Action features dominate cortical representation during natural vision

Abstract

Cortical resources are allocated into systems specialized for processing ecologically relevant features of the world. These features are typically studied in isolation using controlled experimental stimuli, making it difficult to assess the relative importance of different kinds of features that tend to overlap during natural vision. In the current study, we evaluated the relative contributions of action, agent, and scene features in predicting cortical activity while participants viewed a 1-hour nature documentary. We tested four sets of model features: semantic vectors capturing the observed action, agent, and scene features derived from an annotation of the stimulus, as well as low-level visual motion-energy features. We used banded ridge regression to fit vertex-wise encoding models from the combination of all four sets of features. While each set of features predicted neural activity in the expected areas, the action features predicted more widespread activity. A variance partitioning analysis revealed that the action features captured the most unique variance in neural activity across ten times as many cortical vertices as the agent or scene features. Our findings suggest that cortical activity during dynamic, natural vision is dominated by features for understanding the actions of others.