  If neuroscientists were asked which brain area is responsible for object
recognition in primates, most would probably answer infero-temporal (IT)
cortex. While IT is likely responsible for fine discriminations, and it is
accordingly dominated by foveal visual inputs, there is more to object
recognition than fine discrimination. Importantly, foveation of an object of
interest usually requires recognizing, with reasonable confidence, its presence
in the periphery. Arguably, IT plays a secondary role in such peripheral
recognition, and other visual areas might instead be more critical. To
investigate how signals carried by early visual processing areas (such as LGN
and V1) could be used for object recognition in the periphery, we focused here
on the task of distinguishing faces from non-faces. We tested how sensitive
various models were to nuisance parameters, such as changes in scale and
orientation of the image, and the type of image background. We found that a
model of V1 simple or complex cells could provide quite reliable information,
resulting in performance better than 80% in realistic scenarios. An LGN model
performed considerably worse. Because peripheral recognition is both crucial to
enable fine recognition (by bringing an object of interest on the fovea), and
probably sufficient to account for a considerable fraction of our daily
recognition-guided behavior, we think that the current focus on area IT and
foveal processing is too narrow. We propose that rather than a hierarchical
system with IT-like properties as its primary aim, object recognition should be
seen as a parallel process, with high-accuracy foveal modules operating in
parallel with lower-accuracy and faster modules that can operate across the
visual field.
