TY - JOUR
T1 - A computational role for top–down modulation from frontal cortex in infancy
AU - Jaffe-Dax, Sagi
AU - Boldin, Alex M.
AU - Daw, Nathaniel D.
AU - Emberson, Lauren L.
N1 - Publisher Copyright:
© 2019 Massachusetts Institute of Technology.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85078814750&partnerID=8YFLogxK
U2 - 10.1162/jocn_a_01497
DO - 10.1162/jocn_a_01497
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C2 - 31682568
AN - SCOPUS:85078814750
SN - 0898-929X
VL - 32
SP - 508
EP - 514
JO - Journal of Cognitive Neuroscience
JF - Journal of Cognitive Neuroscience
IS - 3
ER -