Young children integrate current observations, priors and agent information to predict 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 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.

Making Sense of the World: Infant Learning From a Predictive Processing Perspective

“…However, research on PP in the infant brain is in its infancy. Although there is accumulating evidence that the adult brain might be working on the basis of the principles of the PP framework (e.g., Egner, Monti, & Summerfield, 2010; Wacongne et al, 2011), our knowledge on the predictive nature of the infant brain is sparse (but for recent research on PP in infants, see Emberson et al, 2015; Kayhan, Heil, et al, 2019; Kayhan, Hunnius, et al, 2019; Kayhan, Meyer, et al, 2019; Kouider et al, 2015). These studies provided initial evidence that the infant brain is already capable of forming predictions on the basis of prior knowledge and physiologically responds to violations of these predictions.…”

“…The predictive-processing (PP) perspective originated from the basic computational problem that successful navigation in the environment relies on the organism’s ability to optimize predictions about how one’s own behavior will affect proprioceptive experiences (Helmholtz, 1867) and how social and physical entities in the outer world behave (e.g., Clark, 2013; Schubotz, 2015). Although the basic idea of the PP framework very closely resembles the challenges described for learning processes in young infants, researchers have only recently begun to investigate PP mechanisms in infancy (Emberson, Richards, & Aslin, 2015; Kayhan, Heil, et al, 2019; Kayhan, Hunnius, O’Reilly, & Bekkering, 2019; Kayhan, Meyer, O’Reily, Hunnius, & Bekkering, 2019; Kouider et al, 2015). The PP account has been related to several phenomena in developmental psychology, including mentalizing about own and others’ internal bodily and mental states (Fotopoulou & Tsakiris, 2017; Palmer, Seth, & Hohwy, 2015), early language acquisition (Trainor, 2012), and autism spectrum disorder (e.g., Bolis & Schilbach, 2018; Lawson, Rees, & Friston, 2014; Pellicano & Burr, 2012; Sinha et al, 2014; Van de Cruys et al, 2014).…”

Differentiating Bayesian model updating and model revision based on their prediction error dynamics

“…This suggests that pupil dilation and hence prediction error was on average decreasing in the updating phase and increasing in the revision phase. These results are unexpected, because, according to existing literature, prediction error is decreasing as a result of learning (Friston et al, 2016; Smith et al, 2020; Fitzgerald, 2015; Clark, 2016; Zenon, 2019) and so should pupil dilation (Kayhan, 2019). However, a look at the individual level pupil data (see supplementary Fig 2) reveals large variability in the sign of the σ parameter for both phases, potentially suggesting individual differences in the size of pupil dilation over time.…”

“…Alternatively, future studies might investigate empirically the interpretation of the current results: that participants construct multiple models at the beginning and later choose among them and update them. Lastly, our study is one of the few that studied how the change in prediction error, as captured by pupil dilation, over time (for a few exceptions see Kayhan, 2019; Koenig et al, 2017). Therefore, little is known about how pupil dynamics and hence prediction errors change over extended periods of time and whether individual differences exist in this process.…”

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).…”