Reinforcement Learning Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Extension to Learning Task Events.

ABSTRACT: Learning is a crucial basis for biological systems to adapt to environments. Environments include various states or episodes, and episode-dependent learning is essential in adaptation to such complex situations. Here, we developed a model for learning a two-target search task used in primate physiological experiments. In the task, the agent is required to gaze one of the four presented light spots. Two neighboring spots are served as the correct target alternately, and the correct target pair is switched after a certain number of consecutive successes. In order for the agent to obtain rewards with a high probability, it is necessary to make decisions based on the actions and results of the previous two trials. Our previous work achieved this by using a dynamic state space. However, to lear

SUBMITTER: Sakamoto K 

PROVIDER: S-EPMC9201426 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature