Philosophy of Cognitive Science in the Age of Deep Learning

TL;DR

This paper interrogates how advances in deep learning bear on the philosophy of cognitive science and the modeling of human cognition. It traces the historical shift from symbolic cognition to connectionism, and assesses how deep neural networks—through depth, architectural biases, and large-scale data—challenge prior critiques of neural approaches. It discusses compositional generalization, variable binding, grounding, and the role of language models in informing linguistic theory and theory of mind, while acknowledging that DLs may rely on different mechanisms than classical theories. The piece argues for hypothesis-driven, interdisciplinary evaluation to rigorously compare human and machine cognition, and to determine the extent to which DLs can serve as genuine cognitive models with explanatory power.

Abstract

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.