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

Abstract

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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 individual differences in motivation for communication affect the learning process. The results showed that low motivation for communication can delay the learning process and decrease the frequency of gaze following. These simulations suggest that communicative cues may enhance infants’ internal states and promote the development of gaze following. Also, initial social motivation may affect the learning process of social behaviors in the long term.

Keywords

• gaze following
• reinforcement learning
• computational modeling
• infant internal state
• communicative cues