Abstract
Scene information is conveyed by a large array of visual features, which are processed in specialized high-level visual areas after passing through early visual cortex. Scene processing is believed to rely mainly on low spatial frequencies, as inferred from the relative size of population receptive fields (pRFs) in scene processing areas. In an apparent contradiction with this account, information of scene content in high-level visual areas relies predominantly on high spatial frequency information. Here we attempt to reconcile these accounts by combining pRF mapping of visual cortex with the decoding of scene information from fMRI activity elicited by modified images. Specifically, we re-analyzed the fMRI data from Berman et al's (2017) study of spatial frequency-filtered scene images. We computed scene category prediction accuracy within functionally localized Parahippocampal Place Area (PPA) and primary visual cortex (V1). We then selected subsets of voxels within these ROIs based on their pRF properties, as derived from the Human Connectome Project retinotopy dataset (Benson et al., 2018). In area V1, we found significantly greater decoding accuracy for images filtered for low spatial frequencies (LSF) in peripheral voxels with large pRFs compared to foveal voxels with smaller pRFs, thereby confirming the presumed relationship between receptive field size and sensitivity to spatial frequencies. In the PPA, on the other hand, we found no such difference based on pRF size. Instead, the PPA is organized along the AP axis, with more accurate decoding of HSF scenes in anterior than posterior PPA. These findings demonstrate that while the commonly held relationship between receptive field size and specialization for spatial frequencies holds in primary visual cortex, it does not extend to the PPA with its more complex receptive field properties. Instead, an anatomical subdivision of the PPA along the AP axis dominates its specialization for particular spatial frequencies.