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Modeling naturalistic face processing in humans with deep convolutional neural networks.

Guo Jiahui1, Ma Feilong1, Matteo Visconti di Oleggio Castello2

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Summary
This summary is machine-generated.

Deep convolutional neural networks (DCNNs) capture categorical face attributes but struggle with individuation. Our study used dynamic faces to compare DCNNs, behavior, and brain activity, finding DCNNs better match cognitive and neural data in earlier layers.

Keywords:
artificial neural networkdeep neural networkface identificationhyperalignmentnaturalistic stimuli

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Area of Science:

  • Neuroscience
  • Computer Science
  • Cognitive Science

Background:

  • Deep convolutional neural networks (DCNNs) show high performance in face identification.
  • Understanding how DCNN internal representations map to human cognition and brain activity is limited.
  • Previous research primarily used static face images, neglecting dynamic, naturalistic processing.

Purpose of the Study:

  • To investigate the relationship between DCNNs, human behavior, and brain activity using naturalistic dynamic face stimuli.
  • To compare representational geometries across DCNNs, behavioral tasks, and neural data.
  • To identify which DCNN layers best capture human face processing.

Main Methods:

  • Developed the largest naturalistic dynamic face stimulus set for human neuroimaging research (700+ video clips).
  • Compared representational geometries from DCNNs, a behavioral arrangement task, and brain responses in face-selective areas.
  • Analyzed consistency of representational geometries across DCNN architectures, behavioral raters, and individual brains.

Main Results:

  • Representational geometries were consistent within DCNNs, across behavioral raters, and across brains.
  • Late-intermediate DCNN layers showed stronger correlations with cognitive and neural geometries than late, fully connected layers.
  • DCNNs successfully matched cognitive representational geometries for categorical attributes and correlated with neural geometries.

Conclusions:

  • Current DCNNs effectively model cognitive and neural processes for categorical face attributes.
  • DCNNs are less accurate in capturing individuation and dynamic features of face perception.
  • Dynamic, naturalistic stimuli are crucial for understanding the complexities of face processing in AI and the brain.