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Area of Science:

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Computer Vision

Background:

  • Number sense, or estimating quantities without counting, is vital for humans and animals.
  • Artificial neural networks (ANNs) show promise as models for number sense emergence.
  • Previous ANN studies used simplified stimuli, limiting real-world applicability.

Purpose of the Study:

  • To investigate if ANNs trained for object recognition can perceive approximate numerosity in complex, naturalistic scenes.
  • To determine if this numerosity information is abstract and separable from object properties.
  • To explore the role of the ventral visual pathway in abstract numerosity representation.

Main Methods:

  • Utilized novel, synthetically generated photorealistic stimuli with diverse objects and scenes.
  • Trained deep convolutional neural networks (CNNs) for object recognition.
  • Analyzed information encoded in later convolutional layers and compared with untrained networks.

Main Results:

  • Trained CNNs encoded approximate numerosity information abstractly across various objects and scenes.
  • Numerosity information was linearly decodable from distributed activity in later convolutional layers.
  • Untrained networks primarily captured low-level features, failing to represent abstract numerosity.

Conclusions:

  • Complex, naturalistic stimuli are crucial for studying number sense in biological and artificial systems.
  • Trained deep CNNs offer a viable model for abstract numerosity perception.
  • The brain's ventral visual pathway may contribute significantly to abstract numerosity representation.