mlsanityData to 7 Oct 2026

Philosophy of Cognitive Science in the Age of Deep Learning

arXiv 2405.04048 ↗First posted 7 May 2024cs.CLcited by 5

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Abstract (arXiv)

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the centre stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 38 of its 80 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

38 of 80 references.

References · 38

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26 more
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Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Language Models as Models of Languagecited by 24
  2. 2024From Frege to chatGPT: Compositionality in language, cognition, and deep neural networkscited by 7
  3. 2024Multiple Realizability and the Rise of Deep Learningcited by 2
  4. 2025Bridging Minds and Machines: Toward an Integration of AI and Cognitive Sciencecited by 2
  5. 2025How Large Language Models are Designed to Hallucinatecited by 0