Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

Abstract

Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift–diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting.

An Introduction and Tutorial to Fitting (Time-Dependent) Diffusion Models with the R-Package dRiftDM

“…For example, current models such as DMC or SSP need to be implemented manually, and the results typically required by psychological researchers also need to be extracted manually. Nonetheless, they represent major advances in solving DDMs, and PyDDM has already been used in several research articles since its publication (e.g., Asadpour et al, 2024;Koob, Mackenzie, et al, 2023;Zgonnikov et al, 2024).…”