Biology subjects

Roefs, A.

Publications and source records attributed to Roefs, A..

2 recordsLinked to original sources

bioRxiv2026

Vision-language encoding models reveal an image-computable food-quality dimension in human occipitotemporal cortex

Perceived calorie content contributes to neural representational structure in human ventral visual cortex, yet it remains unclear whether this reflects an abstract nutritional signal or whether perceived calorie is largely recoverable from the visual-semantic structure of the food image itself. In 25 female participants who passively viewed 96 food images during functional MRI, we decomposed perceived calorie ratings into a component predicted from CLIP (Contrastive Language-Image Pretraining) image embeddings, a vision-language model that captures high-level visual-semantic image structure, and a residual component not captured by this CLIP-based prediction. We then tested their respective contributions to neural prediction using cross-validated banded ridge encoding models. The CLIP-predictable component organized foods along a processedness and naturalness dimension, separating raw single-ingredient foods from prepared and energy-dense foods. Adding this component to a visual-semantic baseline improved neural prediction progressively along the ventral visual hierarchy, with the strongest relative contribution in higher-level ventral temporal cortex. These findings indicate that calorie-related encoding in ventral visual cortex is carried mainly by a shared food-quality axis indexing processedness, naturalness, and perceived healthiness, a substantial part of which is recoverable from image-computable visual-semantic structure, rather than providing evidence for an isolated abstract representation of caloric magnitude. Because these results derive from a reanalysis of 25 female participants viewing a fixed set of 96 images, generalization to broader populations and larger, more varied stimulus sets remains to be established.

neuroscience↗

bioRxiv2026

Distributed representational encoding of food attributes in ventral visual cortex

Despite substantial progress in understanding how visual features of food are processed in the brain, it remains unclear how subjective food properties, such as perceived palatability, perceived caloric content, and perceived health value, are reflected in neural representational structure after accounting for visual similarity. Using functional MRI and representational similarity analysis (RSA), we examined visual, subjective, and categorical food representations in 25 healthy young women viewing 96 food images during an orthogonal color-discrimination task. Univariate analyses revealed reliable activation differences between high- and low-calorie foods in visual and ventral temporal cortex, with additional effects in orbitofrontal regions. Surface-based parcel-wise RSA and hypothesis-driven ROI analyses showed that visual models explained representational structure most strongly in early visual cortex and extended into occipitotemporal regions. Food-related models showed smaller but reliable effects in lateral and ventral occipitotemporal cortex: perceived calorie, perceived health, and objective calorie category were all associated with neural representational geometry in LOTC and VOTC. However, partial RSA and commonality analyses indicated that these effects were largely shared rather than separable. Perceived-calorie structure remained reliable after controlling for visual models, but not after controlling for perceived health. Palatability did not show reliable positive representational correspondence. These findings suggest that food-related information is reflected within occipitotemporal visual representational spaces during incidental viewing, but not as an isolated scalar calorie code. Instead, calorie-related effects appear to reflect a broader food-property axis on which energy density, perceived health, category structure, and learned food knowledge covary.

neuroscience↗