Information Sampling and Adaptive Cognition

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 biases inherent in the samples of information input with which judges and decision makers are fed. Because 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 “naïve intuitive statisticians”: They are 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 reinterpreting findings obtained in the past two decades of research on judgment and decision making.

Klaus Fiedler is Professor of Psychology at the University of Heidelberg in Germany. Among his main research interests are cognitive social psychology, language and communication, social memory, inductive cognitive processes in judgment and decision making, and computer modeling of the human mind. Professor Fiedler is the winner of the 2000 Leibniz Award.

Peter Juslin is Professor of Psychology at Uppsala University in Sweden. His main research interests concern judgment and decision making, categorization, and computational modeling. He received the Brunswik New Scientist Award in 1994 and the Oscar’s Award at Uppsala University in 1996 for young distinguished scientists. He has published numerous scientific papers in various journals, including many articles in the major APA journals such as Psychological Review.

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Information Sampling and Adaptive Cognition

Edited by

KLAUS FIEDLER
University of Heidelberg

PETER JUSLIN
Uppsala University

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CAMBRIDGE UNIVERSITY PRESS
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© Cambridge University Press 2006

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no reproduction of any part may take place without
the written permission of Cambridge University Press.

First published 2006

Printed in the United States of America

A catalog record for this publication is available from the British Library.

Library of Congress Cataloging in Publication Data

Information sampling and adaptive cognition / edited by Klaus Fiedler, Peter Juslin.
p. cm.
Includes bibliographical references and index.
ISBN 0-521-83159-8 (casebound) – ISBN 0-521-53933-1 (pbk.)
1. Human information processing. 2. Cognition. I. Fiedler, Klaus, 1951–
II. Juslin, Peter. III. Title.
BF444.155    2005
153.4 – dc22    2004025856

ISBN-13 978-0-521-83159-8 hardback
ISBN-10 0-521-83159-8 hardback

ISBN-13 978-0-521-53933-3 paperback
ISBN-10 0-521-53933-1 paperback

Cambridge University Press has no responsibility for
the persistence or accuracy of URLs for external or
third-party Internet Web sites referred to in this publication
and does not guarantee that any content on such
Web sites is, or will remain, accurate or appropriate.

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Contents

List of Contributors			page vii
PART I    INTRODUCTION
1.		Taking the Interface between Mind and Environment Seriously	3
		Klaus Fiedler and Peter Juslin
PART II    THE PSYCHOLOGICAL LAW OF LARGE NUMBERS
2.		Good Sampling, Distorted Views: The Perception of Variability	33
		Yaakov Kareev
3.		Intuitive Judgments about Sample Size	53
		Peter Sedlmeier
4.		The Role of Information Sampling in Risky Choice	72
		Ralph Hertwig, Greg Barron, Elke U. Weber, and Ido Erev
5.		Less Is More in Covariation Detection – Or Is It?	92
		Peter Juslin, Klaus Fiedler, and Nick Chater
PART III    BIASED AND UNBIASED JUDGMENTS FROM BIASED SAMPLES
6.		Subjective Validity Judgments as an Index of Sensitivity to Sampling Bias	127
		Peter Freytag and Klaus Fiedler
7.		An Analysis of Structural Availability Biases, and a Brief Study	147
		Robyn M. Dawes
8.		Subjective Confidence and the Sampling of Knowledge	153
		Joshua Klayman, Jack B. Soll, Peter Juslin, and Anders Winman
9.		Contingency Learning and Biased Group Impressions	183
		Thorsten Meiser
10.		Mental Mechanisms: Speculations on Human Causal Learning and Reasoning	210
		Nick Chater and Mike Oaksford
PART IV    WHAT INFORMATION CONTENTS ARE SAMPLED?
11.		What’s in a Sample? A Manual for Building Cognitive Theories	239
		Gerd Gigerenzer
12.		Assessing Evidential Support in Uncertain Environments	261
		Chris M. White and Derek J. Koehler
13.		Information Sampling in Group Decision Making: Sampling Biases and Their Consequences	299
		Andreas Mojzisch and Stefan Schulz-Hardt
14.		Confidence in Aggregation of Opinions from Multiple Sources	327
		David V. Budescu
15.		Self as Sample	353
		Joachim I. Krueger, Melissa Acevedo, and Jordan M. Robbins
PART V    VICISSITUDES OF SAMPLING IN THE RESEARCHER’S MIND AND METHOD
16.		Which World Should Be Represented in Representative Design?	381
		Ulrich Hoffrage and Ralph Hertwig
17.		“I’m m/n Confident That I’m Correct”: Confidence in Foresight and Hindsight as a Sampling Probability	409
		Anders Winman and Peter Juslin
18.		Natural Sampling of Stimuli in (Artificial) Grammar Learning	440
		Fenna H. Poletiek
19.		Is Confidence in Decisions Related to Feedback? Evidence from Random Samples of Real-World Behavior	456
		Robin M. Hogarth
Index			485

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List of Contributors

Melissa Acevedo, Brown University

Greg Barron, Harvard Business School

David J. Budescu, University of Illinois

Nick Chater, University College, London

Robyn Dawes, Carnegie Mellon University

Ido Erev, Technion, Israel Institute of Technology

Klaus Fiedler, University of Heidelberg, Germany

Peter Freytag, University of Heidelberg, Germany

Gerd Gigerenzer, Max Planck Institute, Germany

Ralph Hertwig, University of Basel, Switzerland

Ulrich Hoffrage, Max Planck Institute, Germany

Robin M. Hogarth, Universitat Pompeu Fabra, Spain

Peter Juslin, University of Uppsala, Sweden

Yakoov Kareev, The Hebrew University of Jerusalem

Joshua Klayman, University of Chicago Graduate School of Business

Derek S. Koehler, University of Waterloo, Canada

Joachim Krueger, Brown University

Thorsten Meiser, University of Jena, Germany

Andreas Mojzisch, Universität Göttingen, Germany

Mike Oaksford, University of London

Fenna Poletiek, Leiden University, The Netherlands

Jordan M. Robbins, Brown University

Stefan Schulz-Hardt, Universität München, Germany

Peter Sedlmeier, Chemnitz University of Technology, Germany

Jack B. Soll, INSEAD Business School, France

Elke Weber, Columbia University

Chris M. White, Université de Lausanne, Switzerland

Anders Winman, Uppsala University, Sweden