Go to ICLR 2025 Conference homepageFeature-Based Analysis of Theory of Mind Representations in Neural Network Models
Keywords: theory of mind, computational modeling, social cognition
TL;DR: We examine the features represented within neural networks as they learn to solve challenging theory of mind tasks
Abstract:
Theory of Mind (ToM) presents a significant generalization challenge in computational modeling. This paper explores how neural networks with varying architectures and training regimes learn and represent ToM-related features. We introduce a novel method for quantifying feature representation within neural networks and apply it to a set of theoretically-grounded features designed to differentiate between hypothesized ToM strategies. We examine the relationship between feature representation and task accuracy across different model architectures and training datasets. This work provides insights into the mechanisms underlying ToM capabilities in neural networks and offers a framework for future research in computational ToM.
Primary Area: applications to neuroscience & cognitive science
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Submission Number: 11566
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