Facial Emotion Recognition and Executive Functions in Fibromyalgia

Pain Medicine ◽  
2021 ◽  
Author(s):  
Cristina Muñoz Ladrón de Guevara ◽  
Gustavo A Reyes del Paso ◽  
María José Fernández-Serrano ◽  
Stefan Duschek

Abstract Objective The ability to accurately identify facial expressions of emotions is crucial in human interaction. While a previous study suggested deficient emotional face recognition in patients with fibromyalgia, not much is known about the origin of this impairment. Against this background, this study investigated the role of executive functions. Executive functions refer to cognitive control mechanisms enabling implementation and coordination of basic mental operations. Deficits in this domain are prevalent in fibromyalgia. Methods Fifty-two fibromyalgia patients and thirty-two healthy individuals completed the Ekman-60 Faces Test, which requires classification of facial displays of happiness, sadness, anger, fear, surprise and disgust. They also completed eight tasks assessing the executive function components of shifting, updating and inhibition. Effects of comorbid depression and anxiety disorders, and medication use, were tested in stratified analyses of patient subgroups. Results Patients made more errors overall than controls in classifying the emotional expressions. Moreover, their recognition accuracy correlated positively with performance on most of the executive function tasks. Emotion recognition did not vary as a function of comorbid psychiatric disorders or medication use. Conclusions The study supports impaired facial emotion recognition in fibromyalgia, which may contribute to the interaction problems and poor social functioning characterizing this condition. Facial emotion recognition is regarded as a complex process, which may be particularly reliant on efficient coordination of various basic operations by executive functions. As such, the correlations between cognitive task performance and recognition accuracy suggest that deficits in higher cognitive functions underlie impaired emotional communication in fibromyalgia.

2015 ◽  
Vol 5 (2) ◽  
pp. 154-162 ◽  
Author(s):  
Michael F. Wagner ◽  
Joel S. Milner ◽  
Randy J. McCarthy ◽  
Julie L. Crouch ◽  
Thomas R. McCanne ◽  
...  

2013 ◽  
Vol 16 ◽  
Author(s):  
Esther Lázaro ◽  
Imanol Amayra ◽  
Juan Francisco López-Paz ◽  
Amaia Jometón ◽  
Natalia Martín ◽  
...  

AbstractThe assessment of facial expression is an important aspect of a clinical neurological examination, both as an indicator of a mood disorder and as a sign of neurological damage. To date, although studies have been conducted on certain psychosocial aspects of myasthenia, such as quality of life and anxiety, and on neuropsychological aspects such as memory, no studies have directly assessed facial emotion recognition accuracy. The aim of this study was to assess the facial emotion recognition accuracy (fear, surprise, sadness, happiness, anger, and disgust), empathy, and reaction time of patients with myasthenia. Thirty-five patients with myasthenia and 36 healthy controls were tested for their ability to differentiate emotional facial expressions. Participants were matched with respect to age, gender, and education level. Their ability to differentiate emotional facial expressions was evaluated using the computer-based program Feel Test. The data showed that myasthenic patients scored significantly lower (p < 0.05) than healthy controls in the total Feel score, fear, surprise, and higher reaction time. The findings suggest that the ability to recognize facial affect may be reduced in individuals with myasthenia.


Author(s):  
Suchitra Saxena ◽  
Shikha Tripathi ◽  
Sudarshan Tsb

This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.


