A neural computation model for decision-making times

Yuri Bakhtin, Joshua Correll

Research output: Contribution to journalArticle

Abstract

We introduce two new models for decision-making times for a two-choice decision task with no a priori bias. One of the models is the mean-field Curie-Weiss model of neural computation, and the other is based on dynamics near an unstable equilibrium under a small noise perturbation. As in the existing literature, we interpret exit times as reaction times and show that our models lead to a specific shape of the exit time distributions in the vanishing noise limit. We test the distribution shape against experimental data and show that for almost 90% of the participants, reaction times are described well by the model. Among the features of our model are: the dependence of the exit distribution only on two parameters, the elegance of rigorous mathematical analysis, and the microscopic nature of the noise.

Original languageEnglish (US)
Pages (from-to)333-340
Number of pages8
JournalJournal of Mathematical Psychology
Volume56
Issue number5
DOIs
StatePublished - Oct 2012

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Decision Making
Decision making
Noise
Exit Time
Reaction Time
Model
Mathematical Analysis
Mean Field
Two Parameters
Unstable
Experimental Data
Perturbation

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

Cite this

A neural computation model for decision-making times. / Bakhtin, Yuri; Correll, Joshua.

In: Journal of Mathematical Psychology, Vol. 56, No. 5, 10.2012, p. 333-340.

Research output: Contribution to journalArticle

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