Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

Abstract

From early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agents preferences from systematic violations of random sampling. We investigated how young children build and update models of an agents sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.\n\nWe formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to build predictive models of others actions. We measured pupillary responses as a behavioral marker of prediction errors (i.e., the perceived mismatch between what ones model of an agent predicts and what the agent actually does), as described in the predictive processing framework. Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to generate predictive models of agents and their actions.\n\nThese findings shed light on the mechanisms behind toddlers inferences about agent-caused events. To our knowledge, this is the first study in which young childrens pupillary responses are used as markers of prediction errors, and explained by a computational model based on the causal Bayesian network formalization of predictive processing. We argue that the predictive processing framework provides a promising explanation of the way in which young children process other persons actions.\n\nHighlightsO_LIWe present three formalized hypotheses on how young children generate predictive models of others sampling actions.\nC_LIO_LIWe measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.\nC_LIO_LIResults showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others actions.\nC_LIO_LIA computational model based on the causal Bayesian network formalization of predictive processing can explain this process.\nC_LI