2020 ◽  
Author(s):  
Nazire Duran ◽  
ANTHONY P. ATKINSON

Certain facial features provide useful information for recognition of facial expressions. In two experiments, we investigated whether foveating informative features of briefly presented expressions improves recognition accuracy and whether these features are targeted reflexively when not foveated. Angry, fearful, surprised, and sad or disgusted expressions were presented briefly at locations which would ensure foveation of specific features. Foveating the mouth of fearful, surprised and disgusted expressions improved emotion recognition compared to foveating an eye or cheek or the central brow. Foveating the brow lead to equivocal results in anger recognition across the two experiments, which might be due to the different combination of emotions used. There was no consistent evidence suggesting that reflexive first saccades targeted emotion-relevant features; instead, they targeted the closest feature to initial fixation. In a third experiment, angry, fearful, surprised and disgusted expressions were presented for 5 seconds. Duration of task-related fixations in the eyes, brow, nose and mouth regions was modulated by the presented expression. Moreover, longer fixation at the mouth positively correlated with anger and disgust accuracy both when these expressions were freely viewed (Experiment 3) and when briefly presented at the mouth (Experiment 2). Finally, an overall preference to fixate the mouth across all expressions correlated positively with anger and disgust accuracy. These findings suggest that foveal processing of informative features is functional/contributory to emotion recognition, but they are not automatically sought out when not foveated, and that facial emotion recognition performance is related to idiosyncratic gaze behaviour.


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260814
Author(s):  
Nazire Duran ◽  
Anthony P. Atkinson

Certain facial features provide useful information for recognition of facial expressions. In two experiments, we investigated whether foveating informative features of briefly presented expressions improves recognition accuracy and whether these features are targeted reflexively when not foveated. Angry, fearful, surprised, and sad or disgusted expressions were presented briefly at locations which would ensure foveation of specific features. Foveating the mouth of fearful, surprised and disgusted expressions improved emotion recognition compared to foveating an eye or cheek or the central brow. Foveating the brow led to equivocal results in anger recognition across the two experiments, which might be due to the different combination of emotions used. There was no consistent evidence suggesting that reflexive first saccades targeted emotion-relevant features; instead, they targeted the closest feature to initial fixation. In a third experiment, angry, fearful, surprised and disgusted expressions were presented for 5 seconds. Duration of task-related fixations in the eyes, brow, nose and mouth regions was modulated by the presented expression. Moreover, longer fixation at the mouth positively correlated with anger and disgust accuracy both when these expressions were freely viewed (Experiment 2b) and when briefly presented at the mouth (Experiment 2a). Finally, an overall preference to fixate the mouth across all expressions correlated positively with anger and disgust accuracy. These findings suggest that foveal processing of informative features is functional/contributory to emotion recognition, but they are not automatically sought out when not foveated, and that facial emotion recognition performance is related to idiosyncratic gaze behaviour.


2020 ◽  
Vol 11 ◽  
Author(s):  
Katie Moraes de Almondes ◽  
Francisco Wilson Nogueira Holanda Júnior ◽  
Maria Emanuela Matos Leonardo ◽  
Nelson Torro Alves

Author(s):  
Anouck I. Staff ◽  
Marjolein Luman ◽  
Saskia van der Oord ◽  
Catharina E. Bergwerff ◽  
Barbara J. van den Hoofdakker ◽  
...  

AbstractChildren with attention-deficit/hyperactivity disorder (ADHD) symptoms often experience social and emotional problems. Impaired facial emotion recognition has been suggested as a possible underlying mechanism, although impairments may depend on the type and intensity of emotions. We investigated facial emotion recognition in children with (subthreshold) ADHD and controls using a novel task with children’s faces of emotional expressions varying in type and intensity. We further investigated associations between emotion recognition accuracy and social and emotional problems in the ADHD group. 83 children displaying ADHD symptoms and 30 controls (6–12 years) completed the Morphed Facial Emotion Recognition Task (MFERT). The MFERT assesses emotion recognition accuracy on four emotions using five expression intensity levels. Teachers and parents rated social and emotional problems on the Strengths and Difficulties Questionnaire. Repeated measures analysis of variance revealed that the ADHD group showed poorer emotion recognition accuracy compared to controls across emotions (small effect). The significant group by expression intensity interaction (small effect) showed that the increase in accuracy with increasing expression intensity was smaller in the ADHD group compared to controls. Multiple regression analyses within the ADHD group showed that emotion recognition accuracy was inversely related to social and emotional problems, but not prosocial behavior. Not only children with an ADHD diagnosis, but also children with subthreshold ADHD experience impairments in facial emotion recognition. This impairment is predictive for social and emotional problems, which may suggest that emotion recognition may contribute to the development of social and emotional problems in these children.


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