title: Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation.
abstract: What is the role of the Fusiform Face Area (FFA)? Is it specific to face processing, or is it a visual expertise area? The expertise hypothesis is appealing due to a number of studies showing that the FFA is activated by pictures of objects within the subject's domain of expertise (e.g., cars for car experts, birds for birders, etc.), and that activation of the FFA increases as new expertise is acquired in the lab. However, it is incumbent upon the proponents of the expertise hypothesis to explain how it is that an area that is initially specialized for faces becomes recruited for new classes of stimuli. We dub this the "visual expertise mystery." One suggested answer to this mystery is that the FFA is used simply because it is a fine discrimination area, but this account has historically lacked a mechanism describing exactly how the FFA would be recruited for novel domains of expertise. In this study, we show that a neurocomputational model trained to perform subordinate-level discrimination within a visually homogeneous class develops transformations that magnify differences between similar objects, in marked contrast to networks trained to simply categorize the objects. This magnification generalizes to novel classes, leading to faster learning of new discriminations. We suggest this is why the FFA is recruited for new expertise. The model predicts that individual FFA neurons will have highly variable responses to stimuli within expertise domains.
authors.affiliation: Computer Science and Engineering, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0114, USA.
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Tong
authors.forename: Carrie A
authors.initials: CA
authors.lastname: Joyce
authors.forename: Garrison W
authors.initials: GW
authors.lastname: Cottrell
mesh_terms: D003198:Computer Simulation; D004193:Discrimination Learning / Q000502:physiology; D005145:Face; D006801:Humans; D007858:Learning / Q000502:physiology; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods; D012919:Social Behavior; D013663:Teaching / Q000379:methods; D013702:Temporal Lobe / Q000033:anatomy & histology / Q000502:physiology*; D014793:Visual Cortex / Q000033:anatomy & histology / Q000502:physiology*; D014795:Visual Pathways / Q000033:anatomy & histology / Q000502:physiology