Similarity Effect and Optimal Control of Multiple-Choice Decision Making

Moran Furman, Xiao-Jing Wang

Research output: Contribution to journalArticle

Abstract

Decision making with several choice options is central to cognition. To elucidate the neural mechanisms of such decisions, we investigated a recurrent cortical circuit model in which fluctuating spiking neural dynamics underlie trial-by-trial stochastic decisions. The model encodes a continuous analog stimulus feature and is thus applicable to multiple-choice decisions. Importantly, the continuous network captures similarity between alternatives and possible overlaps in their neural representation. Model simulations accounted for behavioral as well as single-unit neurophysiological data from a recent monkey experiment and revealed testable predictions about the patterns of error rate as a function of the similarity between the correct and actual choices. We also found that the similarity and number of options affect speed and accuracy of responses. A mechanism is proposed for flexible control of speed-accuracy tradeoff, based on a simple top-down signal to the decision circuit that may vary nonmonotonically with the number of choice alternatives.

Original languageEnglish (US)
Pages (from-to)1153-1168
Number of pages16
JournalNeuron
Volume60
Issue number6
DOIs
StatePublished - Dec 26 2008

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Cognition
Haplorhini
Decision Making

Keywords

  • MOLNEURO
  • SYSBIO
  • SYSNEURO

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Similarity Effect and Optimal Control of Multiple-Choice Decision Making. / Furman, Moran; Wang, Xiao-Jing.

In: Neuron, Vol. 60, No. 6, 26.12.2008, p. 1153-1168.

Research output: Contribution to journalArticle

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