Tracking the contribution of inductive bias to individualised internal models.

ABSTRACT: Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the c

SUBMITTER: Torok B 

PROVIDER: S-EPMC9255757 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature