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Young children integrate current observations, priors and agent information to predict others' actions

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Kayhan,  Ezgi
University of Potsdam, Germany;
Max Planck Research Group Early Social Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands;

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Citation

Kayhan, E., Heil, L., Kwisthout, J., van Rooij, I., Hunnius, S., & Bekkering, H. (2019). Young children integrate current observations, priors and agent information to predict others' actions. PLoS One, 14(5): e0200976. doi:10.1371/journal.pone.0200976.


Cite as: https://hdl.handle.net/21.11116/0000-0003-B4CC-C
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 agent’s preferences from systematic violations of random sampling. We investigated whether and how young children infer an agent’s sampling biases. Moreover, we examined whether pupil data of toddlers follow the predictions of a computational model based on the causal Bayesian network formalization of predictive processing. We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to predict 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). Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to make predictions about 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, which were qualitatively compared to the predictions by a computational model based on the causal Bayesian network formalization of predictive processing.