Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models.

ABSTRACT:

Introduction

Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments.

Methods

These ideas are exemplified on the process of delay discounting (D

SUBMITTER: Thome J 

PROVIDER: S-EPMC9868576 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature