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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Deep neural networks (DNNs) achieve human-like performance in complex tasks.
  • The inscrutability of DNNs challenges their relevance to human cognition theories.
  • Existing debates polarize views on DNNs' utility for psychological science.

Purpose of the Study:

  • To review research on interpreting DNN internal mechanisms.
  • To challenge the assumption that DNNs are irrelevant to cognitive theories.
  • To demonstrate how DNNs can inform theories of cognition and development.

Main Methods:

  • Literature review of recent studies on DNN interpretability.
  • Analysis of functional similarities between DNN mechanisms and psychological constructs.
  • Argumentation against extreme viewpoints on DNNs in cognitive science.

Main Results:

  • Evidence suggests DNN internal mechanisms can be interpreted functionally.
  • This interpretability counters the notion that DNNs are psychologically irrelevant.
  • DNNs offer a new avenue for understanding human cognition and its development.

Conclusions:

  • The interpretability of DNNs reconciles AI advancements with cognitive science.
  • DNNs can inform and potentially reshape theories of human cognition.
  • A nuanced perspective on DNNs is crucial for advancing interdisciplinary research.