Infinite hidden Markov models can dissect the complexities of learning.

ABSTRACT: Learning the contingencies of a task is difficult. Individuals learn in an idiosyncratic manner, revising their approach multiple times as they explore and adapt. Quantitative characterization of these learning curves requires a model that can capture both new behaviors and slow changes in existing ones. Here we suggest a dynamic infinite hidden semi-Markov model, whose latent states are associated with specific components of behavior. This model can describe new behaviors by introducing new states and capture more modest adaptations through dynamics in existing states. We tested the model by fitting it to behavioral data of >100 mice learning a contrast-detection task. Although animals showed large interindividual differences while learning this task, most mice progressed through three st

SUBMITTER: Bruijns SA 

PROVIDER: S-EPMC12779568 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature