Forming classes by stimulus frequency: Behavior and theory

Visual classification is the way we relate to different images in our envir onment as if they were the same, while relating differently to other collec tions of stimuli (e.g., human vs, animal faces). It is still not clear, how ever, how the brain forms such classes, especially when introduced with new or changing environments. To isolate a perception-based mechanism underlyi ng class representation, we studied unsupervised classification of an incom ing stream of simple images. Classification patterns were clearly affected by stimulus frequency distribution, although subjects were unaware of this distribution. There was a common bias to locate class centers near the most frequent stimuli and their boundaries near the least frequent stimuli. Res ponses were also faster for more frequent stimuli. Using a minimal, biologi cally based neural-network model, we demonstrate that a simple, self-organi zing representation mechanism based on overlapping tuning curves and slow H ebbian learning suffices to ensure classification. Combined behavioral and theoretical results predict large tuning overlap, implicating posterior inf ero-temporal cortex as a possible site of classification.