Lower-Level Stimulus Features Strongly Influence Responses in the Fusiform Face Area

Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
Cerebral Cortex (Impact Factor: 8.67). 04/2010; 21(1):35-47. DOI: 10.1093/cercor/bhq050
Source: PubMed

ABSTRACT An intriguing region of human visual cortex (the fusiform face area; FFA) responds selectively to faces as a general higher-order stimulus category. However, the potential role of lower-order stimulus properties in FFA remains incompletely understood. To clarify those lower-level influences, we measured FFA responses to independent variation in 4 lower-level stimulus dimensions using standardized face stimuli and functional Magnetic Resonance Imaging (fMRI). These dimensions were size, position, contrast, and rotation in depth (viewpoint). We found that FFA responses were strongly influenced by variations in each of these image dimensions; that is, FFA responses were not "invariant" to any of them. Moreover, all FFA response functions were highly correlated with V1 responses (r = 0.95-0.99). As in V1, FFA responses could be accurately modeled as a combination of responses to 1) local contrast plus 2) the cortical magnification factor. In some measurements (e.g., face size or a combinations of multiple cues), the lower-level variations dominated the range of FFA responses. Manipulation of lower-level stimulus parameters could even change the category preference of FFA from "face selective" to "object selective." Altogether, these results emphasize that a significant portion of the FFA response reflects lower-level visual responses.

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Available from: Xiaomin Yue, Sep 27, 2015
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    • "Four images of human faces (see Figure 1A) were generated using FaceGen 3.4 (Singular Inversions, Canada), as described previously (Yue et al., 2011, 2013; Holt et al., 2014). All four faces (A, B, C and D) were male and caucasian, and achromatic (i.e., all color parameters were set to 0). "
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    ABSTRACT: Fear generalization is the production of fear responses to a stimulus that is similar – but not identical - to a threatening stimulus. Although prior studies have found that fear generalization magnitudes are qualitatively related to the degree of perceptual similarity to the threatening stimulus, the precise relationship between these two functions has not been measured systematically. Also, it remains unknown whether fear generalization mechanisms differ for social and non-social information. To examine these questions, we measured perceptual discrimination and fear generalization in the same subjects, using images of human faces and non-face control stimuli (“blobs”) that were perceptually matched to the faces. First, each subject’s ability to discriminate between pairs of faces or blobs was measured. Each subject then underwent a Pavlovian fear conditioning procedure, in which each of the paired stimuli were either followed (CS+) or not followed (CS-) by a shock. Skin conductance responses (SCRs) were also measured. Subjects were then presented with the CS+, CS- and five levels of a CS+-to-CS- morph continuum between the paired stimuli, based on individual discrimination thresholds. Finally, subjects rated the likelihood that each stimulus had been followed by a shock. Subjects showed both autonomic (SCR-based) and conscious (ratings-based) fear responses to morphs that they could not discriminate from the CS+ (generalization). For both faces and non-face objects, fear generalization was not found above discrimination thresholds. However, subjects exhibited greater fear generalization in the shock likelihood ratings compared to the SCRs, particularly for faces. These findings reveal that autonomic threat detection mechanisms in humans are highly sensitive to small perceptual differences between stimuli. Also, the conscious evaluation of threat shows broader generalization than autonomic responses, biased towards labeling a stimulus as threatening.
    Frontiers in Human Neuroscience 09/2014; 8:624. DOI:10.3389/fnhum.2014.00624 · 2.99 Impact Factor
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    • "In contrast, Yue and his colleagues reported that FFA produces neural activities that fit well with the model based on V1 function [26]. They analyzed neural responses along 4 meridians, including the ipsilateral horizontal positions. "
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    ABSTRACT: In human visual cortex, the primary visual cortex (V1) is considered to be essential for visual information processing; the fusiform face area (FFA) and parahippocampal place area (PPA) are considered as face-selective region and places-selective region, respectively. Recently, a functional magnetic resonance imaging (fMRI) study showed that the neural activity ratios between V1 and FFA were constant as eccentricities increasing in central visual field. However, in wide visual field, the neural activity relationships between V1 and FFA or V1 and PPA are still unclear. In this work, using fMRI and wide-view present system, we tried to address this issue by measuring neural activities in V1, FFA and PPA for the images of faces and houses aligning in 4 eccentricities and 4 meridians. Then, we further calculated ratio relative to V1 (RRV1) as comparing the neural responses amplitudes in FFA or PPA with those in V1. We found V1, FFA, and PPA showed significant different neural activities to faces and houses in 3 dimensions of eccentricity, meridian, and region. Most importantly, the RRV1s in FFA and PPA also exhibited significant differences in 3 dimensions. In the dimension of eccentricity, both FFA and PPA showed smaller RRV1s at central position than those at peripheral positions. In meridian dimension, both FFA and PPA showed larger RRV1s at upper vertical positions than those at lower vertical positions. In the dimension of region, FFA had larger RRV1s than PPA. We proposed that these differential RRV1s indicated FFA and PPA might have different processing strategies for encoding the wide field visual information from V1. These different processing strategies might depend on the retinal position at which faces or houses are typically observed in daily life. We posited a role of experience in shaping the information processing strategies in the ventral visual cortex.
    PLoS ONE 08/2013; 8(8):e72728. DOI:10.1371/journal.pone.0072728 · 3.23 Impact Factor
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    • "Additional details of the stimuli and scanning are described elsewhere (Nasr et al., 2011). Area LOC was also localized for each individual subject based on images of isolated objects versus scrambled objects (Grill-Spector et al., 2000; Kourtzi and Kanwisher, 2000; Yue et al., 2011). The V1 border was based on MR-based myelination differences (Hinds et al., 2008). "
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    ABSTRACT: Three cortical areas (Retro-Splenial Cortex (RSC), Transverse Occipital Sulcus (TOS) and Parahippocampal Place Area (PPA)) respond selectively to scenes. However,their wider role in spatial encoding and their functional connectivity remains unclear. Using fMRI, first we tested the responses of these areas during spatial comparison tasks using dot targets on white noise. Activity increased during task performance in both RSC and TOS, but not in PPA. However, the amplitude of task-driven activity and behavioral measures of task demand were correlated only in RSC. A control experiment showed that none of these areas were activated during a comparable shape comparison task. Secondly, we analyzed functional connectivity of these areas during the resting state. Results revealed a significant connection between RSC and frontal association areas (known to be involved in perceptual decision-making). In contrast, TOS showed functional connections dorsally with the Inferior Parietal Sulcus, and ventrally with the Lateral Occipital Complex - but not with RSC and/or frontal association areas. Moreover, RSC and TOS showed differentiable functional connections with the anterior-medial and posterior-lateral parts of PPA, respectively. These results suggest two parallel pathways for spatial encoding, including RSC and TOS respectively. Only the RSC network was involved in active spatial comparisons.
    NeuroImage 07/2013; 83. DOI:10.1016/j.neuroimage.2013.07.030 · 6.36 Impact Factor
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