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Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognition
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Neuropsychology of artificial intelligence focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. With the goal of making artificial neural networks of language models more explainable, this approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition. The human cognitive concept involved here is categorization, serving as a heuristic for thinking about the process of segmentation and construction of reality carried out by the neural vectors of synthetic cognition.
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Cited by 2 Pith papers
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The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition
Words that strongly activate both a lower-layer neuron and its strongly connected upper-layer neuron in GPT-2XL form more semantically similar clusters, which the paper interprets as a clipping process.
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Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces
A GPT2-XL analysis reports that a neuron's highest-activation tokens are also the ones most similar to multiple categorical subclusters, offered as evidence for an intra-neuronal vector-space view of polysemy.
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