Objective: We explored the relationship between a visual scanning strategy and a facial emotion recognition deficit in Parkinson's disease (PD).
Method: Thirty nondemented PD patients (balanced for symptom side at onset) and 20 age, education and gender-matched healthy controls (HC) were enrolled. The PD group underwent a comprehensive neuropsychological battery also exploring the executive functions. In both groups, eye movements were recorded while subjects categorized facial emotion from Ekman's 60-faces test. We were particularly interested in the location of fixations on facial pictures (top vs. bottom) and in emotional valence (positive vs. negative). We also compared performance of the two groups on a verbal emotion attribution task.
Results: Compared to HC, PD patients performed worse on visual recognition of negative emotions such as anger, fear, and sadness (where the upper part of the face is more informative than the lower part); the two groups did not differ on the verbal emotion attribution task. HC modified their visual scanning strategy (both number and overall time duration of fixations) according to the valence of the emotion; by contrast, PD showed the same pattern regardless of the valence. In the PD group, accuracy in the visual recognition of negative emotions and fixation pattern correlated with performance on tasks exploring executive functions; however, no associations were observed with severity of motor state.
Conclusions: Our results suggest that visual scanning strategy contributes significantly to the facial emotion recognition deficit of PD patients, especially at a "high level" related to cognitive control of eye movements. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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http://dx.doi.org/10.1037/neu0000802 | DOI Listing |
Front Comput Neurosci
December 2024
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Facial emotion recognition (FER) can serve as a valuable tool for assessing emotional states, which are often linked to mental health. However, mental health encompasses a broad range of factors that go beyond facial expressions. While FER provides insights into certain aspects of emotional well-being, it can be used in conjunction with other assessments to form a more comprehensive understanding of an individual's mental health.
View Article and Find Full Text PDFFront Psychol
December 2024
Center for Cognitive Neuroscience, Duke University, Durham, NC, United States.
Effective emotion regulation is critical for maintaining emotional health in the face of adverse events that accumulate over the lifespan. These abilities are thought to be generally maintained in older adults, accompanied by the emergence of attentional biases to positive information. Such age-related positivity biases, however, are not always reported and may be moderated by individual differences in affective vulnerabilities and competencies, such as those related to dispositional negative affect and emotion regulation styles.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired reasoning. It is the leading cause of dementia in older adults, marked by the pathological accumulation of amyloid-beta plaques and neurofibrillary tangles. These pathological changes lead to widespread neuronal damage, significantly impacting daily functioning and quality of life.
View Article and Find Full Text PDFBMC Public Health
December 2024
Bengbu Medical University, School of Health Management, Bengbu, China.
Objective: To explore the health information avoidance behaviors and influencing factors of cancer patients, and to construct a structural equation model to analyze the mediating roles of self-efficacy and negative emotions in the process of generating health information avoidance behaviors of cancer patients.
Methods: A face-to-face electronic questionnaire was used to collect data. Applying a chi-square test and multivariate logistic regression model to analyze the role of different socio-demographic factors in influencing health information avoidance behavior of cancer patients; applying structural equation modeling to analyze the role mechanism of health information avoidance behavior of cancer patients.
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