A computational role for top–down modulation from frontal cortex in infancy

Sagi Jaffe-Dax, Alex M. Boldin, Nathaniel D. Daw, Lauren L. Emberson

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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 fullterm 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.

Original languageEnglish (US)
Pages (from-to)508-514
Number of pages7
JournalJournal of cognitive neuroscience
Issue number3
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience


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