Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience

In recent years, trends towards studying object-based games have gained momentum in the fields of artificial intelligence, cognitive science, psychology, and neuroscience. In artificial intelligence, interactive physical games are now a common testbed for reinforcement learning (François-Lavet et al., 2018; Leike et al., 2017; Mnih et al., 2013; Sutton and Barto, 2018) and object representations are of particular interest for sample efficient and generalizable AI (Battaglia et al., 2018; Greff et al., 2020; van Steenkiste et al., 2019). In cognitive science and psychology, object-based games are used to study a variety of cognitive capacities, such as planning, intuitive physics, and intuitive psychology (Chabris, 2017; Ullman et al., 2017). Developmental psychologists also use object-based visual stimuli to probe questions about object-oriented reasoning in infants and young animals (Spelke and Kinzler, 2007; Wood et al., 2020). In neuroscience, object-based computer games have recently been used to study decision-making and physical reasoning in both human and non-human primates (Fischer et al., 2016; McDonald et al., 2019; Rajalingham et al., 2021; Yoo et al., 2020). Furthermore, a growing number of researchers are studying tasks using a combination of approaches from these fields.