The Neural Basis of the Behavioral Face-Inversion Effect

McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Current Biology (Impact Factor: 9.57). 01/2006; 15(24):2256-62. DOI: 10.1016/j.cub.2005.10.072
Source: PubMed


Two of the most robust markers for "special" face processing are the behavioral face-inversion effect (FIE)-the disproportionate drop in recognition of upside-down (inverted) stimuli relative to upright faces-and the face-selective fMRI response in the fusiform face area (FFA). However, the relationship between these two face-selective markers is unknown. Here we report that the behavioral FIE is closely associated with the fMRI response in the FFA, but not in other face-selective or object-selective regions. The FFA and the face-selective region in the superior temporal sulcus (f_STS), but not the occipital face-selective region (OFA), showed a higher response to upright than inverted faces. However, only in the FFA was this fMRI-FIE positively correlated across subjects with the behavioral FIE. Second, the FFA, but not the f_STS, showed greater neural sensitivity to differences between faces when they were upright than inverted, suggesting a possible neural mechanism for the behavioral FIE. Although a similar trend was found in the occipital face area (OFA), it was less robust than the FFA. Taken together, our data suggest that among the face-selective and object-selective regions, the FFA is a primary neural source of the behavioral FIE.

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    • "Although it has been argued that the FFA might not be specialized for faces per se, but for individuation within any object class of high expertise (Gauthier et al., 2000, 1999), there is evidence that the FFA is genuinely face-sensitive (Rhodes et al., 2004; see also McKone, Kanwisher, and Duchaine, 2007, for a review arguing that the FFA is specialized for faces and not for any object class of expertise). Individual faces are assumed to be represented in a holistic manner in the FFA (e.g., Schiltz and Rossion, 2006; Yovel and Kanwisher, 2005). According to the neurocognitive model proposed by Haxby et al. (2000), face perception and recognition are based on a distributed neural network. "
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    ABSTRACT: Misidentifications are a common phenomenon in unfamiliar face processing, but little is known about the underlying cognitive and neural mechanisms. We used the face identity-sensitive N250r component of the event-related brain potential as a measure of identity-sensitive face matching process in visual working memory. Two face images were presented in rapid succession, and participants had to judge whether they showed the same or two different individuals. Identity match and mismatch trials were presented in random sequence. On similar mismatch trials, perceptually similar faces of two different individuals were shown, while two physically distinct faces were presented on dissimilar mismatch trials. Misidentification errors occurred on 40% of all similar mismatch trials. N250r components were elicited not only in response to an identity match, but also on trials with misidentification errors. This misidentification N250r was smaller and emerged later than the N250r to correctly detected identity repetitions. Importantly, N250r components were entirely eliminated on similar mismatch trials where participants correctly reported two different facial identities. Results show that misidentification errors are not primarily a post-perceptual decision-related phenomenon, but are generated during early visual stages of identity-related face processing. Misidentification errors occur when stored representations of a particular individual face in visual working memory are incorrectly activated by a perceptual match with a different face.
    Neuropsychologia 09/2015; DOI:10.1016/j.neuropsychologia.2015.09.021 · 3.30 Impact Factor
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    • "Face inversion reduces face identification accuracy, implying that internal face representations are not fully invariant to orientation (Gold et al., 2012; X. Jiang et al., 2006; Susilo, McKone, & Edwards, 2010; Yovel & Kanwisher, 2005). The Jiang et al. (2007) model that was used to generate dissimilarities for Experiment 1 did not take orientation into account. "
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    ABSTRACT: From phonetic features to connected discourse, every level of psycholinguistic structure including prosody can be perceived through viewing the talking face. Yet a longstanding notion in the literature is that visual speech perceptual categories comprise groups of phonemes (referred to as visemes), such as /p, b, m/ and /f, v/, whose internal structure is not informative to the visual speech perceiver. This conclusion has not to our knowledge been evaluated using a psychophysical discrimination paradigm. We hypothesized that perceivers can discriminate the phonemes within typical viseme groups, and that discrimination measured with d-prime (d’) and response latency is related to visual stimulus dissimilarities between consonant segments. In Experiment 1, participants performed speeded discrimination for pairs of consonant-vowel (CV) spoken nonsense syllables that were predicted to be same, near, or far in their perceptual distances, and that were presented as natural or synthesized video. Near pairs were within-viseme consonants. Natural within-viseme stimulus pairs were discriminated significantly above chance (except for /k/-/h/). Sensitivity (d’) increased and response times decreased with distance. Discrimination and identification were superior with natural stimuli, which comprised more phonetic information. We suggest that the notion of the viseme as a unitary perceptual category is incorrect. Experiment 2 probed the perceptual basis for visual speech discrimination by inverting the stimuli. Overall reductions in d’ with inverted stimuli but a persistent pattern of larger d’ for far than for near stimulus pairs are interpreted as evidence that visual speech is represented by both its motion and configural attributes. The methods and results of this investigation open up avenues for understanding the neural and perceptual bases for visual and audiovisual speech perception and for development of practical applications such as visual speech synthesis.
    Frontiers in Psychology 07/2015; 6:878. DOI:10.3389/fpsyg.2015.00878 · 2.80 Impact Factor
    • "Our data revealed that the FSRs showed considerable interindividual variability in both functional and spatial features. As for the functional features, the FSRs showed significant variability in their selectivity for faces, consistent with previous studies (Furl et al., 2011; Gauthier et al., 2005; Yovel and Kanwisher, 2005). For the spatial features, to our knowledge, only a few studies have provided quantitative estimates of intersubject variability of the FSRs, mostly focusing on the variability of the location of peak activation. "
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    ABSTRACT: Face-selective regions (FSRs) are among the most widely studied functional regions in the human brain. However, individual variability of the FSRs has not been well quantified. Here we use functional magnetic resonance imaging (fMRI) to localize the FSRs and quantify their spatial and functional variability in 202 healthy adults. The occipital face area (OFA), posterior and anterior fusiform face area (pFFA and aFFA), posterior continuation of the superior temporal sulcus (pcSTS), and posterior and anterior STS (pSTS and aSTS) were delineated for each individual with a semi-automated procedure. A probabilistic atlas was constructed to characterize their interindividual variability, revealing that the FSRs were highly variable in location and extent across subjects. The variability of FSRs was further quantified on both functional (i.e., face selectivity) and spatial (i.e., volume, location of peak activation, and anatomical location) features. Considerable interindividual variability and rightward asymmetry was found in all FSRs on these features. Taken together, our work presents the first effort to characterize comprehensively the variability of FSRs in a large sample of healthy subjects, and invites future work on the origin of the variability and its relation to individual differences in behavioral performance. Moreover, the probabilistic functional atlas will provide an adequate spatial reference for mapping the face network. Copyright © 2015 Elsevier Inc. All rights reserved.
    NeuroImage 03/2015; 113. DOI:10.1016/j.neuroimage.2015.03.010 · 6.36 Impact Factor
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