Instance-based generalization for human judgments about uncertainty
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- dc.contributor.author Schustek, Philipp
- dc.contributor.author Moreno Bote, Rubén
- dc.date.accessioned 2019-07-04T10:36:28Z
- dc.date.available 2019-07-04T10:36:28Z
- dc.date.issued 2018
- dc.description.abstract While previous studies have shown that human behavior adjusts in response to uncertainty, it is still not well understood how uncertainty is estimated and represented. As probability distributions are high dimensional objects, only constrained families of distributions with a low number of parameters can be specified from finite data. However, it is unknown what the structural assumptions are that the brain uses to estimate them. We introduce a novel paradigm that requires human participants of either sex to explicitly estimate the dispersion of a distribution over future observations. Judgments are based on a very small sample from a centered, normally distributed random variable that was suggested by the framing of the task. This probability density estimation task could optimally be solved by inferring the dispersion parameter of a normal distribution. We find that although behavior closely tracks uncertainty on a trial-by-trial basis and resists an explanation with simple heuristics, it is hardly consistent with parametric inference of a normal distribution. Despite the transparency of the simple generating process, participants estimate a distribution biased towards the observed instances while still strongly generalizing beyond the sample. The inferred internal distributions can be well approximated by a nonparametric mixture of spatially extended basis distributions. Thus, our results suggest that fluctuations have an excessive effect on human uncertainty judgments because of representations that can adapt overly flexibly to the sample. This might be of greater utility in more general conditions in structurally uncertain environments.
- dc.description.sponsorship PS was supported by a FI-AGAUR scholarship of the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (G62978689, agaur.gencat.cat). RM-B is supported by PSI2013-44811-P and FLAGERA-PCIN-2015-162-C02-02 from MINECO (Spain) and Howard Hughes Medical Institute (HHMI), ref 55008742. This work was supported by CERCA Programme / Generalitat de Catalunya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- dc.format.mimetype application/pdf
- dc.identifier.citation Schustek P, Moreno-Bote R. Instance-based generalization for human judgments about uncertainty. PLoS Comput Biol. 2018 Jun 4;14(6):e1006205. DOI: 10.1371/journal.pcbi.1006205
- dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1006205
- dc.identifier.uri http://hdl.handle.net/10230/41942
- dc.language.iso eng
- dc.publisher Public Library of Science (PLoS)
- dc.relation.ispartof PLoS Computational Biology. 2018 Jun 4;14(6):e1006205.
- dc.relation.isreferencedby https://doi.org/10.1371/journal.pcbi.1006205.s001
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2013-44811-P
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PCIN-2015-162-C02-02
- dc.rights © 2018 Schustek, Moreno-Bote. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.title Instance-based generalization for human judgments about uncertainty
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion