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Individual differences in artificial neural networks capture individual differences in human behavior.

Herrick Fung1, N Apurva Ratan Murty1, Dobromir Rahnev1

  • 1School of Psychology, Georgia Institute of Technology.

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This summary is machine-generated.

Artificial neural networks (ANNs) show individual differences in performance, mirroring human behavior. This suggests ANNs can model human variability in perception and cognition.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Human behavior exhibits significant individual variability.
  • Artificial neural networks (ANNs) are often assumed to lack individual differences.
  • Understanding sources of variability is key in cognitive modeling.

Purpose of the Study:

  • To investigate if individual artificial neural network (ANN) instances show behavioral differences.
  • To determine if ANN individual differences mimic human behavioral variability.
  • To explore the potential of ANNs as models for human variability.

Main Methods:

  • Trained and tested 60 ANN instances across three architectures on digit and object recognition tasks.
  • Collected behavioral data (accuracy, confidence, response time) from ANNs and 60 humans.
  • Quantified the mapping strength between ANN and human individual differences.

Main Results:

  • Multiple ANN instances displayed substantial individual differences in accuracy, confidence, and response time.
  • ANN individual differences consistently mapped onto human individual differences across tasks and metrics.
  • Mapping strength between humans and ANNs approached human-to-human benchmarks.

Conclusions:

  • ANNs, even with identical architectures, exhibit individual differences mirroring human variability.
  • ANN ensembles can serve as computational proxies to study mechanisms of human behavioral variation.
  • This research bridges artificial intelligence and cognitive science by modeling individual differences.