Learning Process of Gaze Following: Computational Modeling Based on Reinforcement Learning.

ABSTRACT: Many studies have explored factors which influence gaze-following behavior of young infants. However, the results of empirical studies were inconsistent, and the mechanism underlying the contextual modulation of gaze following remains unclear. In order to provide valuable insight into the mechanisms underlying gaze following, we conducted computational modeling using Q-learning algorithm and simulated the learning process of infant gaze following to suggest a feasible model. In Experiment 1, we simulated how communicative cues and infant internal states affect the learning process of gaze following. The simulation indicated that the model in which communicative cues enhance infant internal states is the most feasible to explain the infant learning process. In Experiment 2, we simulated how

SUBMITTER: Ishikawa M 

PROVIDER: S-EPMC7063100 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature