Identifying the factors governing internal state switches during nonstationary sensory decision-making.

ABSTRACT: Traditional models of perceptual decision-making fail to capture dynamic strategy switching in non-stationary environments, and the factors governing these switches remain unknown. To address this gap, we developed an advanced internal state model with input-driven transitions and observations. Our approach employs a hidden Markov model (HMM) coupled with two sets of per-state generalized linear models (GLMs): a Bernoulli GLM for state- and stimulus-dependent choices, and a multinomial GLM for input-dependent transitions between states. We applied our model to a decision-making task in a non-stationary environment, analyzing hundreds of thousands of trials from a cohort of mice, and found that their behavior can be accurately described by a four-state model. This model identified two engag

SUBMITTER: Mohammadi Z 

PROVIDER: S-EPMC12749821 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature