A critical period for developing face recognition

Jinge Wang, Runnan Cao, Puneeth N. Chakravarthula, Xin Li, Shuo Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Face learning has important critical periods during development. However, the computational mechanisms of critical periods remain unknown. Here, we conducted a series of in silico experiments and showed that, similar to humans, deep artificial neural networks exhibited critical periods during which a stimulus deficit could impair the development of face learning. Face learning could only be restored when providing information within the critical period, whereas, outside of the critical period, the model could not incorporate new information anymore. We further provided a full computational account by learning rate and demonstrated an alternative approach by knowledge distillation and attention transfer to partially recover the model outside of the critical period. We finally showed that model performance and recovery were associated with identity-selective units and the correspondence with the primate visual systems. Our present study not only reveals computational mechanisms underlying face learning but also points to strategies to restore impaired face learning.

Original languageEnglish
Article number100895
JournalPatterns
Volume5
Issue number2
DOIs
StatePublished - Feb 9 2024

Keywords

  • DSML2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem
  • attention transfer
  • autism spectrum disorder
  • critical period
  • deep neural network
  • eyes
  • faces
  • facial landmarks
  • knowledge distillation
  • learning
  • mouth

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