Kayhan, E. | bioRxiv Science

bioRxiv2021

Visual category representations in the infant brain

Visual categorization is a human core cognitive capacity 1,2 that depends on the development of visual category representations in the infant brain 3-7. However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown 8. Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory towards adult maturity in the key characteristics of temporal dynamics 2,9, representational format 10-12, and spectral properties 13,14. Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences, infants and adults already partly share visual category representations. The format of infants representations is visual features of low to intermediate complexity, whereas adults representations also encode high complexity features. Theta band activity contributes to visual category representations in infants, and these representations are shifted to the alpha/beta band in adults. Together, we reveal the developmental neural basis of visual categorization in humans, show how information transmission channels change in development, and demonstrate the power of advanced multivariate analysis techniques in infant EEG research for theory building in developmental cognitive science.

neuroscience↗

bioRxiv2018

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

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 agents preferences from systematic violations of random sampling. We investigated how young children build and update models of an agents sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.\n\nWe 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 ones 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.\n\nThese findings shed light on the mechanisms behind toddlers inferences about agent-caused events. To our knowledge, this is the first study in which young childrens 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.\n\nHighlightsO_LIWe present three formalized hypotheses on how young children generate predictive models of others sampling actions.\nC_LIO_LIWe measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.\nC_LIO_LIResults showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others actions.\nC_LIO_LIA computational model based on the causal Bayesian network formalization of predictive processing can explain this process.\nC_LI

neuroscience↗