Strategy Shift Toward Lower Spatial Frequencies in the Recognition of Dynamic Facial Expressions of Basic Emotions: When It Moves It Is Different

Plouffe-Demers, Marie-Pier; Fiset, Daniel; Saumure, Camille; Duncan, Justin; Blais, Caroline

doi:10.3389/fpsyg.2019.01563

Cited by 13 publications

(15 citation statements)

References 94 publications

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“…Considering the broad literature supporting the advantage of dynamic facial expression over static ones (e.g. [50,51]) and the shift toward lower SFs in their processing, it would be interesting to replicate these ndings using dynamic pain facial expression.…”

Section: Discussionmentioning

confidence: 96%

“…Hence, a more ecological way of investigating pain facial expression recognition would be to use dynamic stimuli. Interestingly, it has been shown that a slight shift toward lower SFs occurs for dynamic expressions (i.e anger, disgust, fear, joy, sadness, surprise) in comparison with static ones [50]. These authors suggest that it is the presence of motion in general that causes this shift in SFs and not motion information per se, since a shift to LSFs is also observed when frames of dynamic facial expressions are shu ed.…”

Section: Discussionmentioning

confidence: 98%

“…The sample size for all experiments was chosen based on similar studies (between 3 and 28; e.g. [28,35,50]). According to recent studies using the SF Bubble's method [29,33], the results obtained with this method are generally very robust since they are based on many trials for a single task.…”

Section: Participantsmentioning

confidence: 99%

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The Role of Spatial Frequencies for Facial Pain Categorization

Charbonneau

Guérette

Cormier

et al. 2021

Preprint

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Studies on low-level visual information underlying pain categorization have led to inconsistent findings. Some are showing an advantage for low spatial frequency information (SFs) and others a preponderance of mid SFs. This study aims to clarify this gap in knowledge since these results have different theoretical and practical implications, such as how far away an observer can be in order to categorize pain. This study addresses this question by using two complementary methods: a data-driven method without a priori about the most useful SFs for pain recognition and a more ecological method that simulates the distance of stimuli presentation. We reveal a broad range of important SF for pain recognition starting from low to relatively high SFs and showed that performance is optimal in a short to medium distance (1.2 to 4.8 meters) but declines significantly when mid SFs are no longer available. This study reconciles previous results that show an advantage of LSFs over HSFs when using arbitrary cutoffs, but above all reveal the prominent role of mid-SFs for pain recognition across two experimental tasks.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 98%

See 1 more Smart Citation

The Role of Spatial Frequencies for Facial Pain Categorization

Charbonneau

Guérette

Cormier

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Interestingly, it has been shown that a slight shift toward lower SFs occurs for dynamic expressions (i.e. anger, disgust, fear, joy, sadness, surprise) in comparison with static ones 57 . These authors suggest that it is the presence of motion in general that causes this shift in SFs and not motion information per se , since a shift to LSFs is also observed when frames of dynamic facial expressions are shuffled.…”

Section: Discussionmentioning

confidence: 99%

The role of spatial frequencies for facial pain categorization

Charbonneau

Guérette

Cormier

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Studies on low-level visual information underlying pain categorization have led to inconsistent findings. Some show an advantage for low spatial frequency information (SFs) and others a preponderance of mid SFs. This study aims to clarify this gap in knowledge since these results have different theoretical and practical implications, such as how far away an observer can be in order to categorize pain. This study addresses this question by using two complementary methods: a data-driven method without a priori expectations about the most useful SFs for pain recognition and a more ecological method that simulates the distance of stimuli presentation. We reveal a broad range of important SFs for pain recognition starting from low to relatively high SFs and showed that performance is optimal in a short to medium distance (1.2–4.8 m) but declines significantly when mid SFs are no longer available. This study reconciles previous results that show an advantage of LSFs over HSFs when using arbitrary cutoffs, but above all reveal the prominent role of mid-SFs for pain recognition across two complementary experimental tasks.

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“…To our knowledge, no prior studies used EEG to study the oscillatory basis of dynamic FE processing. Two behavioral studies compared dynamic FEs to static FEs [ 30 , 31 ], revealing stronger perception and emotional responses to dynamic FEs as compared to static FEs. And there were several studies investigating dynamic FEs with EEG, but focusing on event-related potentials [ 32 – 35 ].…”

Section: Introductionmentioning

confidence: 99%

Event-related EEG oscillatory responses elicited by dynamic facial expression

et al. 2021

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Background Recognition of facial expressions (FEs) plays a crucial role in social interactions. Most studies on FE recognition use static (image) stimuli, even though real-life FEs are dynamic. FE processing is complex and multifaceted, and its neural correlates remain unclear. Transitioning from static to dynamic FE stimuli might help disentangle the neural oscillatory mechanisms underlying face processing and recognition of emotion expression. To our knowledge, we here present the first time–frequency exploration of oscillatory brain mechanisms underlying the processing of dynamic FEs. Results Videos of joyful, fearful, and neutral dynamic facial expressions were presented to 18 included healthy young adults. We analyzed event-related activity in electroencephalography (EEG) data, focusing on the delta, theta, and alpha-band oscillations. Since the videos involved a transition from neutral to emotional expressions (onset around 500 ms), we identified time windows that might correspond to face perception initially (time window 1; first TW), and emotion expression recognition subsequently (around 1000 ms; second TW). First TW showed increased power and phase-locking values for all frequency bands. In the first TW, power and phase-locking values were higher in the delta and theta bands for emotional FEs as compared to neutral FEs, thus potentially serving as a marker for emotion recognition in dynamic face processing. Conclusions Our time–frequency exploration revealed consistent oscillatory responses to complex, dynamic, ecologically meaningful FE stimuli. We conclude that while dynamic FE processing involves complex network dynamics, dynamic FEs were successfully used to reveal temporally separate oscillation responses related to face processing and subsequently emotion expression recognition.

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Strategy Shift Toward Lower Spatial Frequencies in the Recognition of Dynamic Facial Expressions of Basic Emotions: When It Moves It Is Different

Cited by 13 publications

References 94 publications

The Role of Spatial Frequencies for Facial Pain Categorization

The Role of Spatial Frequencies for Facial Pain Categorization

The role of spatial frequencies for facial pain categorization

Event-related EEG oscillatory responses elicited by dynamic facial expression

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