### Abstract

Language | English |
---|---|

Pages | 2336-2362 |

Journal | Neural Computation |

Volume | 21 |

Issue number | 8 |

DOIs | |

Publication status | Published - 21 Aug 2009 |

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*Neural Computation*,

*21*(8), 2336-2362. https://doi.org/10.1162/neco.2009.07-08-817

}

*Neural Computation*, vol. 21, no. 8, pp. 2336-2362. https://doi.org/10.1162/neco.2009.07-08-817

**Time-varying perturbations can distinguish among integrate-to-threshold models for perceptualdecision making in reaction time tasks.** / Zhou, Xiang; Wong-Lin, KongFatt; Holmes, Philip.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Time-varying perturbations can distinguish among integrate-to-threshold models for perceptualdecision making in reaction time tasks

AU - Zhou, Xiang

AU - Wong-Lin, KongFatt

AU - Holmes, Philip

PY - 2009/8/21

Y1 - 2009/8/21

N2 - 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.

AB - 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.

U2 - 10.1162/neco.2009.07-08-817

DO - 10.1162/neco.2009.07-08-817

M3 - Article

VL - 21

SP - 2336

EP - 2362

JO - Neural Computation

T2 - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 8

ER -