A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex

• Published in: Neuron (Cambridge, Mass.) Vol. 72; no. 2; pp. 404 - 416
• Main Authors: Haxby, James V., Guntupalli, J. Swaroop, Connolly, Andrew C., Halchenko, Yaroslav O., Conroy, Bryan R., Gobbini, M. Ida, Hanke, Michael, Ramadge, Peter J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20.10.2011; Elsevier Limited
• Subjects: Accuracy; Adult; Brain; Brain Mapping - methods; Classification; Functional Neuroimaging; Humans; Image Processing, Computer-Assisted; Models, Neurological; Neurons - physiology; Photic Stimulation; Principal components analysis; Temporal Lobe - physiology; Visual Cortex - physiology; Visual Perception - physiology
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2011.08.026

We present a high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response tunings. We map response-pattern vectors, measured with fMRI, from individual subjects' voxel spaces into this common model space using a new method, “hyperalignment.” Hyperalignment parameters based on responses during one experiment—movie viewing—identified 35 common response-tuning functions that captured fine-grained distinctions among a wide range of stimuli in the movie and in two category perception experiments. Between-subject classification (BSC, multivariate pattern classification based on other subjects' data) of response-pattern vectors in common model space greatly exceeded BSC of anatomically aligned responses and matched within-subject classification. Results indicate that population codes for complex visual stimuli in VT cortex are based on response-tuning functions that are common across individuals. ► Response-tuning functions for visual population codes are common across individuals ► 35 response basis functions capture fine-grained distinctions among representations ► The common model space greatly improves between-subject classification of fMRI data ► The model has general validity across brains and across a wide range of stimuli