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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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 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
Pages (from-to)508-514
Number of pages7
JournalJournal of Cognitive Neuroscience
Volume32
Issue number3
DOIs
StatePublished - 2020
Externally publishedYes

Funding

FundersFunder number
National Institute of Child Health and Human DevelopmentR00HD076166
Bill and Melinda Gates Foundation
James S. McDonnell Foundation220020505
Eunice Kennedy Shriver National Institute of Child Health and Human Development

    Fingerprint

    Dive into the research topics of 'A computational role for top–down modulation from frontal cortex in infancy'. Together they form a unique fingerprint.

    Cite this