Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?

• Published in: NeuroImage (Orlando, Fla.) Vol. 23; no. 1; pp. 156 - 166
• Main Authors: Hanson, Stephen José, Matsuka, Toshihiko, Haxby, James V.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2004; Elsevier Limited
• Subjects: Adult; Brain Mapping; Codes; Combinatorial codes; Discriminant analysis; Discrimination Learning - physiology; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Logistics; Magnetic Resonance Imaging - statistics & numerical data; Mathematical Computing; Medical imaging; Nerve Net - physiology; Neural networks; Neural Networks (Computer); Object recognition; Occipital Lobe - physiology; Orientation - physiology; Oxygen Consumption - physiology; Parahippocampal Gyrus - physiology; Pattern Recognition, Visual - physiology; Reference Values; Response rates; Sensitivity analysis; Statistical methods; Temporal Lobe - physiology; Ventral temporal lobe; Object recognition; Combinatorial codes; Ventral temporal lobe
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2004.05.020

Haxby et al. [Science 293 (2001) 2425] recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby's hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories.