In the mobile conjugate reinforcement 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 the frequency of movement of the connected leg. This behavior is sometimes interpreted as an indication that infants experience the efficacy of causal control. However, some researchers have argued that an underlying causal model is not necessary and that a simple reinforcement model that favors mobile movements can explain this behavioral pattern. Interestingly, after the mobile is disconnected from the leg, some 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 alone. 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 discovering the underlying cause-effect relationship on the fly without the need to specify either the cause or the effect in advance. We also propose an active-learning mechanism based on expectation violation, that can be combined with the proposed causal model and a number of alternative models, including a naïve reinforcement 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. SUMMARY: A causality-driven model is proposed that successfully simulates infant behavior in the mobile paradigm. The causal model actively discovers the underlying causal mechanism, while alternative models fail under specific simulated conditions. An active-learning mechanism based on expectation violation is introduced that unifies causal learning and hypothesis-testing within a single, coherent framework. We show that this mechanism successfully models the extinction burst and demonstrates robustness across different experimental settings.
Xu et al. (Wed,) studied this question.