Name
Title | ||
|---|---|---|
Time-varying perturbations can distinguish among integrate-to-threshold models for perceptual decision making in reaction time tasks. |
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1162/neco.2009.07-08-817 | Neural Computation |
Keywords | Field | DocType |
decision time,temporal integration mechanism,drift-diffusion model,decision making,high signal-to-noise ratio,perceptual decision,standard deviation,reaction time task,first passage time,integrate-to-threshold model,unstable ornstein-uhlenbeck,time-varying drift rate,pulse perturbation,computational modeling,neural integrator,behavioral experiment,time-varying perturbation,reaction time,signal to noise ratio,prescriptions,ornstein uhlenbeck process,computer simulation,threshold model,linear models,stochastic differential equation,linear model,perception,nonlinear dynamics,computer model | Mathematical optimization,Nonlinear system,Linear model,Integrator,Algorithm,Models of neural computation,Stochastic differential equation,Stochastic modelling,Artificial intelligence,Artificial neural network,Prior probability,Mathematics | Journal |
Volume | Issue | ISSN |
21 | 8 | 0899-7667 |
Citations | PageRank | References |
3 | 0.50 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Zhou | 1 | 15 | 7.60 |
| KongFatt Wong-Lin | 2 | 46 | 11.52 |
| Philip J. Holmes | 3 | 194 | 82.66 |