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Fast and accurate learning when making discrete numerical estimates

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Sanborn, Adam N. and Beierholm, Ulrik R. (2016) Fast and accurate learning when making discrete numerical estimates. PLoS Computational Biology, 12 (4). e1004859. doi:10.1371/journal.pcbi.1004859 ISSN 1553-7358.

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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1004859

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Abstract

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Divisions: Faculty of Science, Engineering and Medicine > Science > Psychology
Library of Congress Subject Headings (LCSH): Estimates, Perception
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Official Date: 12 April 2016
Dates:
DateEvent
12 April 2016Published
9 March 2016Accepted
22 September 2015Submitted
Volume: 12
Number: 4
Article Number: e1004859
DOI: 10.1371/journal.pcbi.1004859
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 20 April 2016
Date of first compliant Open Access: 20 April 2016
Funder: Economic and Social Research Council (Great Britain) (ESRC)
Grant number: ES/K004948/1 (ESRC)

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