August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
What's behind a face: semantic person identity coding in FFA, as revealed by multi-voxel pattern analysis
Author Affiliations
  • Job van den Hurk
    Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
    Maastricht Brain Imaging Center, Maastricht, the Netherlands
  • Bernadette M. Jansma
    Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
    Maastricht Brain Imaging Center, Maastricht, the Netherlands
Journal of Vision August 2010, Vol.10, 627. doi:10.1167/10.7.627
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      Job van den Hurk, Bernadette M. Jansma; What's behind a face: semantic person identity coding in FFA, as revealed by multi-voxel pattern analysis. Journal of Vision 2010;10(7):627. doi: 10.1167/10.7.627.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Identifying a familiar face involves the access to and activation of semantic knowledge about an individual. Several studies have shown that the Fusiform Face Area (FFA) is involved in face detection and identification. Conventional studies targeting face identification processes are generally limited to visual features, thereby ignoring semantic knowledge about individuals. To what extent FFA has access to person-specific knowledge remains unknown.

In the present study we addressed this issue by designing an 8 x 8 word matrix, consisting of 8 categories: profession, European capital cities, car brands, music styles, pets, hobbies, sports and housing types. Each column represents a category, whereas each row can be interpreted as information about an individual. In the fMRI-scanner, participants were repeatedly presented with blocks of 8 words: either presented in category-related context (column-wise, category condition), or presented in person-related context (row-wise, person condition). Subjects were instructed to memorize all 8 items belonging to each category (e.g., “sports”, category condition) and to each person (e.g. “John”, person condition). Using this approach, we were able to control for visual and semantic stimulation across conditions.

Univariate statistical contrasts did not show any significant differences between the two conditions in FFA. However, a multivariate method based on a machine learning classification algorithm was able to successfully classify the functional relationship between the two conditional contexts and their underlying response patterns in FFA. This suggests that activation patterns in FFA can code for different semantic contexts, thus going beyond facial feature processing. These results will encourage the debate about the specific role of FFA in face identification.

van den Hurk, J. Jansma, B. M. (2010). What's behind a face: semantic person identity coding in FFA, as revealed by multi-voxel pattern analysis [Abstract]. Journal of Vision, 10(7):627, 627a, http://www.journalofvision.org/content/10/7/627, doi:10.1167/10.7.627. [CrossRef]
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