Rapid neural categorization of facelike objects predicts the perceptual awareness of a face (face pareidolia)

Rekow, Diane; Baudouin, Jean‐Yves; Brochard, Renaud; Rossion, Bruno; Leleu, Arnaud

doi:10.1016/j.cognition.2022.105016

“…4 ). In line with previous studies ( Liu-Shuang et al, 2015a , b ; Quek et al, 2018b ; Retter et al, 2020 ; Or et al, 2021 ; Rekow et al, 2022 ), the group level categorization response was most prominent over the bilateral occipito-temporal scalp electrodes ( Fig. 5 ).…”

Section: Resultssupporting

confidence: 91%

“…All observers showed periodic responses at the frequency of face presentation and its harmonics; all harmonic frequencies were aggregated to quantify the face-selective EEG response at the individual level (see Methods; Figure 4). In line with previous studies (e.g., Liu-Shuang et al, 2015b;Liu-Shuang et al, 2015a;Quek et al, 2018b;Retter et al, 2020;Rekow et al, 2022), the group level categorisation response was most prominent over the bilateral occipito-temporal scalp electrodes (Figure 5). We defined occipito-temporal ROIs for each individual based on their response to fullcontrast sequences.…”

Section: Face Categorization Response In Full Contrast Viewing Condit...supporting

confidence: 91%

Natural Contrast Statistics Facilitate Human Face Categorization

Liu‐Shuang¹,

Yang²,

Rossion

³

et al. 2022

eNeuro

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The ability to detect faces in the environment is of utmost ecological importance for human social adaptation. While face categorization is efficient, fast and robust to sensory degradation, it is massively impaired when the facial stimulus does not match the natural contrast statistics of this visual category, i.e., the typically experienced ordered alternation of relatively darker and lighter regions of the face. To clarify this phenomenon, we characterized the contribution of natural contrast statistics to face categorization. Specifically, 31 human adults viewed various natural images of non-face categories at a rate of 12 Hz, with highly variable images of faces occurring every eight stimuli (1.5 Hz). As in previous studies, neural responses at 1.5 Hz as measured with high-density EEG provided an objective neural index of face categorization. Here, when face images were shown in their naturally experienced contrast statistics, the 1.5 Hz face categorization response emerged over occipito-temporal electrodes at very low contrast (5.1%, or .009 root-mean-square, RMS, contrast), quickly reaching optimal amplitude at 22.6% of contrast (i.e., RMS contrast of .041). Despite contrast negation preserving an image's spectral and geometrical properties, negative contrast images required twice as much contrast to trigger a face categorization response, and three times as much to reach optimum. These observations characterize how the internally stored natural contrast statistics of the face category facilitate visual processing for the sake of fast and efficient face categorization. Significance statementHuman faces share a universal property: the strict alternation of contrast, with the darker main features against the more uniform, lighter skin. The ability to categorise faces depends critically on the presence of these natural contrast statistics in the input stimulus. However, it is not yet known how natural contrast statistics facilitate the visual processing leading to categorisation. Using frequency tagging and high-density electroencephalography, we show that access to internally stored natural statistics reduces the amount of sensory input necessary for human face categorization.

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“…All observers showed periodic responses at the frequency of face presentation and its harmonics; all harmonic frequencies were aggregated to quantify the face-selective EEG response at the individual level (see Methods; Figure 5). In line with previous studies (e.g., Liu-Shuang et al, 2015b;Liu-Shuang et al, 2015a;Quek et al, 2018b;Retter et al, 2020;Rekow et al, 2022), the group level categorisation response was most prominent over the bilateral occipito-temporal scalp electrodes (Figure 6). We defined occipito-temporal ROIs for each individual based on their response to full-contrast sequences.…”

Section: Face Categorization Response In Full Contrast Viewing Condit...supporting

confidence: 91%

Natural contrast statistics facilitate human face categorization

Liu‐Shuang¹,

Yang²,

Rossion³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The ability to detect faces in the environment is of utmost ecological importance for human social adaptation. While face categorization is efficient, fast and robust to sensory degradation, it is massively impaired when the facial stimulus does not match the natural contrast statistics of this visual category, i.e., the typically experienced ordered alternation of relatively darker and lighter regions of the face. To clarify this phenomenon, we characterized the contribution of natural contrast statistics to face categorization. Specifically, 31 human adults viewed various natural images of non-face categories at a rate of 12 Hz, with highly variable images of faces occurring every eight stimuli (1.5 Hz). As in previous studies, neural responses at 1.5 Hz as measured with high-density EEG provided an objective neural index of face categorization. Here, when face images were shown in their naturally experienced contrast statistics, the 1.5 Hz face categorization response emerged over occipito-temporal electrodes at very low contrast (5.1%, or .009 root-mean-square, RMS, contrast), quickly reaching optimal amplitude at 22.6% of contrast (i.e., RMS contrast of .041). Despite contrast negation preserving an image's spectral and geometrical properties, negative contrast images required twice as much contrast to trigger a face categorization response, and three times as much to reach optimum. These observations characterize how the internally stored natural contrast statistics of the face category facilitate visual processing for the sake of fast and efficient face categorization.

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“…The present results point toward the promise of probing creativity through the exploration of the neural dynamics associated with pareidolia. Whereas a few studies have examined face pareidolia detection ( Liu et al., 2014 ; Rekow et al., 2022 ; Wardle et al., 2020 ), none have used fractal visual stimuli with open-ended designs. Another promising avenue for further research is designing methods to measure the properties of pareidolic percepts (e.g., richness, diversity) in order to strengthen the theoretical link between creativity and pareidolia.…”

Section: Discussionmentioning

confidence: 99%