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Experience matters: information acquisition optimizes probability gain

Nelson, JD, McKenzie, CRM, Cottrell, GW and Sejnowski, TJ (2010) Experience matters: information acquisition optimizes probability gain Psychological Science, 21 (7). pp. 960-969.

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Abstract

Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects’ information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects’ preference for probability gain is robust, suggesting that the other models contribute little to subjects’ search behavior.

Item Type: Article
Subjects : Psychology
Divisions : Faculty of Health and Medical Sciences > School of Psychology
Authors :
NameEmailORCID
Nelson, JDUNSPECIFIEDUNSPECIFIED
McKenzie, CRMUNSPECIFIEDUNSPECIFIED
Cottrell, GWUNSPECIFIEDUNSPECIFIED
Sejnowski, TJUNSPECIFIEDUNSPECIFIED
Date : 1 July 2010
Identification Number : 10.1177/0956797610372637
Copyright Disclaimer : © The Author(s) 2010. This is an Accepted Manuscript of an article published by Sage in Psychological Science on 01 Jul 2010, available online: http://journals.sagepub.com/doi/abs/10.1177/0956797610372637
Uncontrolled Keywords : Optimal experimental design, Bayesian decision theory, Probability gain, Hypothesis testing, Computer simulation
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 26 Apr 2017 13:38
Last Modified : 31 Oct 2017 19:19
URI: http://epubs.surrey.ac.uk/id/eprint/814038

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