Alignment of visual brain encoding models with individual perceptual similarity judgments
Encoding models that predict brain activation responses to images have traditionally been trained to maximize accuracy when predicting observed brain data. However, their internal representations may not reflect individual human perception. In this work, we investigate whether we can fine-tune brain encoding models to better align their representations with individuals' similarity judgments. Using similarity judgments of 100 images from 8 participants in the Natural Scenes Dataset (NSD), we adapt a generic human similarity judgment model (DreamSim) separately for each participant to obtain personalized perceptual similarity models. These models are then used to obtain subject-specific sim...
| Authors | Daniel Chong, Haomiao Chen, Keith W Jamison et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 15 October 2026 |
| Last updated at source | 20 October 2026 |
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