Time-varying perturbations can distinguish among integrate-to-threshold models for perceptualdecision making in reaction time tasks

Xiang Zhou, KongFatt Wong-Lin, Philip Holmes

    Research output: Contribution to journalArticle

    13 Citations (Scopus)
    6 Downloads (Pure)

    Abstract

    Several integrate-to-threshold models with differing temporal integration mechanisms have been proposed to describe the accumulation of sensory evidence to a prescribed level prior to motor response in perceptual decision-making tasks. An experiment and simulation studies have shown that the introduction of time-varying perturbations during integration may distinguish among some of these models. Here, we present computer simulations and mathematical proofs that provide more rigorous comparisons among one-dimensional stochastic differential equation models. Using two perturbation protocols and focusing on the resulting changes in the means and standard deviations of decision times, we show that for high signal-to-noise ratios, drift-diffusion models with constant and time-varying drift rates can be distinguished from Ornstein-Uhlenbeck processes, but not necessarily from each other. The protocols can also distinguish stable from unstable Ornstein-Uhlenbeck processes, and we show that a nonlinear integrator can be distinguished from these linear models by changes in standard deviations. The protocols can be implemented in behavioral experiments.
    Original languageEnglish
    Pages (from-to)2336-2362
    JournalNeural Computation
    Volume21
    Issue number8
    DOIs
    Publication statusPublished - 21 Aug 2009

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