.png){kind=icon}
.png){kind=icon}
Return
Mice adaptively generate choice variability in a deterministic task
.png){kind=icon}
OA
AI
DOI：10.1038/s42003-020-0759-x.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Can decisions be made solely by chance? Can variability be intrinsic to the decision-maker or is it inherited from environmental conditions? To investigate these questions, we designed a deterministic setting in which mice are rewarded for non-repetitive choice sequences, and modeled the experiment using reinforcement learning. We found that mice progressively increased their choice variability. Although an optimal strategy based on sequences learning was theoretically possible and would be more rewarding, animals used a pseudo-random selection which ensures high success rate. This was not the case if the animal is exposed to a uniform probabilistic reward delivery. We also show that mice were blind to changes in the temporal structure of reward delivery once they learned to choose at random. Overall, our results demonstrate that a decision-making process can self-generate variability and randomness, even when the rules governing reward delivery are neither stochastic nor volatile. Marwen Belkaid et al. develop a deterministic task to study variability in decision-making processes. They show that mice embrace behavioral variability as an effective decision-making strategy, highlighting the brain's ability to generate random decisions independently from the environment statistics.
Keywords:
DECISION-MAKING
PREFRONTAL CORTEX
RECURRENCE PLOTS
UNCERTAINTY
BRAIN
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.1
Papers:
1.0W
Citations:
3.2W