The translation of retinal images into recognizable objects and scenes is not yet well understood. Beyond edge-detection in primary visual cortex, higher stages of cortical representation are still uncertain. We use a multi-layer convolutionalneural network (Krizhevsky, 2012) to provide models for visual selectivities in the ventral visual pathway. We examine individ-ual neural units, or ”kernels”, in CNN layer 2, correlating kernel activity to single fMRI voxel activity for 1750 natural images(Kay, 2008). Building on G ̈uc ̧l ̈u (2015), we find most significant voxel-kernel correlations in V2, with additional matchesthroughout the ventral pathway. Notably, only 25% of kernels correlate with voxel responses — many voxels correlate with aconsistent small set of kernels. Inhibition of voxel response for kernel selectivities also was observed. Our results indicate alimited number of CNN kernels may be used to gain a finer understanding of voxel level representations in the mid-level ventralvisual pathway.

Localizing function in the brain has been an elusive long-term goal in the study of cognition. Prior studies have utilized four reference abilities (RAs) to capture cognition (Salthouse, 2009). Full-brain cortical networks have been tied to these abilities using common multi-voxel patterns across subjects in distinct age groups. Using voxel searchlights the current study explores purely local cortical representations of cognition, less commonly explored. This work analyzes 240 subjects’ responses to cognitive tasks from the four RAs. The current study further employs representational similarity analysis (RSA, Kriegeskorte, Mur, & Bandettini, 2008) to the similarity of brain activities from tasks within the same RA; RSA can capture representational consistencies within each subject even when exact voxel pattern may vary across subjects. We found distinct topographical localizations for each RA that were mostly consistent across age and suggested refinements of broader functional divisions of the brain from prior literature.