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.

14 Reads
    • "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. "
    [Show abstract] [Hide abstract]
    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; 77. DOI:10.1016/j.neuropsychologia.2015.09.021 · 3.30 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. "
    [Show abstract] [Hide abstract]
    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
  • Source
    • "Functional Magnetic Resonance Imaging (fMRI) studies have shown that these regions play a critical role in the recognition of facial identity. For instance, OFA and FFA fMRI activity is correlated with behavioral measures of face recognition ability (Yovel and Kanwisher, 2005; Kriegeskorte et al., 2007; Furl et al., 2011). In addition, brain injuries encompassing at least one of these regions often results in severe face recognition deficits (i.e., acquired prosopagnosia) (Barton, 2008; Rossion, 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The ability to identify faces is mediated by a network of cortical and subcortical brain regions in humans. It is still a matter of debate which regions represent the functional substrate of congenital prosopagnosia (CP), a condition characterized by a lifelong impairment in face recognition, and affecting around 2.5% of the general population. Here, we used functional Magnetic Resonance Imaging (fMRI) to measure neural responses to faces, objects, bodies, and body-parts in a group of seven CPs and ten healthy control participants. Using multi-voxel pattern analysis (MVPA) of the fMRI data we demonstrate that neural activity within the "core" (i.e., occipital face area and fusiform face area) and "extended" (i.e., anterior temporal cortex) face regions in CPs showed reduced discriminability between faces and objects. Reduced differentiation between faces and objects in CP was also seen in the right parahippocampal cortex. In contrast, discriminability between faces and bodies/body-parts and objects and bodies/body-parts across the ventral visual system was typical in CPs. In addition to MVPA analysis, we also ran traditional mass-univariate analysis, which failed to show any group differences in face and object discriminability. In sum, these findings demonstrate (i) face-object representations impairments in CP which encompass both the "core" and "extended" face regions, and (ii) superior power of MVPA in detecting group differences.
    Frontiers in Human Neuroscience 11/2014; 8. DOI:10.3389/fnhum.2014.00925 · 3.63 Impact Factor
Show more

Preview (2 Sources)

14 Reads
Available from