Evolving choice hysteresis in reinforcement learning: Comparing the adaptive value of positivity bias and gradual perseveration
成果类型：
Article
署名作者：
Hoxha, Isabelle; Sperber, Leo; Palminteri, Stefano
署名单位：
Universite PSL; Ecole Normale Superieure (ENS); Institut National de la Sante et de la Recherche Medicale (Inserm)
刊物名称：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN：
0027-11435
DOI：
10.1073/pnas.2422144122
发表日期：
2025-09-09
关键词：
摘要：
The tendency to repeat past choices more often than expected from the history of outcomes has been repeatedly empirically observed in reinforcement learning experiments. It can be explained by at least two computational processes: asymmetric update and (gradual) choice perseveration. A recent meta-analysis showed that both mechanisms are detectable in human reinforcement learning. However, while their descriptive value seems to be well established, they have not been compared regarding their possible adaptive value. In this study, we address this gap by simulating reinforcement learning agents in a variety of environments with a variant of an evolutionary algorithm. Our results show that positivity bias (in the form of asymmetric update) is evolutionary stable in many situations, while the emergence of gradual perseveration is less systematic and robust. Overall, our results illustrate that biases can be adaptive and selected by evolution, in an environment-specific manner.