Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI “localizer” using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

Convolutional neural networks (CNNs) trained for object recognition have been widely used to account for visually-driven neural responses in both the human and primate brains. However, because of the generality and complexity of the task of object classification, it is often difficult to make precise inferences about neural information processing using CNN representations from object classification despite the fact that these representations are effective for predicting brain activity. To better understand underlying the nature of the visual features encoded in different brain regions of the human brain, we predicted brain responses to images using fine-grained representations drawn from 19 specific computer vision tasks. Individual encoding models for each task were constructed and then applied to BOLD5000-a large-scale dataset comprised of fMRI scans collected while observers viewed over 5000 naturalistic scene and object images. Because different encoding models predict activity in different brain regions, we were able to associate specific vision tasks with each region. For example, within scene-selective brain regions, features from 3D tasks such as 3D keypoints and 3D edges explain greater variance as compared to 2D tasks-a pattern that replicates across the whole brain. Using results across all 19 task representations, we constructed a "task graph" based on the spatial layout of well-predicted brain areas from each task. We then compared the brain-derived task structure with the task structure derived from transfer learning accuracy in order to assess the degree of shared information between the two task spaces. These computationally-driven results-arising out of state-of-the-art computer vision methods-begin to reveal the task-specific architecture of the human visual system.

Ventral visual cortex contains regions of selectivity for domains of ecological importance. Food is an ecologically and evolutionarily important category, whose high degree of visual variability may make the identification of selectivity more challenging. We investigated neural responsiveness to food using natural images combined with large-scale human neuroimaging. Leveraging the improved sensitivity of modern designs and statistical analysis methods, we identified two food-selective regions in the ventral visual cortex. Our results were robust across 8 subjects, multiple independent sets of images and multiple analysis methods. Additionally, these results were not due to stimulus properties or saliency. The identification of food-selective regions stands alongside prior findings of functional selectivity and provides an important addition to our understanding of the organization of knowledge within the human visual system.

We hypothesize that high-level visual representations contain more than the representation of individual categories: they represent complex semantic information inherent in scenes that is most relevant for interaction with the world. Consequently, multimodal models such as Contrastive Language-Image Pre-training (CLIP) which construct image embeddings to best match embeddings of image captions should better predict neural responses in visual cortex, since image captions typically contain the most semantically relevant information in an image for humans. We extracted image features using CLIP, which encodes visual concepts with supervision from natural language captions. We then used voxelwise encoding models based on CLIP features to predict brain responses to real-world images from the Natural Scenes Dataset. CLIP explains up to R2=78% of variance in stimulus-evoked responses from individual voxels in the held out test data. CLIP also explains greater unique variance in higher-level visual areas compared to models trained only with image/label pairs (ImageNet trained ResNet) or text (BERT). Visualizations of model embeddings and Principal Component Analysis (PCA) reveal that, with the use of captions, CLIP captures both global and fine-grained semantic dimensions represented within visual cortex. Based on these novel results, we suggest that human understanding of their environment form an important dimension of visual representation.