Diminished facial EMG responses to disgusting scenes and happy and fearful faces in Huntington's disease

Kordsachia, Catarina C.; Labuschagne, Izelle; Andrews, Sophie C.; Stout, Julie C.

doi:10.1016/j.cortex.2018.05.019

Cited by 11 publications

(6 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social–cognitive function is the ability to interpret people’s emotions and intentions and to respond appropriately. While social–cognitive abnormalities are common in neurodegenerative diseases such as Huntington’s disease, Alzheimer’s disease, and frontotemporal dementia (Kordsachia, Labuschagne, Andrews, & Stout, 2018; Kordsachia, Labuschagne, & Stout, 2017, 2018; Phillips, Scott, Henry, Mowat, & Bell, 2010), considerable literature indicates that difficulties are also evident in healthy aging (Labuschagne, Pedder, Henry, Terrett, & Rendell, 2019; Moran, 2013). Moreover, because of their links with interpersonal functioning, social–cognitive abnormalities are a critical predictor of mental health and well-being in older adults (Phillips et al, 2010).…”

mentioning

confidence: 99%

Task characteristics influence facial emotion recognition age-effects: A meta-analytic review.

Hayes¹,

McLennan

Henry³

et al. 2020

Psychology and Aging

Self Cite

View full text Add to dashboard Cite

Relative to their young counterparts, older adults are poorer at recognizing facial expressions. A 2008 meta-analysis of 17 facial emotion recognition data sets showed that these age-related difficulties are not uniform. Rather, they are greatest for the emotions of anger, fear, and sadness, comparative with happiness and surprise, with no age-effect found for disgust. Since then, there have been many methodological advances in assessing emotion recognition. The current comprehensive meta-analysis systematically tested the influence of task characteristics (e.g., photographs vs. videos). The meta-analysis included 102 data sets that compared facial emotion recognition in older and young adult samples (N = 10,526). With task type combined, the pattern of age-effects across emotions was mostly consistent with the previous meta-analysis (i.e., largest age-effects for anger, fear, sadness; no effect for disgust). However, the magnitude and direction of age-effects were strongly influenced by elements of task design. Specifically, videos produced relatively moderate age-effects across all emotions, which indicates that older adults may not exhibit a positivity effect for facial emotion recognition. For disgust recognition, older adults demonstrated superior accuracy to young adults for the most common image set (Pictures of Facial Affect). However, they were poorer than young adults at recognizing this emotion for all other stimulus formats and image sets, which suggests that they do not retain disgust recognition. We discuss the implications that such diversity in the age-effects produced by different facial emotion recognition task designs has for understanding real-world deficits and task selection in future emotion recognition studies.

show abstract

mentioning

confidence: 99%

Task characteristics influence facial emotion recognition age-effects: A meta-analytic review.

Hayes¹,

McLennan

Henry³

et al. 2020

Psychology and Aging

Self Cite

View full text Add to dashboard Cite

show abstract

“…That EMG response is thought to be related to the emotional intensity rating scores participants attribute to the emotional stimuli 18 . Experimentally blocking facial muscles 19 – 21 or pathological conditions 22 – 24 that affect the muscles controlling facial expressions lead to a reduction of emotional recognition and emotional intensity ratings 11 , 12 . Congruent social information (positive or negative social labeling) improves facial mimicking responses, while incongruent social information decreases valence and arousal ratings 25 .…”

Section: Introductionmentioning

confidence: 99%

Simulated proximity enhances perceptual and physiological responses to emotional facial expressions

Bogdanova

Bogdanov

Miller

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Physical proximity is important in social interactions. Here, we assessed whether simulated physical proximity modulates the perceived intensity of facial emotional expressions and their associated physiological signatures during observation or imitation of these expressions. Forty-four healthy volunteers rated intensities of dynamic angry or happy facial expressions, presented at two simulated locations, proximal (0.5 m) and distant (3 m) from the participants. We tested whether simulated physical proximity affected the spontaneous (in the observation task) and voluntary (in the imitation task) physiological responses (activity of the corrugator supercilii face muscle and pupil diameter) as well as subsequent ratings of emotional intensity. Angry expressions provoked relative activation of the corrugator supercilii muscle and pupil dilation, whereas happy expressions induced a decrease in corrugator supercilii muscle activity. In proximal condition, these responses were enhanced during both observation and imitation of the facial expressions, and were accompanied by an increase in subsequent affective ratings. In addition, individual variations in condition related EMG activation during imitation of angry expressions predicted increase in subsequent emotional ratings. In sum, our results reveal novel insights about the impact of physical proximity in the perception of emotional expressions, with early proximity-induced enhancements of physiological responses followed by an increased intensity rating of facial emotional expressions.

show abstract

“…Moreso, the work of Ekman et al (1980) and Brown and Schwartz (Brown and Schwartz, 1980) are two of the few who showed that using facial electromyogram measurements of the zygomaticus muscle (zEMG) gave the most distinct indicator of valence compared to other facial muscles involved in the act of smiling. Multiple studies have suggested the relation between emotional states and facial electromyogram activity (Van Boxtel, 2010;Tan et al, 2011;Koelstra et al, 2012;Künecke et al, 2014;Kordsachia et al, 2018;Kayser et al, 2021;Shiva et al, 2021). Golland et al (2018) also showcased a consistent relationship between the emotional media viewed and the changes seen in the components of the facial electromyogram signal.…”

Section: Introductionmentioning

confidence: 85%

Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements

Branco

Ehteshami

Azgomi

et al. 2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

show abstract

Diminished facial EMG responses to disgusting scenes and happy and fearful faces in Huntington's disease

Cited by 11 publications

References 69 publications

Task characteristics influence facial emotion recognition age-effects: A meta-analytic review.

Task characteristics influence facial emotion recognition age-effects: A meta-analytic review.

Simulated proximity enhances perceptual and physiological responses to emotional facial expressions

Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements

Contact Info

Product

Resources

About