The Library
Fast and accurate learning when making discrete numerical estimates
Tools
Tools
Tools
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
|
PDF
WRAP_journal.pcbi.1004859.PDF - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2108Kb) | Preview |
Official URL: http://dx.doi.org/10.1371/journal.pcbi.1004859
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 > 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: |
|
||||||||
| Date of first compliant deposit: | 20 April 2016 | ||||||||
| 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 | ||||||||
| Funder: | Economic and Social Research Council (Great Britain) (ESRC) | ||||||||
| Grant number: | ES/K004948/1 (ESRC) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
