@article{160171,
  author = {Sagi Jaffe-Dax and Alex M. Boldin and Nathaniel D. Daw and Lauren L. Emberson},
  title = {A Computational Role for Top{\textendash}Down Modulation from Frontal Cortex in Infancy},
  abstract = { Recent findings have shown that full-term infants engage in top{\textendash}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{\textendash}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{\textendash}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{\textendash}down signals in young infants when they are engaged in a probabilistic associative learning. },
  year = {2020},
  journal = {Journal of Cognitive Neuroscience},
  volume = {32},
  pages = {508-514},
  url = {https://doi.org/10.1162/jocn_a_01497},
  doi = {10.1162/jocn\_a\_01497},
  language = {eng},
}