AbstractFrom 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 agent’s preferences from systematic violations of random sampling. We investigated how young children build and update models of an agent’s sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.We 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 one’s 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.These findings shed light on the mechanisms behind toddlers’ inferences about agent-caused events. To our knowledge, this is the first study in which young children’s 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.HighlightsWe present three formalized hypotheses on how young children generate predictive models of others’ sampling actions.We measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.Results showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others’ actions.A computational model based on the causal Bayesian network formalization of predictive processing can explain this process.
Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks
