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arXiv · 2505.13742

Understanding Task Representations in Neural Networks via Bayesian Ablation

Andrew Nam·Declan Campbell·Thomas Griffiths·Jonathan Cohen·Sarah-Jane Leslie
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Abstract

Neural networks are powerful tools for cognitive modeling due to their flexibility and emergent properties. However, interpreting their learned representations remains challenging due to their sub-symbolic semantics. In this work, we introduce a novel probabilistic framework for interpreting latent task representations in neural networks. Inspired by Bayesian inference, our approach defines a distribution over representational units to infer their causal contributions to task performance. Using ideas from information theory, we propose a suite of tools and metrics to illuminate key model properties, including representational distributedness, manifold complexity, and polysemanticity.

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Andrew Nam, Declan Campbell, Thomas Griffiths, Jonathan Cohen, Sarah-Jane Leslie. 2025-05-19. Understanding Task Representations in Neural Networks via Bayesian Ablation. https://arxiv.org/abs/2505.13742

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