“Identifying and characterizing scene representations relevant for categorization behavior”

Scene recognition is a core sensory capacity that enables humans to adaptively interact with their environment. Despite substantial progress in the understanding of the neural representations underlying scene recognition, the relevance of these representations for behavior given varying task demands remains unknown. To address this, we aimed to identify behaviorally relevant scene representations, to characterize them in terms of their underlying visual features, and to reveal how they vary across different tasks. We recorded fMRI data while human participants viewed scenes and linked brain responses to behavior in three tasks acquired in separate sessions: manmade/natural categorization, basic-level categorization, and fixation color discrimination. We found correlations between categorization response times and scene-specific brain responses, quantified as the distance to a hyperplane derived from a multivariate classifier. Across tasks, these effects were found in largely distinct parts of the ventral visual stream. This suggests that different scene representations are relevant for behavior depending on the task. Next, using deep neural networks as a proxy for visual feature representations, we found that early/intermediate layers mediated the relationship between scene representations and behavior for both categorization tasks, indicating a contribution of low-/mid-level visual features to these representations. Finally, we observed opposite patterns of brain-behavior correlations in the manmade/natural and the fixation task, indicating interference of representations with behavior for task demands that do not align with the content of representations. Together, these results reveal the spatial extent, content, and task-dependence of the visual representations that mediate behavior in complex scenes.