Aslin, R. N. | bioRxiv Science

bioRxiv2020

Temporal dynamics of visual representations in the infant brain

Tools from computational neuroscience have facilitated the investigation of the neural correlates of mental representations. However, access to the representational content of neural activations early in life has remained limited. We asked whether patterns of neural activity elicited by complex visual stimuli (animals, human body) could be decoded from EEG data gathered from 12-15-month-old infants and adult controls. We assessed pairwise classification accuracy at each time-point after stimulus onset, for individual infants and adults. Classification accuracies rose above chance in both groups, within 500 ms. In contrast to adults, neural representations in infants were not linearly separable across visual domains. Representations were similar within, but not across, age groups. These findings suggest a developmental reorganization of visual representations between the second year of life and adulthood and provide a promising proof-of-concept for the feasibility of decoding EEG data within-subject to assess how the infant brain dynamically represents visual objects.

neuroscience↗

bioRxiv2019

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes-optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes-optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6-7-year old children when tasked with localizing a hidden target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6-7-year olds learn task-relevant statistics at a rate on-par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young childrens behavior to a greater extent than that of adults, leading their behavior to look sub-optimal. Our findings have important implications for how we should interpret failures in young childrens ability to carry out sophisticated computations. These failures need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.\n\nResearch HighlightsO_LIYoung children are sensitive to, and can learn, environmental regularities. Can they also utilize such learned regularities in a statistically efficient manner?\nC_LIO_LIWe demonstrate that 6-7-year olds are capable of learning and utilizing regularities in a statistically efficient fashion, and in a manner indistinguishable from adult behavior.\nC_LIO_LIHowever, variables such as task complexity can influence young childrens behavior to a greater extent than that of adults, leading their behavior to look sub-optimal.\nC_LIO_LIThese findings have important implications for how we should interpret failures in young childrens ability to carry out sophisticated computations.\nC_LI

neuroscience↗