September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
High Resolution fMRI Reveals Holistic Car Representations in the anterior FFA of Car Experts
Author Affiliations
  • David Ross
    Vanderbilt University University of Massachusetts Amherst
  • Benjamin Tamber-Rosenau
    Vanderbilt University
  • Thomas Palmeri
    Vanderbilt University
  • Jiedong Zhang
    Harvard University
  • Yaoda Xu
    Harvard University
  • Isabel Gauthier
    Vanderbilt University
Journal of Vision September 2015, Vol.15, 614. doi:10.1167/15.12.614
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      David Ross, Benjamin Tamber-Rosenau, Thomas Palmeri, Jiedong Zhang, Yaoda Xu, Isabel Gauthier; High Resolution fMRI Reveals Holistic Car Representations in the anterior FFA of Car Experts. Journal of Vision 2015;15(12):614. doi: 10.1167/15.12.614.

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

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Abstract

Perceptual expertise with an object category correlates with increased neural selectivity to that category in several visual areas, with the most robust effects in the fusiform face area (FFA). While expertise effects in FFA are well established, little is known about the representations that underlie these effects. Prior work in training studies with novel objects found that acquired behavioral holistic processing effects correlated with selectivity in FFA. Here, we probe the neural representations of cars for evidence of holistic information as a function of car expertise. With high-resolution 7T fMRI, in a sample of 26 participants, we measured the activation patterns elicited by whole cars, scrambled cars and car parts (top or bottom halves). We trained a SVM classifier to differentiate whole and scrambled cars in several face and object selective ROIs (FFA1, FFA2, OFA and LO). We then tested the classifier on the average of the part-evoked fMRI patterns. If the neural representations consisted only of part information, the classifier should classify the average as being equally like the whole car as the scrambled car. However, if the neural representations included information about the configural information of the parts, then the average of the parts should look more like the scrambled image than the whole. In line with this second prediction, we found a strong correlation between the tendency for the classifier to classify an averaged part pattern as scrambled and behavioral car expertise (r = 0.58, p< 0.01) in the anterior FFA (FFA2), bilaterally. FFA1, OFA and LO did not show expertise effects (p.40). These results go beyond the correlation of neural signals with behavioral holistic effects, providing direct evidence that the neural representations of objects in FFA of perceptual experts are more holistic than in novices.

Meeting abstract presented at VSS 2015

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