The Selectivity and Competition of the Mind’s Eye in Visual Perception

Research has shown that neurons within the brain are selective to certain stimuli. For example, the fusiform face area (FFA) region is known by neuroscientists to selectively activate when people see faces over non-face objects. While the exact mechanisms by which the primary visual system directs information to the correct higher levels of the brain are currently unknown, there are high-level neural mechanisms of perception that we can incorporate in a novel computational model - ones that utilizes lateral and top down feedback in the form of hierarchical competition. We demonstrate that these neural mechanisms provide the foundation of a novel classification framework that rivals traditional supervised learning in computer vision. Additionally, we show that the innate priors built into our architecture support out of distribution generalization on the application of face detection.