Time course of spatial frequency integration in face perception: An ERP study

Jeantet, Coline; Laprévote, Vincent; Schwan, Raymund; Schwitzer, Thomas; Maillard, Louis; Lighezzolo-Alnot, Joëlle; Caharel, Stéphanie

doi:10.1016/j.ijpsycho.2019.07.001

Cited by 19 publications

(26 citation statements)

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“…However, peak-to peak amplitudes were greater in HSF than in LSF and BSF. This latest result corroborates other recent findings on passive viewing (Obayashi et al, 2009;Mares et al, 2018), categorization (Jeantet et al, 2019) or detection tasks (Tian et al, 2018) and are in line with the coarse-to-fine integration for visual recognition and with Bar's model. In this framework, HSF would be preferentially used at later stages of visual recognition, for converging to a single percept.…”

Section: Early Sensory Responsesupporting

confidence: 92%

“…This effect is surprising regarding our theoretical framework but the literature on this topic is heterogeneous. Indeed, it has been shown either shorter P100 latencies for LSF than HSF (Vlamings et al, 2009;Peters and Kemner, 2017) or no difference (Jeantet et al, 2019, for parieto-occipital channels) or, similarly to our results, shorter latencies for HSF than LSF (Obayashi et al, 2009;Jeantet et al, 2019, for occipital channels). However, similar or shorter latencies for the P100 in HSF compared to LSF does not rule out Bar's model.…”

Section: Early Sensory Responsesupporting

confidence: 87%

“…It has been suggested that the negative peak for HSF would reflect the parvocellular activity while the positive peak for LSF would reflect the magnocellular activity (Ellemberg et al, 2002 ). Larger P100 amplitude for LSF relative to HSF was found for faces in a gender categorization task (Jeantet et al, 2019 ), in a passive viewing task (Obayashi et al, 2009 ), and in a task of valence categorization during a rapid serial visual presentation (Tian et al, 2018 ), all including adults. Nevertheless, results are contradictory in the literature.…”

Section: Discussionmentioning

confidence: 99%

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The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation

Lacroix

Harquel

Mermillod

et al. 2022

Front. Hum. Neurosci.

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Visual processing is thought to function in a coarse-to-fine manner. Low spatial frequencies (LSF), conveying coarse information, would be processed early to generate predictions. These LSF-based predictions would facilitate the further integration of high spatial frequencies (HSF), conveying fine details. The predictive role of LSF might be crucial in automatic face processing, where high performance could be explained by an accurate selection of clues in early processing. In the present study, we used a visual Mismatch Negativity (vMMN) paradigm by presenting an unfiltered face as standard stimulus, and the same face filtered in LSF or HSF as deviant, to investigate the predictive role of LSF vs. HSF during automatic face processing. If LSF are critical for predictions, we hypothesize that LSF deviants would elicit less prediction error (i.e., reduced mismatch responses) than HSF deviants. Results show that both LSF and HSF deviants elicited a mismatch response compared with their equivalent in an equiprobable sequence. However, in line with our hypothesis, LSF deviants evoke significantly reduced mismatch responses compared to HSF deviants, particularly at later stages. The difference in mismatch between HSF and LSF conditions involves posterior areas and right fusiform gyrus. Overall, our findings suggest a predictive role of LSF during automatic face processing and a critical involvement of HSF in the fusiform during the conscious detection of changes in faces.

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Section: Early Sensory Responsesupporting

confidence: 92%

Section: Early Sensory Responsesupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

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The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation

Lacroix

Harquel

Mermillod

et al. 2022

Front. Hum. Neurosci.

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“…Evidence from behavioral experiments and neuroscience studies suggests that face perception is hierarchical, and involves multiple brain circuits (Bruce & Young, 1986; Calder & Young, 2005; Haxby, Hoffman, & Gobbini, 2000; Kanwisher, McDermott, & Chun, 1997; Maurer, Le Grand, & Mondloch, 2002; Xu, Dayan, Lipkin, & Qian, 2008). Each circuit may be specialized to process specific aspects of facial characteristics, including spatial frequencies (SFs; Badcock, Whitworth, Badcock, & Lovegrove, 1990; DeValois, Albrecht, & Thorell, 1982; Goffaux, Gauthier, & Rossion, 2003; Goffaux & Rossion, 2006; Halit, de Haan, Schyns, & Johnson, 2006; Jeantet et al, 2019; Zhang & Li, 2019). For example, the amygdala, an important subcortical region for emotion perception, favors low spatial frequency (LSF) information (Said, Baron, & Todorov, 2009); while the fusiform face area, one of the core regions for face perception, receives visual inputs at various SFs (Gauthier, Curby, Skudlarski, & Epstein, 2005).…”

