title: Explicit information for category-orthogonal object properties increases along the ventral stream.
abstract: Extensive research has revealed that the ventral visual stream hierarchically builds a robust representation for supporting visual object categorization tasks. We systematically explored the ability of multiple ventral visual areas to support a variety of 'category-orthogonal' object properties such as position, size and pose. For complex naturalistic stimuli, we found that the inferior temporal (IT) population encodes all measured category-orthogonal object properties, including those properties often considered to be low-level features (for example, position), more explicitly than earlier ventral stream areas. We also found that the IT population better predicts human performance patterns across properties. A hierarchical neural network model based on simple computational principles generates these same cross-area patterns of information. Taken together, our empirical results support the hypothesis that all behaviorally relevant object properties are extracted in concert up the ventral visual hierarchy, and our computational model explains how that hierarchy might be built.
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Ha
authors.initials: H
authors.lastname: Hong
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Daniel L K
authors.initials: DL
authors.lastname: Yamins
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Najib J
authors.identifier: http://orcid.org/0000-0001-9910-5627
authors.initials: NJ
authors.lastname: Majaj
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: DiCarlo
mesh_terms: D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology*; D021641:Recognition, Psychology / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*