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A Computational Role for Top-Down Modulation from Frontal Cortex in Infancy. | LitMetric

AI Article Synopsis

  • Recent research indicates that full-term infants actively use top-down sensory predictions, which are altered in premature infants.
  • An associative learning model reveals that both full-term and preterm infants show similar responses in sensory and frontal brain areas, but preterm infants exhibit weaker prediction error tracking in sensory regions.
  • These findings suggest that the frontal lobe's top-down signals are crucial for conveying information about prediction errors during probabilistic learning in infants.

Article Abstract

Recent findings have shown that full-term infants engage in top-down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top-down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes. However, preterm infants differ from their full-term peers in weaker tracking of prediction error in sensory regions. We infer that top-down signals from the frontal lobe to the sensory regions carry information about prediction error. Using computational learning models and comparing neuroimaging results from full-term and preterm infants, we have uncovered the computational content of top-down signals in young infants when they are engaged in a probabilistic associative learning.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294582PMC
http://dx.doi.org/10.1162/jocn_a_01497DOI Listing

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