Current Challenges and Advances in Computational and Artificial Agent Modeling for the Simulation of Affective Social Learning and Regulation of Motivated Behaviors

Abstract

Psychological science aims at understanding the development and interplay of hidden psychological mechanisms (cognitive, affective, and social) and their causal role in observable behaviors, both in adaptive and maladaptive contexts. It is thus relevant, though highly challenging, to develop computational models and artificial agents derived from psychological theories allowing investigators to explore and test hypotheses through simulations. This chapter reviews and discusses current modeling challenges and advances that are relevant to the understanding and simulation of affective social learning and the development of adaptive and motivated behaviors. The hope is that the chapter will encourage dialogue and the sharing of perspectives with developmental and clinical psychologists. The chapter emphasizes the importance of modeling the complex integration of multiple interacting mechanisms, including processes analogous to the imagination and subjective experience, in order to understand the development of appraisal, emotions, emotion regulation, and their roles in adaptive and maladaptive behaviors.