Factors influencing spatial frequency extraction in faces: A review

Jeantet, Coline; Caharel, Stéphanie; Schwan, Raymund; Lighezzolo-Alnot, Joëlle; Laprévote, Vincent

doi:10.1016/j.neubiorev.2018.03.006

Cited by 23 publications

(20 citation statements)

References 196 publications

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“…It is therefore likely that amplitude differences in our P1 at least partly reflect sensitivity to low-level differences between faces and houses rather than face-categorization (e.g., luminance contrast, orientation, color, spatial frequency content). For instance, the P1 amplitude differs in infant responses to lower versus higher spatial frequencies (e.g., Norcia and Tyler, 1985 ), a property that differs between faces and houses ( Jeantet et al, 2018 ). Note that we opted not to control for such low-level differences but for keeping the stimuli to appear as realistic as possible.…”

Section: Discussionmentioning

confidence: 99%

Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Lorenzo

Boomen

Kemner

et al. 2020

Developmental Cognitive Neuroscience

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Highlights We report longitudinal ERP data of 80 infants in a face-discrimination task. P1, N290, Nc are all sensitive to faces in five-month-olds. P1, N290, Nc show equal face-categorization in infants tested longitudinally. N290 shows less variation in face-categorization trajectories than P1 or Nc. Visual ERPs increase in amplitude over infancy, but this is not face-specific.

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Section: Discussionmentioning

confidence: 99%

Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Lorenzo

Boomen

Kemner

et al. 2020

Developmental Cognitive Neuroscience

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“…Therefore, it is possible that some degree of size dependence is built into the mechanisms or processes of human visual recognition that mirror this natural coupling. Evidence for such a relationship has been found by numerous behavioural studies examining critical spatial frequency bands for the recognition of various objects and faces at different sizes (Chung, Legge, & Tjan, 2002; Chung & Tjan, 2009; Majaj, Pelli, Kurshan, & Palomares, 2002; Nasanen, 1999; Oruc & Barton, 2010; Oruc & Landy, 2009; Willenbockel et al., 2010; for a review, see Jeantet, Caharel, Schwan, Lighezzolo-Alnot, & Laprevote, 2018). These studies have consistently reported that observers utilize coarser features of an object at small sizes and finer details at large sizes.…”

Section: Introductionmentioning

confidence: 97%

Size Effects in the Recognition of Blurry Faces

Mousavi

Oruç

2020

Perception

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Spatial frequencies critical for recognition of faces are scale-dependent. Progressively coarser features of the face are utilized at smaller sizes, despite the availability of finer features. Blur removes fine details in an image, disrupting the finer features utilized for recognition at large sizes. At smaller sizes, observers utilize coarser features, and thus, recognition may be less impacted by blur. This coupling between size and critical spatial frequencies allows us to predict a regime in which observers tolerate blur better with decreasing image sizes within a range of moderate face sizes. We tested recognition of famous faces in four conditions: large-intact, small-intact, large-blurry, and small-blurry. Observers showed high recognition performance in both intact conditions. Blur significantly disrupted recognition, yet accuracy was significantly and consistently higher in the small-blurry compared to the large-blurry condition. These results are suggestive of an inherent scale-dependent mechanism that, at certain sizes, negatively impacts recognition of blurry images.

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“…For example, while identi cation of happy facial expressions relies on lower spatial frequency content, identi cation of sad facial expressions relies on higher spatial frequencies (Kumar & Srinivasan, 2011). Moreover, the exact range within lower or higher spatial frequencies that drive emotion identi cation and recognition varies between emotional expressions (Jeantet et al, 2018). However, as noted by Jeantet and colleagues, the common approach to understanding the relevance of the speci c spatial frequency content of faces may have several limitations.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, faces are ltered to contain a speci c range of spatial frequencies, which in turn affects the ecological validity of the results. Moreover, since studies vary widely in their ranges of spatial frequencies, their conclusions depend on what is de ned as 'higher' versus 'lower' spatial frequencies (Jeantet et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The Visual Features of Emotional Faces That Predict Forced Choice Selection of Faces Download Text Copy to Clipboard

Stuit¹,

Kootstra²,

Terburg³

et al. 2020

Preprint

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Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their visual features rather than in terms of the semantic labels (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the first selected face out of two simultaneously presented faces. In other words, we show which visual features predict selection between two faces. Interestingly, the identified features serve as better predictors than the semantic label of the expressions. We therefore propose that our modelling approach can further specify which visual features drive the behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.

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Factors influencing spatial frequency extraction in faces: A review

Cited by 23 publications

References 196 publications

Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Size Effects in the Recognition of Blurry Faces

The Visual Features of Emotional Faces That Predict Forced Choice Selection of Faces Download Text Copy to Clipboard

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Factors influencing spatial frequency extraction in faces: A review

Cited by 23 publications

References 196 publications

Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Size Effects in the Recognition of Blurry Faces

The Visual Features of Emotional Faces That Predict Forced Choice Selection of Faces Download Text&nbsp;Copy to Clipboard

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The Visual Features of Emotional Faces That Predict Forced Choice Selection of Faces Download Text Copy to Clipboard