Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making.

ABSTRACT: Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ-softmax policy). However, this assumption is not guaranteed to hold - for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically infer the levels of noise in choice behavior, under a model assumption that agents can transition betwee

SUBMITTER: Li JJ 

PROVIDER: S-EPMC10849494 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature