scholarly journals Facial mimicry, empathy, and emotion recognition: a meta-analysis of correlations

2020 ◽  
pp. 1-19
Author(s):  
Alison C. Holland ◽  
Garret O’Connell ◽  
Isabel Dziobek
2015 ◽  
Vol 5 (2) ◽  
pp. 154-162 ◽  
Author(s):  
Michael F. Wagner ◽  
Joel S. Milner ◽  
Randy J. McCarthy ◽  
Julie L. Crouch ◽  
Thomas R. McCanne ◽  
...  

2014 ◽  
Vol 26 (4pt1) ◽  
pp. 933-945 ◽  
Author(s):  
Leah M. Lozier ◽  
John W. Vanmeter ◽  
Abigail A. Marsh

AbstractAutism spectrum disorders (ASDs) are characterized by social impairments, including inappropriate responses to affective stimuli and nonverbal cues, which may extend to poor face-emotion recognition. However, the results of empirical studies of face-emotion recognition in individuals with ASD have yielded inconsistent findings that occlude understanding the role of face-emotion recognition deficits in the development of ASD. The goal of this meta-analysis was to address three as-yet unanswered questions. Are ASDs associated with consistent face-emotion recognition deficits? Do deficits generalize across multiple emotional expressions or are they limited to specific emotions? Do age or cognitive intelligence affect the magnitude of identified deficits? The results indicate that ASDs are associated with face-emotion recognition deficits across multiple expressions and that the magnitude of these deficits increases with age and cannot be accounted for by intelligence. These findings suggest that, whereas neurodevelopmental processes and social experience produce improvements in general face-emotion recognition abilities over time during typical development, children with ASD may experience disruptions in these processes, which suggested distributed functional impairment in the neural architecture that subserves face-emotion processing, an effect with downstream developmental consequences.


PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e57889 ◽  
Author(s):  
Wataru Sato ◽  
Tomomi Fujimura ◽  
Takanori Kochiyama ◽  
Naoto Suzuki

2021 ◽  
Author(s):  
Qijie Kuang ◽  
Yi Liu ◽  
Sumiao Zhou ◽  
Taiyong Bi ◽  
Lin Mi ◽  
...  

Abstract Our aim was to analyse the correlation between the fractional amplitude of low-frequency fluctuation (fALFF) and facial emotion recognition (FER) ability in patients with first-episode schizophrenia (FSZ). A total of 28 patients with FSZ and 33 healthy controls (HCs) completed visual search tasks for FER ability. Regions of interest (ROIs) related to facial emotion were obtained from a previous meta-analysis. Pearson correlation analysis was performed to understand the correlation between fALFF and FER ability. Our results indicated that the patients performed worse than the HCs in the accuracy performances of happy FER and fearful FER. The previous meta-analysis results showed that the brain regions related to FER included the bilateral amygdala (AMY)/hippocampus (HIP), right fusiform gyrus (FFG), and right supplementary motor area (SMA). Pearson correlation showed that the fALFF of the right FFG was associated with high-load fearful FER accuracy (r = -0.43, p = 0.022). Multiple regression analysis showed that the fALFF of the right FFG was an independent contributor to fearful FER accuracy. Our study indicates that FER ability is correlated with resting-state intrinsic activity in brain regions related to facial emotion, which may provide a reference for the study of FER in schizophrenia.


2019 ◽  
Vol 17 (3) ◽  
pp. 273-291 ◽  
Author(s):  
Agnieszka Landowska

Purpose The purpose of this paper is to explore uncertainty inherent in emotion recognition technologies and the consequences resulting from that phenomenon. Design/methodology/approach The paper is a general overview of the concept; however, it is based on a meta-analysis of multiple experimental and observational studies performed over the past couple of years. Findings The main finding of the paper might be summarized as follows: there is uncertainty inherent in emotion recognition technologies, and the phenomenon is not expressed enough, not addressed enough and unknown by the users of the technology. Practical implications Practical implications of the study are formulated as postulates for the developers, users and researchers dealing with the technologies of automatic emotion recognition. Social implications As technologies that recognize emotions are becoming more and more common, and perhaps more decisions influencing people lives are to come in the next decades, the trustworthiness of the technology is important from a scientific, practical and ethical point of view. Originality/value Studying uncertainty of emotion recognition technologies is a novel approach and is not explored from such a broad perspective before.


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