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Multiple visual objects are represented differently in the human brain and convolutional neural networks.

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Artificial Intelligence

Background:

  • The primate brain forms object representations that are independent of concurrent objects.
  • Neural responses to object pairs approximate the average of responses to individual objects.

Purpose of the Study:

  • To compare object representation in the human brain and convolutional neural networks (CNNs).
  • To investigate how CNNs process paired objects compared to single objects.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) in humans to measure brain activity in the lateral occipital complex (LO).
  • Analysis of response amplitudes in macaque IT neurons and fMRI voxel patterns in humans.
  • Comparison with five different CNN architectures pretrained for object classification.

Main Results:

  • Human LO exhibits averaging in both single voxel and population responses for paired objects.
  • Higher layers of CNNs showed significant deviations from brain data in unit slope distribution and population averaging.
  • CNNs demonstrate interacting object representations, unlike the averaging observed in the human brain.

Conclusions:

  • Human object representation in LO relies on averaging, creating context-independent representations.
  • CNNs exhibit context-dependent object representations due to interactions between units.
  • The representational differences may limit CNNs' ability to generalize object recognition across different contexts.