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

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dc.creatorZhou, Xiang
dc.creatorWong-Lin, KongFatt
dc.creatorHolmes, Philip
dc.date2009-01-15
dc.date.accessioned2026-07-07T12:29:38Z
dc.date.available2026-07-07T12:29:38Z
dc.descriptionSeveral 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.
dc.description32 pages, 9 figures, 3 tables, accepted for publication in Neural Computation
dc.identifierhttps://arxiv.org/abs/0901.2173
dc.identifierhttp://arxiv.org/abs/0901.2173
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/215939
dc.subjectNeurons and Cognition
dc.titleTime-varying perturbations can distinguish among integrate-to-threshold models for perceptual decision-making in reaction time tasks
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