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dc.contributor.author
Fernandez Slezak, Diego
dc.contributor.author
Sigman, Mariano
dc.contributor.author
Cecchi, Guillermo Alberto
dc.date.available
2021-07-12T22:22:20Z
dc.date.issued
2018-03-02
dc.identifier.citation
Fernandez Slezak, Diego; Sigman, Mariano; Cecchi, Guillermo Alberto; An entropic barriers diffusion theory of decision-making in multiple alternative tasks; Public Library of Science; Plos Computational Biology; 14; 3; 2-3-2018; 1-14
dc.identifier.issn
1553-734X
dc.identifier.uri
http://hdl.handle.net/11336/135905
dc.description.abstract
We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Public Library of Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
Decision-making
dc.subject
Entropic barriers
dc.subject.classification
Otras Ciencias Naturales y Exactas
dc.subject.classification
Otras Ciencias Naturales y Exactas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
An entropic barriers diffusion theory of decision-making in multiple alternative tasks
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-07-08T16:43:08Z
dc.identifier.eissn
1553-7358
dc.journal.volume
14
dc.journal.number
3
dc.journal.pagination
1-14
dc.journal.pais
Estados Unidos
dc.journal.ciudad
San Francisco
dc.description.fil
Fil: Fernandez Slezak, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
dc.description.fil
Fil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Cecchi, Guillermo Alberto. IBM Research. Thomas J. Watson Research Center; Estados Unidos
dc.journal.title
Plos Computational Biology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1371/journal.pcbi.1005961
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005961
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