July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
The role of time in human decision-making
Author Affiliations
  • Marjena Popovic
    Graduate Program in Neuroscience, Brandeis University, Waltham, MA
  • Mate Lengyel
    Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, United Kingdom
  • Jozsef Fiser
    Department of Cognitive Science, Central European University, Budapest, Hungary
Journal of Vision July 2013, Vol.13, 305. doi:10.1167/13.9.305
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      Marjena Popovic, Mate Lengyel, Jozsef Fiser; The role of time in human decision-making. Journal of Vision 2013;13(9):305. doi: 10.1167/13.9.305.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The effects of time on human decision-making are well known, yet, the precise mechanisms underlying these effects remain unclear. Under the classic signal processing framework (e.g. integration-to-bound) the passing of time allows for accumulation of evidence, parametric models of probabilistic neural representations (e.g. PPC) hold that time is used for averaging internal noise for a better estimate of firing rates, while non-parametric, sampling-based models posit that time influences the collection of samples from subjective posterior distributions. These models provide different predictions about the nature and temporal evolution of subjects’ errors and the correlation between their error and their subjective uncertainty. We have analytically derived the progression of error and subjective uncertainty in time for the three models under a decision-making scenario, and found characteristic differences in their behavior. Under sampling, after a possible transient decrease depending on the kurtosis of the posterior, the correlation always increases monotonically to an asymptote. Importantly, this increase continues long after the error itself has reached its asymptote. In contrast, both integration-to-bound and PPC models can show increasing or decreasing changes in correlation depending on the posterior’s kurtosis, and when noise corrupts the posterior, this correlation decreases. We conducted a decision-making study in which subjects performed time-limited orientation matching and reported their uncertainty about their decisions, and found that the results confirmed both predictions of the sampling-based model. As these characteristics are not present in parametric and integration-to-bound models, the present results lend strong support to a novel use of time in decision-making: collecting samples from otherwise static internally represented distributions.

Meeting abstract presented at VSS 2013

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