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arXiv · 2409.06362

Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks

Teresa Dorszewski·Lenka Tětková·Lorenz Linhardt·Lars Kai Hansen
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Abstract

Understanding how neural networks align with human cognitive processes is a crucial step toward developing more interpretable and reliable AI systems. Motivated by theories of human cognition, this study examines the relationship between \emph{convexity} in neural network representations and \emph{human-machine alignment} based on behavioral data. We identify a correlation between these two dimensions in pretrained and fine-tuned vision transformer models. Our findings suggest that the convex regions formed in latent spaces of neural networks to some extent align with human-defined categories and reflect the similarity relations humans use in cognitive tasks. While optimizing for alignment generally enhances convexity, increasing convexity through fine-tuning yields inconsistent effects on alignment, which suggests a complex relationship between the two. This study presents a first step toward understanding the relationship between the convexity of latent representations and human-machine alignment.

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Teresa Dorszewski, Lenka Tětková, Lorenz Linhardt, Lars Kai Hansen. 2024-09-10. Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks. https://arxiv.org/abs/2409.06362

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