mentioning

confidence: 99%

“…High and low SFs played different roles in configural and local processing in terms of processing time and tasks (e.g., identity, facial expressions, and gender; Aguado, Serrano‐Pedraza, Rodríguez, & Román, 2010; Kumar & Srinivasan, 2011; Rhodes, Peters, Lee, Morrone, & Burr, 2005; Russell, 2003; Vuilleumier, Armony, Driver, & Dolan, 2003; Xu, Liu, Yuan, & Lin, 2015). However, it has been suggested that the SF channel or its combinations that are activated may be dependent on the type of face perception (Jeantet et al, 2019) and tasks (Oruç, Balas, & Landy, 2019).…”

mentioning

confidence: 99%

Perception of attractive and unattractive face groups is driven by distinct spatial frequencies

et al. 2020

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Our visual system is able to extract information on facial attractiveness from groups of faces that contain both coarse and detailed information. This raises the question: What information is extracted from a face group? Is the attractiveness perception of multiple faces driven by high or low spatial frequency that can highlight the local or global information of the faces, respectively? In the first experiment, we adapted participants to four unattractive faces with full bandwidth (FB), high spatial frequency (HSF), and low spatial frequency (LSF). We observed significant aftereffects in the HSF faces adaptation condition, which suggests that the perception of multiple unattractive faces is largely driven by HSF information. In the second experiment, we found a similar but different pattern in the directrating tasks, suggesting distinct perception mechanisms in unattractive versus attractive faces. In the third experiment, both the adaptation and direct-rating paradigms suggested that perception of multiple attractive faces is largely driven by LSF information. Overall, results from the three experiments together found that perception of multiple attractive and unattractive faces depends on visual information from different spatial frequencies, suggesting distinctive mechanisms in processing attractive and unattractive groups of faces.

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Reduced spatial frequency differentiation and sex‐related specificities in fearful face detection in autism: Insights from EEG and the predictive brain model

Lacroix,

Harquel,

Barbosa

et al. 2024

Autism Research

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Face processing relies on predictive processes driven by low spatial frequencies (LSF) that convey coarse information prior to fine information conveyed by high spatial frequencies. However, autistic individuals might have atypical predictive processes, contributing to facial processing difficulties. This may be more normalized in autistic females, who often exhibit better socio‐communicational abilities than males. We hypothesized that autistic females would display a more typical coarse‐to‐fine processing for socio‐emotional stimuli compared to autistic males. To test this hypothesis, we asked adult participants (44 autistic, 51 non‐autistic) to detect fearful faces among neutral faces, filtered in two orders: from coarse‐to‐fine (CtF) and from fine‐to‐coarse (FtC). Results show lower d’ values and longer reaction times for fearful detection in autism compared to non‐autistic (NA) individuals, regardless of the filtering order. Both groups presented shorter P100 latency after CtF compared to FtC, and larger amplitude for N170 after FtC compared to CtF. However, autistic participants presented a reduced difference in source activity between CtF and FtC in the fusiform. There was also a more spatially spread activation pattern in autistic females compared to NA females. Finally, females had faster P100 and N170 latencies, as well as larger occipital activation for FtC sequences than males, irrespective of the group. Overall, the results do not suggest impaired predictive processes from LSF in autism despite behavioral differences in fear detection. However, they do indicate reduced brain modulation by spatial frequency in autism. In addition, the findings highlight sex differences that warrant consideration in understanding autistic females.

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Time course of spatial frequency integration in face perception: An ERP study

Cited by 19 publications

References 100 publications

The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation

The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation

Perception of attractive and unattractive face groups is driven by distinct spatial frequencies

Reduced spatial frequency differentiation and sex‐related specificities in fearful face detection in autism: Insights from EEG and the predictive brain model

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