Arash Akbarinia

Abstract A key goal of visual neuroscience is to explain how our brains infer object properties such as colour, curvature or gloss. Here we used machine learning to identify computations underlying human gloss judgements—traditionally considered a challenging inference. We rendered thousands of objects with varied shapes using a Ward reflectance model across lighting and viewpoints, then obtained gloss ratings for each image.

Abstract This study delves into the categorical aspects of colour perception, employing the odd-one-out paradigm on artificial neural networks. We reveal a significant alignment between human data and unimodal vision networks (e.g., ImageNet object recognition). Vision-language models (e.g., CLIP text-image matching) account for the remaining unexplained data even in non-linguistic experiments.