A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex

Mid-level visual features, such as contour and texture, provide a computational link between low- and high-level visual representations. While the detailed nature of mid-level representations in the brain is not yet fully understood, past work has suggested that a texture statistics model (P-S model; Portilla Simoncelli, 2000) is a candidate for predicting neural responses in areas V1-V4 as well as human behavioral data. However, it is not currently known how well this model accounts for the responses of higher visual cortex regions to natural scene images. To examine this, we constructed single voxel encoding models based on P-S statistics and fit the models to human fMRI data from the Natural Scenes Dataset (Allen et al., 2021). We demonstrate that the texture statistics encoding model can predict the held-out responses of individual voxels in early retinotopic areas as well as higher-level category-selective areas. The ability of the model to reliably predict signal in higher visual cortex voxels suggests that the representation of texture statistics features is widespread throughout the brain, potentially playing a role in higher-order processes like object recognition. Furthermore, we use variance partitioning analyses to identify which features are most uniquely predictive of brain responses, and show that the contributions of higher-order texture features to model accuracy increases from early areas to higher areas on the ventral and lateral surface of the brain. These results provide a key step forward in characterizing how mid-level feature representations emerge hierarchically across the visual system.