Information Sampling and Adaptive Cognition

ISBN-13: 9780521539333 / Angielski / Miękka / 2005 / 498 str.

Klaus Fiedler; Peter Juslin

Opis

Even the most intelligent and informed judges and decision makers have been shown to be prone to errors and biases with potentially serious consequences. Previous scientific attempts to explain the causes of wrong decisions have mainly focused on shortcomings in the cognitive processing of the information given. The recent research reviewed in the present volume offers an intriguing alternative for consideration. Although the input of environmental information samples may be biased, the cognitive processing of information samples is often unbiased and remarkably accurate.

Spis treści

Part I. Introduction: 1. Taking the interface between mind and environment seriously Klaus Fiedler and Peter Juslin; Part II. The Psychological Law of Large Number: 2. Good sampling, distorted views: the perception of variability Yakoov Kareev; 3. Intuitive judgments about sample size Peter Sedlmeier; 4. Risky prospects: when valued through a window of sampled experiences Ralph Hertwig, Greg Barron, Elke Weber and Ido Erev; 5. Less is more in contingency assessment - or is it? 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 Peter Freytag and Klaus Fiedler; 7. An analysis of structural availability biases and a brief study Robyn Dawes; 8. Subjective confidence and the sampling of knowledge Joshua Klayman, Jack Soll, Peter Juslin, and Anders Winman; 9. Contingency learning and biased group impressions Thorsten Meiser; 10. Mental mechanisms: speculations on human causal learning and reasoning Nick Chater and Mike Oaksford; Part IV. What Information Contents Are Sampled?: 11. What's in a sample? A manual for building cognitive theories Gerd Gigerenzer; 12. Assessing evidential support in an uncertain environment Chris M. White and Derek Koehler; 13. Information sampling in group decision making: sampling biases and their consequences Andreas Mojzisch and Stefan Schulz-Hardt; 14. Confidence in aggregation of opinions from multiple sources David Budescu, Adrian K. Rantilla, Tzur M. Kareliz and Hsiu Ting Yu; 15. Self as a sample Joachim Krueger, Melissa Acevedo and Jordan Robbins; Part V. Vicissitudes of Sampling in the Researcher's Mind and Method: 16. Which world should be represented in representative design? Ulrich Hoffrage and Ralph Hertwig; 17. 'I'm m/n confident that I'm correct': confidence in foresight and hindsight as a sampling probability Anders Winman and Peter Juslin; 18. Natural sampling of stimuli in (artificial) grammar learning Fenna Poletiek; 19. Is confidence in decisions related to feedback? Evidence - lack of evidence - from random samples of real-world behavior Robin Hogarth.