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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Zhou, Xiang, Wong-Lin, KongFatt and Holmes, Philip (2009) Time-varying perturbations can distinguish among integrate-to-threshold models for perceptualdecision making in reaction time tasks. Neural Computation, 21 (8). pp. 2336-2362. [Journal article]

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URL: http://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.07-08-817?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed

DOI: 10.1162/neco.2009.07-08-817

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.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21351
Deposited By:Dr Kongfatt Wong-Lin
Deposited On:09 Mar 2012 14:56
Last Modified:09 Mar 2012 14:56

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