RT Journal Article
SR Electronic
T1 Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.19.496747
DO 10.1101/2022.06.19.496747
A1 Alexander Fengler
A1 Krishn Bera
A1 Mads L. Pedersen
A1 Michael J. Frank
YR 2022
UL http://biorxiv.org/content/early/2022/06/21/2022.06.19.496747.abstract
AB Computational modeling has become an central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision making intended to capture processes jointly giving rise to reaction time distributions and choice data in n-alternative paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of sequential sampling models. In addition, recent work has motivated the combination of SSMs with reinforcement learning (RL) models, which had historically been considered in separate literatures. Here we provide a significant addition to the widely used HDDM python toolbox and include a tutorial for how users can easily fit and assess a (user extensible) wide variety of SSMs, and how they can be combined with RL models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.Competing Interest StatementThe authors have declared no competing interest.