Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.

ABSTRACT: Machine learning methods have recently begun to be used for fitting and comparing cognitive models, yet they have mainly focused on methods for dealing with models that lack tractable likelihoods. Evaluating how these approaches compare to traditional likelihood-based methods is critical to understanding the utility of machine learning for modeling and determining what role it might play in the development of new models and theories. In this paper, we systematically benchmark neural network approaches against likelihood-based approaches to model fitting and comparison, focusing on intertemporal choice modeling as an illustrative application. By applying each approach to intertemporal choice data from participants with substance use problems, we show that there is convergence between neural

SUBMITTER: Kvam PD 

PROVIDER: S-EPMC12339600 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature