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Faciotopy-A face-feature map with face-like topology in the human occipital face area.

Henriksson L, Mur M, Kriegeskorte N - Cortex (2015)

Bottom Line: The responses in V1 were best explained by low-level image properties of the stimuli.OFA, and to a lesser degree FFA, showed evidence for faciotopic organization.Faciotopy would be the first example, to our knowledge, of a cortical map reflecting the topology, not of a part of the organism itself (its retina in retinotopy, its body in somatotopy), but of an external object of particular perceptual significance.

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Affiliation: MRC Cognition and Brain Sciences Unit, Cambridge, UK; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. Electronic address: linda.henriksson@aalto.fi.

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Distinctness and size-tolerance of the face-feature representations in V1, OFA and FFA. a) The linear discriminant analysis t-values and the corresponding corrected p-values are shown for all pair-wise comparisons of the face-feature response-patterns in V1 (top panel), OFA (middle panel) and FFA (bottom panel). The training data was the data from the first measurement session and the testing was done on data from the second session. In OFA, all face-feature stimuli could be discriminated from each other (no blue squares in the p-value matrix). b) To test for size-tolerance of the feature representations, the linear discriminant analysis was performed by training the classifiers on the small face-feature response-patterns and testing the classifiers on the large face-feature response-patterns. The results are shown as in (a). OFA shows successful generalization of the face-feature discrimination across stimulus size (no blue squares in the p-value matrix), suggesting size-tolerant feature representations.
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fig4: Distinctness and size-tolerance of the face-feature representations in V1, OFA and FFA. a) The linear discriminant analysis t-values and the corresponding corrected p-values are shown for all pair-wise comparisons of the face-feature response-patterns in V1 (top panel), OFA (middle panel) and FFA (bottom panel). The training data was the data from the first measurement session and the testing was done on data from the second session. In OFA, all face-feature stimuli could be discriminated from each other (no blue squares in the p-value matrix). b) To test for size-tolerance of the feature representations, the linear discriminant analysis was performed by training the classifiers on the small face-feature response-patterns and testing the classifiers on the large face-feature response-patterns. The results are shown as in (a). OFA shows successful generalization of the face-feature discrimination across stimulus size (no blue squares in the p-value matrix), suggesting size-tolerant feature representations.

Mentions: We have now shown that V1, OFA and FFA respond to isolated face-features (Fig. 3), but do they also discriminate between the face features (e.g., an eye from a mouth)? Fig. 4a shows the results from linear discriminant analysis (Nili et al., 2014): the discriminability of each pair of face-feature stimuli was evaluated by fitting a Fisher linear discriminant to the response patterns from the first fMRI session and by testing the performance on the response patterns from the second fMRI session (same subject, different day, different stimulus presentation order, all stimulus layouts). The analyses were done on individual data and the results were pooled across the twelve subjects. The left column in Fig. 4a shows the linear-discriminant t-values, reflecting the discrimability of each pair of face-feature stimuli from the response patterns, and the right column shows the corresponding p-values. In V1, the response patterns discriminated each pair of face-feature stimuli, except the two hairlines from each other and the mouth from the chin (Fig. 4a, first row). In OFA, each pair of the face-feature stimuli could be discriminated from the response patterns (Fig. 4a, middle row). In addition, there appears to be a distinction between the inner (first five elements in the linear discriminant t-value and p-value matrices; e.g., the eyes) and outer face-features (elements 6–12 in the matrices; e.g., the ears), that is, the t-values are high for the discriminability of these stimulus pairs in OFA, and also in FFA. Moreover, in FFA, the symmetric face-features (the eyes, the hairlines, the ears, the jaw lines) evoked indistinguishable response patterns (bottom row in Fig. 4a; see the blue rectangles in the p-value matrix).


Faciotopy-A face-feature map with face-like topology in the human occipital face area.

Henriksson L, Mur M, Kriegeskorte N - Cortex (2015)

Distinctness and size-tolerance of the face-feature representations in V1, OFA and FFA. a) The linear discriminant analysis t-values and the corresponding corrected p-values are shown for all pair-wise comparisons of the face-feature response-patterns in V1 (top panel), OFA (middle panel) and FFA (bottom panel). The training data was the data from the first measurement session and the testing was done on data from the second session. In OFA, all face-feature stimuli could be discriminated from each other (no blue squares in the p-value matrix). b) To test for size-tolerance of the feature representations, the linear discriminant analysis was performed by training the classifiers on the small face-feature response-patterns and testing the classifiers on the large face-feature response-patterns. The results are shown as in (a). OFA shows successful generalization of the face-feature discrimination across stimulus size (no blue squares in the p-value matrix), suggesting size-tolerant feature representations.
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fig4: Distinctness and size-tolerance of the face-feature representations in V1, OFA and FFA. a) The linear discriminant analysis t-values and the corresponding corrected p-values are shown for all pair-wise comparisons of the face-feature response-patterns in V1 (top panel), OFA (middle panel) and FFA (bottom panel). The training data was the data from the first measurement session and the testing was done on data from the second session. In OFA, all face-feature stimuli could be discriminated from each other (no blue squares in the p-value matrix). b) To test for size-tolerance of the feature representations, the linear discriminant analysis was performed by training the classifiers on the small face-feature response-patterns and testing the classifiers on the large face-feature response-patterns. The results are shown as in (a). OFA shows successful generalization of the face-feature discrimination across stimulus size (no blue squares in the p-value matrix), suggesting size-tolerant feature representations.
Mentions: We have now shown that V1, OFA and FFA respond to isolated face-features (Fig. 3), but do they also discriminate between the face features (e.g., an eye from a mouth)? Fig. 4a shows the results from linear discriminant analysis (Nili et al., 2014): the discriminability of each pair of face-feature stimuli was evaluated by fitting a Fisher linear discriminant to the response patterns from the first fMRI session and by testing the performance on the response patterns from the second fMRI session (same subject, different day, different stimulus presentation order, all stimulus layouts). The analyses were done on individual data and the results were pooled across the twelve subjects. The left column in Fig. 4a shows the linear-discriminant t-values, reflecting the discrimability of each pair of face-feature stimuli from the response patterns, and the right column shows the corresponding p-values. In V1, the response patterns discriminated each pair of face-feature stimuli, except the two hairlines from each other and the mouth from the chin (Fig. 4a, first row). In OFA, each pair of the face-feature stimuli could be discriminated from the response patterns (Fig. 4a, middle row). In addition, there appears to be a distinction between the inner (first five elements in the linear discriminant t-value and p-value matrices; e.g., the eyes) and outer face-features (elements 6–12 in the matrices; e.g., the ears), that is, the t-values are high for the discriminability of these stimulus pairs in OFA, and also in FFA. Moreover, in FFA, the symmetric face-features (the eyes, the hairlines, the ears, the jaw lines) evoked indistinguishable response patterns (bottom row in Fig. 4a; see the blue rectangles in the p-value matrix).

Bottom Line: The responses in V1 were best explained by low-level image properties of the stimuli.OFA, and to a lesser degree FFA, showed evidence for faciotopic organization.Faciotopy would be the first example, to our knowledge, of a cortical map reflecting the topology, not of a part of the organism itself (its retina in retinotopy, its body in somatotopy), but of an external object of particular perceptual significance.

View Article: PubMed Central - PubMed

Affiliation: MRC Cognition and Brain Sciences Unit, Cambridge, UK; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. Electronic address: linda.henriksson@aalto.fi.

Show MeSH
Related in: MedlinePlus