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

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.