Xia Xu
University of Frankfurt
In the so-called mobile paradigm, an infant’s leg is connected to a mobile via a string, allowing the infant to move the mobile via moving their leg. Over a few minutes, infants exhibit an increase in movement frequency of the connected leg. This behaviour is interpreted as an indication of infants experiencing efficacy of causal control. However, some researchers have argued that an underlying causal model is not necessary and that a simple reinforcement learning model that habitually favours movements can explain this behavioural pattern. Interestingly, after the mobile is disconnected from the leg, infants transiently show an even higher frequency of movement, a phenomenon known as the extinction burst, that is hard to reconcile with a simple reinforcement learning model. In this study, we propose different computational models and study to what extent they are capable of capturing infants’ behavior. In particular, we construct an active-learning causal model that is capable of capturing the underlying cause-effect relationship without the need to specify either the cause or the effect in advance. We also propose an active-learning expectation violation-based mechanism, that can be combined with the proposed causal model and a number of alternative models, including a naive reinforcement learning model, to give rise to an extinction burst. Overall, our work sheds light on possible learning mechanisms giving rise to infant’s developing understanding of cause and effect relationships.
DFG SPP "The Active Self" 