Open AccessMice exhibit stochastic and efficient action switching during probabilistic decision making
Author(s) -
Celia Beron,
Shay Q Neufeld,
Scott W. Linderman,
Bernardo L. Sabatini
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2113961119
Subject(s) - action selection , task (project management) , action (physics) , set (abstract data type) , probabilistic logic , inference , computer science , markov decision process , artificial intelligence , reinforcement learning , representation (politics) , bayesian inference , machine learning , bayesian probability , markov process , mathematics , psychology , statistics , perception , neuroscience , physics , management , quantum mechanics , economics , politics , political science , law , programming language
Significance To obtain rewards in changing and uncertain environments, animals must adapt their behavior. We found that mouse choice and trial-to-trial switching behavior in a dynamic and probabilistic two-choice task could be modeled by equivalent theoretical, algorithmic, and descriptive models. These models capture components of evidence accumulation, choice history bias, and stochasticity in mouse behavior. Furthermore, they reveal that mice adapt their behavior in different environmental contexts by modulating their level of stickiness to their previous choice. Despite deviating from the behavior of a theoretically ideal observer, the empirical models achieve comparable levels of near-maximal reward. These results make predictions to guide interrogation of the neural mechanisms underlying flexible decision-making strategies.