Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition

• URL: http://arxiv.org/abs/2411.07243v1
• Date: Wed, 23 Oct 2024 05:27:09 GMT
• Title: Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition
• Authors: Michael Pichat, Enola Campoli, William Pogrund, Jourdan Wilson, Michael Veillet-Guillem, Anton Melkozerov, Paloma Pichat, Armanush Gasparian, Samuel Demarchi, Judicael Poumay,
• Abstract summary: We propose an approach to explainability of artificial neural networks that involves using concepts from human cognitive tokens. We show that the categorical segment created by a neuron is actually the result of a superposition of categorical sub-dimensions within its input vector space.
• Score: 0.11235145048383502
• License:
• Abstract: We propose a neuropsychological approach to the explainability of artificial neural networks, which involves using concepts from human cognitive psychology as relevant heuristic references for developing synthetic explanatory frameworks that align with human modes of thought. The analogical concepts mobilized here, which are intended to create such an epistemological bridge, are those of categorization and similarity, as these notions are particularly suited to the categorical "nature" of the reconstructive information processing performed by artificial neural networks. Our study aims to reveal a unique process of synthetic cognition, that of the categorical convergence of highly activated tokens. We attempt to explain this process with the idea that the categorical segment created by a neuron is actually the result of a superposition of categorical sub-dimensions within its input vector space.

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