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

Abstract

Highlights We 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.. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/365361 doi: bioRxiv preprint first posted online Jul. 9, 2018; RUNNING HEAD: PREDICTIVE PROCESSING IN DEVELOPMENT 3 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 agentcaused 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.

Seeing the Error in my “Bayes”: A Quantified Degree of Belief Change Correlates with Children's Pupillary Surprise Responses Following Explicit Predictions

“…Our findings also converge with other related research. Like Kayhan and colleagues [45], we found a relationship between pupil dilation and the Kullback-Leibler divergence. Both this previous work and the current investigation find that the calculated divergence may affect belief revision in regard to the amount of updating needed to adjust current beliefs.…”

“…Both this previous work and the current investigation find that the calculated divergence may affect belief revision in regard to the amount of updating needed to adjust current beliefs. However, there are two key differences between our modeling work and that of Kayhan et al [45] which are important to note. First, the current paper investigates children's pupillary surprise under different contextual conditions.…”

“…Second, in line with the original paper that we draw our model from [11], the current model accounts for individual differences among children's prior beliefs and the processes by which they update. In of Kayhan et al [45], children's behaviors are modeled to all follow the same inferred computational model 5 . In contrast, we formalize the prior beliefs that children may have at the individual level, as informed by their past behavior.…”

“…Similarly, Kayhan et al [45] investigates pupillary surprise and learning with 18month-old infants and 24-month-old toddlers. Here, young children completed a statistical learning task that measured their pupil dilation as they viewed movies where an agent sampled five colored balls from a transparent bin containing multiple balls of two colors.…”

Investigating Predictive Coding in Younger and Older Children Using MEG and a Multi-Feature Auditory Oddball Paradigm

“…Iterative model updating should enable children to meet incoming sensory signals with increasingly accurate priors which should, in turn, result in fewer prediction errors, and hence greater confidence in the model's predictions. Children's priors may become increasingly precise as they gain experience in the world (Kayhan et al, 2019;Köster et al, 2020). As such, the posterior distribution (i.e., the perceptual experience; Hohwy, 2012) would be gradually biased towards the increasingly narrow prior distribution (representing the brain's stored knowledge), and away from the sensory signal distribution (Lucas et al, 2014).…”