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

Zhou, Xiang; Wong-Lin, KongFatt; Holmes, Philip

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

Authors

Zhou, Xiang

Wong-Lin, KongFatt

Holmes, Philip

Description

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.
32 pages, 9 figures, 3 tables, accepted for publication in Neural Computation