

Accuracy

ESAM's Parameters

ESAM's Performance

Conclusions

Other models

Summary

References

13 Information Sampling in Group Decision Making: Sampling Biases and Their Consequences

Biased sampling in favor of shared information

Collective Information Sampling

Sequential Entry of Shared and Unshared Information into the Discussion

Repeating Shared and Unshared Information

Mutual Enhancement

Biased sampling in favor of preference-consistent information

The Bias toward Discussing Preference-Consistent Information

Preference-Consistent Framing of Information

Biased Group Search for External Information

The consequences of biased information sampling in groups

Group-Level Explanations for the Failure of Groups to Solve Hidden Profiles

Biased Information Evaluation as an Explanation for the Failure to Solve Hidden Profiles

Conclusion

References

14 Confidence in Aggregation of Opinions from Multiple Sources

Background

Aggregation by individual dms and confidence in the aggregate

A model of the aggregation process and confidence

Empirical tests of the model

Discussion of the model theoretical and practical implications

Future directions

Acknowledgements

References

15 Self as Sample

Collectivism and individualism in social psychology

Self-stereotyping

Category Salience

Attribute Valence

Threat to Self

Social projection

Response Time

Response Variability

Sampling the self in the laboratory

Social categorization

A projection model of in-group bias

Conclusion

References

Part V Vicissitudes of Sampling in the Researchers Minds and Methods

16 Which World Should Be Represented in Representative Design?

Brunswik's critique of psychologists' way of conducting business

Probabilistic Functionalism and Representative Design

Does sampling of experimental stimuli matter?

Do Judgment Policies Differ in Representative versus Systematic Designs?

How Rational Do People Appear in Representative and Systematic Designs? The Case of Overconfidence and Hindsight Bias

Representative design and size of the reference class

Study 1: Over-/Underconfidence Depends on the Size of the Reference Class

Study 2: Policy Capturing and the Size of the Reference Class

Discussion

Selection of the Reference Class: A Time-Honored and Ubiquitous Problem

Selection of the Reference Class in Psychological Theory and Experimental Practice

Epilogue

References

17 “I’m m n Confident That I’m Correct”: Confidence in Foresight and Hindsight as a Sampling Probability

Over- and underconfidence in foresight

Trust in One's Reasoning

Trust in One's Senses

Over- and underestimation in hindsight

Why Is Representative Design Essential to Studies of Hindsight Bias?

I Never Would Have Known That: A Reversal of the Hindsight Bias

What Does Random Sampling of Items Do to the Hindsight Bias?

“I Was Well Calibrated All Along!”

Are There Global Hindsight Effects?

Conclusions

References

18 Natural Sampling of Stimuli in (Artificial) Grammar Learning

Agl as a representative design for natural grammar learning study

How natural sampling of experimental stimuli can bring back the old agenda of agl

The impact of the frequency distribution of learning exemplars on the learnability of the grammar: a simulation

Discussion

References

19 Is Confidence in Decisions Related to Feedback? Evidence from Random Samples of Real-World Behavior

Method

Participants

Procedure

Results

Responses

Checks on Data

ESM Questionnaire Results

Current Activities and Domains of Decisions

Action Types

Orientation

Frequency

Confidence in the "Right" Decision

Feedback

Confidence, Feedback, and Time

discussion

conclusions

References

Index

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