A Two-Stage Diffusion Modeling Approach to the Compelled-Response Task

The issue of how perception and motor planning interact to generate a given choice between actions is a fundamental question in both psychology and neuroscience. Salinas and colleagues have developed a behavioral paradigm, the compelled-response task, where the signal that instructs the subject to make an eye movement is given before the cue that indicates which of two possible target choices is the correct one. When the cue is given rather late, the participant must guess and make an uninformed random choice. Perceptual performance can be tracked as a function of the amount of time during which sensory information is available. In Salinas' accelerated race-to-threshold model, two variables race against each other to a threshold, at which a saccade is initiated. The source of random variability is in the initial state of information buildup across trials. This implies that incorrect decisions are due to the inertia of the racing variables that have, at the start, sampled a constant buildup in the wrong direction. Here we suggest an alternative, non-time-homogeneous two-stage-diffusion model that is able to predict both response time distributions and choice probabilities with a few easy-to-interpret parameters and without assuming crosstrial parameter variability. It is falsifiable already at the level of qualitative features, for example, predicting bimodal reaction time (RT) distributions for particular gap times. It connects the compelled-response paradigm with an approach to decision making that has been uniquely successful in describing both behavioral and neural data in a variety of experimental settings for the last 40 years.

Automated summary

The interaction between perception and motor planning in generating action choices has been studied using the compelled-response task paradigm developed by Salinas and colleagues. The accelerated race-to-threshold model predicts saccade initiation based on two competing variables. An alternative two-stage diffusion model without assuming crosstrial parameter variability has been proposed, successfully predicting response time distributions and choice probabilities.