A generalized reinforcement learning based deep neural network agent model for diverse cognitive constructs.

ABSTRACT: Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modeled the aforementioned cognitive tasks and show how indiv

SUBMITTER: Nair SS 

PROVIDER: S-EPMC10097685 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature