An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data.

ABSTRACT: Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible whe

SUBMITTER: Feltgen Q 

PROVIDER: S-EPMC8064018 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature