Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks
<|reference_start|>Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks: 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.<|reference_end|>
@article{dorszewski2024connecting,
    title={Connecting Concept Convexity and Human-Machine Alignment in Deep Neural
  Networks},
    author={Teresa Dorszewski, Lenka Tv{e}tkov'a, Lorenz Linhardt, Lars Kai
  Hansen},
    journal={arXiv preprint arXiv:2409.06362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06362},
    primaryClass={cs.LG cs.AI}
}