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Expertise for cars and birds recruits brain areas involved in face recognition

Nature Neuroscience volume 3, pages 191–197 (2000)Cite this article

Abstract

Expertise with unfamiliar objects (‘greebles’) recruits face-selective areas in the fusiform gyrus (FFA) and occipital lobe (OFA). Here we extend this finding to other homogeneous categories. Bird and car experts were tested with functional magnetic resonance imaging during tasks with faces, familiar objects, cars and birds. Homogeneous categories activated the FFA more than familiar objects. Moreover, the right FFA and OFA showed significant expertise effects. An independent behavioral test of expertise predicted relative activation in the right FFA for birds versus cars within each group. The results suggest that level of categorization and expertise, rather than superficial properties of objects, determine the specialization of the FFA.

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Figure 1: Examples of stimuli and tasks for the fMRI protocol.
Figure 2: Mean percent signal change for each object category in the two expert groups in three face-specific ROIs and in the center of the right FFA.
Figure 3: The right FFA shows an expertise effect for birds and cars.
Figure 4: Main effects with grand mean and interaction partialed out and interaction effect with the grand mean and main effects partialed out, in the center of the right FFA.
Figure 5: Spatial distribution of percent signal change for faces, birds and cars (relative to an objects baseline) in a 5 × 5 voxel window in the right FFA, centered on the most strongly activated voxel in the localizer.
Figure 6: Expertise effect in the temporal cortex.
Figure 7: Relationship between a behavioral measure of expertise and activation in the right FFA.

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Acknowledgements

We thank Nancy Kanwisher and René Marois for discussions and Jill Moylan, Terry Hickey and Hedy Sarofin for technical assistance. This work was supported by NINDS grant NS33332 to J.C.G. and NIMH grant 56037 to N. Kanwisher. I.G. was supported by NSERC.

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Author notes

  1. Isabel Gauthier

    Present address: Department of Psychology, Vanderbilt University, Wilson Hall, Nashville, Tennessee, 37240, USA

Authors and Affiliations

  1. Department of Diagnostic Radiology, Yale University Medical School, Fitkin Basement, 333 Cedar Street, New Haven, 06510, Connecticut, USA

    Isabel Gauthier, Pawel Skudlarski, John C. Gore & Adam W. Anderson

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  1. Isabel Gauthier
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  2. Pawel Skudlarski
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  3. John C. Gore
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Corresponding author

Correspondence to Isabel Gauthier.

Additional information

Note: Additional analysis can be found on the Nature Neuroscience web site (http://neurosci.nature.com/web_specials/).

Supplementary information

Using a functional definition of a brain area considerably increases the sensitivity and statistical power of neuroimaging analyses. However, the rules used to define an 'area' are somewhat arbitrary, and different criteria will generate areas of different sizes that include variable proportions of highly selective voxels. When applying an ROI selected in a localizer run to results obtained in other runs, authors may choose either to make the region large enough to be confident of capturing the same selective voxels in other runs, or to make the region small enough that it will not include nonselective voxels that may not belong in the same functional area. These two goals motivated the two definitions used in our study. A first criterion defined a right FFA ROI (mean size of six voxels) which we found almost always overlapped with the face-selective region obtained in the experimental runs. A more stringent definition was used to define a center-of-the right 'face area' ROI with a mean size of three voxels. This second definition was adopted as analogous to the criterion used in single-cell recording1, emphasizing the magnitude of the selectivity for faces over objects in each voxel.

We also applied a criterion used previously by Kanwisher and colleagues2,3. This criterion selects all voxels that show a faces-versus-objects difference that is significant at p < 0.0001 on a Kolmogorov-Smirnov test. Because the data acquired in this study at 1.5 T was noisier than that obtained in this study at 3 T as well as in previous studies using this definition, only 7 of our 19 subjects had a right FFA ROI using this criterion. In these subjects, this definition led to a right FFA of six voxels, on average.

For comparison, we give the mean percent signal change in each condition for the right FFA for the three definitions in this subgroup of subjects as well as for the two definitions which could be used in all subjects (Table S1). We also give the percentage of the face selectivity that is accounted for by the expertise effect alone (expert - novice/face - novice) as well as by the level of categorization alone (novice - object/face - object). Note that there is some variability accounted for by the choice of the criterion but that the choice of the subject sample has a relatively more important effect.

Table S1 Percent signal changes in different analyses for the right FFA
Full size table

A major difference between the 2 most stringent criteria (p < 0.0001 and voxel-based) is that the p < 0.0001 criterion is less inclusive with regard to subject (only 7 of 19 subjects had a right FFA), whereas the voxel-based criterion is less inclusive with regard to number of voxels. (The mean size of the FFA defined by this criterion was three versus six in the FFA for the same subjects.) Nonetheless, the level of categorization and expertise effects were present using both definitions, although the magnitude of these effects is different. Note that the choice of the subject sample seemed to have more influence that the criterion per se. This was probably because the smaller sample included only subjects with FFAs with very large face selectivity, thereby artificially reducing the size of the other effects relative to the face activation. The magnitude of the expertise effect (expert - novice) was not significantly correlated with the magnitude of the face selectivity (face - object; p > 0.5), regardless of the ROI definition used. Note that the voxel-based criterion had the advantages of including more subjects as well as defining FFAs that showed stronger face selectivity in both subject samples.

REFERENCES in Supplementary Information

  1. 1

    Rolls, E. T. & Baylis, G. C. Size and contrast have only small effects on the response to faces of neurons in the cortex of the superior temporal sulcus of the monkey. Exp. Brain Res. 65, 38-48 (1986).

  2. 2

    Kanwisher, N., McDermott, J. & Chun, M. M. The fusiform face area: A module in human extrastriate cortex specialized for face perception. J. Neurosci. 17, 4302-4311 (1997).

  3. 3

    Kanwisher, N., Stanley, D. & Harris, A. The fusiform face area is selective for faces not animals. Neuroreport, 10, 183-187 (1999).

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Gauthier, I., Skudlarski, P., Gore, J. et al. Expertise for cars and birds recruits brain areas involved in face recognition. Nat Neurosci 3, 191–197 (2000). https://doi.org/10.1038/72140

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