Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers
For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially from human attentional characteristics. We investigate whether this cognitive gap can be shrunk by fine-tuning the self-attention weights of Google's ViT-B/16 on human saliency fixation maps. To isolate the effects of semantically relevant signals from generic human supervision, the tuned model is compared against a shuffled control. Fine-tuning s
Record details
Published: 21 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (21 April 2026), “Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers,” evidence record 5520, https://ethics.ai/record/5520 (originally published by arXiv).
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