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

Examining predictive coding accounts of typical and autistic neurocognitive development

Predictive coding has emerged as a prominent theoretical framework for understanding perception and its neural underpinnings. There has been a recent surge of interest in the predictive coding framework across the mind sciences. However, comparatively little of the research in this field has investigated the neural underpinnings of predictive coding in young neurotypical and autistic children. This paper provides an overview of predictive coding accounts of typical and autistic neurocognitive development and includes a review of the current electrophysiological evidence supporting these accounts. Based on the current evidence, it is clear that more research in pediatrics is needed to evaluate predictive coding accounts of neurocognitive development fully. If supported, these accounts could have wide-ranging practical implications for pedagogy, parenting, artificial intelligence, and clinical approaches to helping autistic children manage the barrage of everyday sensory information.

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures

Within predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning).

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

Bayesian models allow us to investigate children's belief revision alongside physiological states, such as "surprise". Recent work finds that pupil dilation (or the "pupillary surprise response") following expectancy violations is predictive of belief revision. How can probabilistic models inform the interpretations of "surprise"? Shannon Information considers the likelihood of an observed event, given prior beliefs, and suggests stronger surprise occurs following unlikely events. In contrast, Kullback-Leibler divergence considers the dissimilarity between prior beliefs and updated beliefs following observations-with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of "surprise" to contexts where children are asked to either predict or evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback-Leibler divergence and the children's pupillometric responses only when the children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child's current beliefs and the updated, more accommodating beliefs.

Beyond Infant's Looking: The Neural Basis for Infant Prediction Errors

Contemporary conceptualizations on infant cognitive development focus on predictive processes; the basic idea is that the brain continuously creates predictions about what is expected and that the divergence between predicted and actual perceived data yields a prediction error. This prediction error updates the model from which the predictions are generated and therefore is a basic mechanism for learning and adaptation to the dynamics of the ever-changing environment. In this article, we review the types of available empirical evidence supporting the idea that predictive processes can be found in infancy, especially emphasizing the contribution of electrophysiology as a potential method for testing the similarity of the brain mechanisms for processing prediction errors in infants to those of adults. In infants, as with older children, adolescents, and adults, predictions involve synchronization bursts of middle-central theta reflecting brain activity in the anterior cingulate cortex. We discuss how early in development such brain mechanisms develop and open questions that still remain to be empirically investigated.

Getting a grip on early intention understanding: The role of motor, cognitive, and social factors

This chapter considers various factors that facilitate infants' understanding of other people's intentions. As adults, we view the actions people perform around us as intentional, to achieve a goal, rather than idle movements. For example, when observing another person perform a simple grasping action, such as picking up a slice of pizza, we perceive this action as goal-directed. Due to our understanding of the person's intention, we focus more so on the relation between the person and their goal, rather than the motion involved in the action. Infants develop an understanding of intentional agents and their goals within the first year of life. This chapter reviews multiple factors that are at play in facilitating infants' learning about the intentions of others' actions. We consider this from various perspectives, including the role of active experience, sensitivity to behavioral cues, cognitive factors, and social factors. We first review evidence concerning infants' learning of intentional actions from active experience. We then go on to evaluate how this learning could also come about via comparison processes, statistical learning, and use of behavioral cues such as object labeling and action effects. We also review social factors such as infant-directed actions and triadic engagement within social interactions that emerging evidence suggests are helpful in facilitating infants' understanding of other people's actions. Finally, we consider the extent to which these factors interact with one another in different contexts, as well as implications and future directions.

How an infant's active response to structured experience supports perceptual-cognitive development

Previous research on perceptual and cognitive development has predominantly focused on infants' passive response to experience. For example, if infants are exposed to acoustic patterns in the background while they are engaged in another activity, what are they able to learn? However, recent work in this area has revealed that even very young infants are also capable of active perceptual and cognitive responses to experience. Specifically, recent neuroimaging work showed that infants' perceptual systems predict upcoming sensory events and that learning to predict new events rapidly modulates the responses of their perceptual systems. In addition, there is new evidence that young infants have access to endogenous attention and their prediction and attention are rapidly and robustly modified through learning about patterns in the environment. In this chapter, we present a synthesis of the existing research on the impact of infants' active responses to experience and argue that this active engagement importantly supports infants' perceptual-cognitive development. To this end, we first define what a mechanism of active engagement is and examine how learning, selective attention, and prediction can be considered active mechanisms. Then, we argue that these active mechanisms become engaged in response to higher-order environmental structures, such as temporal, spatial, and relational patterns, and review both behavioral and neural evidence of infants' active responses to these structures or patterns. Finally, we discuss how this active engagement in infancy may give rise to the emergence of specialized perceptual-cognitive systems and highlight directions for future research.

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

For human infants, the first years after birth are a period of intense exploration-getting to understand their own competencies in interaction with a complex physical and social environment. In contemporary neuroscience, the predictive-processing framework has been proposed as a general working principle of the human brain, the optimization of predictions about the consequences of one's own actions, and sensory inputs from the environment. However, the predictive-processing framework has rarely been applied to infancy research. We argue that a predictive-processing framework may provide a unifying perspective on several phenomena of infant development and learning that may seem unrelated at first sight. These phenomena include statistical learning principles, infants' motor and proprioceptive learning, and infants' basic understanding of their physical and social environment. We discuss how a predictive-processing perspective can advance the understanding of infants' early learning processes in theory, research, and application.