用人类认知心理学解释神经网络，揭示高激活词元的类别收敛机制
Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition
- 借鉴人类分类与相似性概念，构建类脑解释框架
- 发现高激活词元在类别空间中存在多维子结构叠加现象
- 适合对可解释性与类脑认知感兴趣的研究者
我们提出一种神经心理学方法来提升人工神经网络的可解释性，通过借鉴人类认知心理学中的分类与相似性概念，建立与人类思维模式对齐的合成解释框架。该方法旨在揭示一种独特的合成认知过程——即高激活词元的类别收敛机制。研究表明，神经元所形成的类别片段实际上是其输入向量空间中多个类别子维度的叠加结果，这一发现为理解人工神经网络的信息重构过程提供了新的视角。
原文摘要 · Abstract (English)
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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