An associative learning account of how infants learn about causal action in animates and inanimates: A critical re-examination of 4 classic studies

Considerable research shows that causal perception emerges between 6 and 10 months of age. Yet, because this research tends to use artificial stimuli, it is unanswered how or by what mechanisms of change human infants learn about the causal properties of real-world categories such as animate entities and inanimate objects. One answer to this question is that this knowledge is innate (i.e., unlearned, evolutionarily ancient, and possibly present at birth) and underpinned by core knowledge and core cognition. An alternative perspective that is tested here through computer simulations is that infants acquire this knowledge via domain-general associative learning. This paper demonstrates that associative learning alone—as instantiated in an artificial neural network—is sufficient to explain the data presented in four classic infancy studies: Spelke, Phillips, and Woodward (1995), Saxe, Tenenbaum, and Carey (2005), Saxe, Tzelnic, and Carey (2007), and Markson and Spelke (2006). This work not only advances theoretical perspectives within developmental psychology but also has implications for the design of artificial intelligence systems inspired by human cognitive development.