Year: 2025
Type: article
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 stages of task understanding, new behavior often arose at session onset, and early response biases did not predict later ones. We thus provide a new tool for comprehensively capturing behavior during learning.
Source: Nature Neuroscience
Authors: Sebastian A. Bruijns, Petrina Y. P. Lau, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Noam Roth, Karolina Z. Socha, Anne E. Urai, Kcénia Bougrova, Inês C. Laranjeira, Petrina Y. P. Lau, Guido T. Meijer, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Noam Roth, Karolina Z. Socha, Anne E. Urai, Peter Dayan
Institutions: Max Planck Institute for Biological Cybernetics, University of Tübingen, Chinese University of Hong Kong, University College London, Sainsbury Wellcome Centre, University of Minnesota, Princeton University, University of Washington, University of California, Los Angeles, Leiden University, Champalimaud Foundation
Language: English
Topic: Memory and Neural Mechanisms
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
OpenAlex ID: W7117548528