Emotion recognition in Parkinson’s disease: Static and dynamic factors.

Wasser, Cory I; Evans, Felicity A.; Kempnich, Clare; Andrews, Sophie C.; Thyagarajan, Dominic; Stout, Julie C.

doi:10.1037/neu0000400

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…We find a worse EFE recognition ability in the PD_CogDec group compared to HC both for static and dynamic EFE tasks, while the PD_CogInt group's performance was only worse than that of HC in the static task. This last result contradicts the findings of Wasser et al (2018), who did not find differences between PD and HC, either for static or dynamic tasks. However, they did observe a trend towards a worse performance in PD than in the HC group in both tasks, and the difference with our results could arise from the fact that these authors did not differentiate patients based on their cognitive status.…”

Section: Discussioncontrasting

confidence: 94%

“…Although in PD patients difficulties to recognize EFEs have been observed with both static and dynamic stimuli (Argaud et al, 2016;Kan, Kawamura, Hasegawa, Mochizuki, & Nakamura, 2002;McIntosh et al, 2015), only two studies have compared the patients' performance with both types of stimuli, with contradictory results. Whereas Wasser et al (2018) did not observe any differences between patients and controls for either static or dynamic stimuli, Kan et al (2002) reported that their patients' recognition of static stimuli was less accurate than their recognition of dynamic stimuli.…”

Section: Introductionmentioning

confidence: 77%

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Social Cognition and Cognitive Decline in Patients with Parkinson’s Disease

Alonso-Recio

Carvajal

Merino

et al. 2020

J Int Neuropsychol Soc

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Social cognition (SC) comprises an array of cognitive and affective abilities such as social perception, theory of mind, empathy, and social behavior. Previous studies have suggested the existence of deficits in several SC abilities in Parkinson disease (PD), although not unanimously. Objective: The aim of this study is to assess the SC construct and to explore its relationship with cognitive state in PD patients. Method: We compare 19 PD patients with cognitive decline, 27 cognitively preserved PD patients, and 29 healthy control (HC) individuals in social perception (static and dynamic emotional facial recognition), theory of mind, empathy, and social behavior tasks. We also assess processing speed, executive functions, memory, language, and visuospatial ability. Results: PD patients with cognitive decline perform worse than the other groups in both facial expression recognition tasks and theory of mind. Cognitively preserved PD patients only score worse than HCs in the static facial expression recognition task. We find several significant correlations between each of the SC deficits and diverse cognitive processes. Conclusions: The results indicate that some components of SC are impaired in PD patients. These problems seem to be related to a global cognitive decline rather than to specific deficits. Considering the importance of these abilities for social interaction, we suggest that SC be included in the assessment protocols in PD.

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Section: Discussioncontrasting

confidence: 94%

Section: Introductionmentioning

confidence: 77%

Social Cognition and Cognitive Decline in Patients with Parkinson’s Disease

Alonso-Recio

Carvajal

Merino

et al. 2020

J Int Neuropsychol Soc

View full text Add to dashboard Cite

show abstract

“…Although more and more researchers pay attention to multimodal emotion recognition technology, the overall emotion recognition rate is relatively low, which is not enough to be applied in real life. Therefore, multimodal emotion recognition will be the key research part in emotion computing [ 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism

Zhao

Chen

2021

Computational and Mathematical Methods in Medicine

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Due to the complexity of human emotions, there are some similarities between different emotion features. The existing emotion recognition method has the problems of difficulty of character extraction and low accuracy, so the bidirectional LSTM and attention mechanism based on the expression EEG multimodal emotion recognition method are proposed. Firstly, facial expression features are extracted based on the bilinear convolution network (BCN), and EEG signals are transformed into three groups of frequency band image sequences, and BCN is used to fuse the image features to obtain the multimodal emotion features of expression EEG. Then, through the LSTM with the attention mechanism, important data is extracted in the process of timing modeling, which effectively avoids the randomness or blindness of sampling methods. Finally, a feature fusion network with a three-layer bidirectional LSTM structure is designed to fuse the expression and EEG features, which is helpful to improve the accuracy of emotion recognition. On the MAHNOB-HCI and DEAP datasets, the proposed method is tested based on the MATLAB simulation platform. Experimental results show that the attention mechanism can enhance the visual effect of the image, and compared with other methods, the proposed method can extract emotion features from expressions and EEG signals more effectively, and the accuracy of emotion recognition is higher.

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“…There is substantial evidence that incorrect recognition and misinterpretations of emotional facial expressions is associated with impairments in social functioning, such as diminished social competence, poor social communication and inappropriate interpersonal behavior [3,4]. Impaired recognition of emotional facial expressions has been documented in various neurological patient groups, including traumatic brain injury (TBI, [5][6][7]), stroke [8][9][10], and various neurodegenerative disorders, such as Alzheimer's disease (AD, [11,12]), frontotemporal dementia (FTD, [13,14]), and Parkinson's disease (PD, [15,16]). At present, measurements of social cognition (i.e.…”

Section: Introductionmentioning

confidence: 99%

Comparing static and dynamic emotion recognition tests: Performance of healthy participants

et al. 2020

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Facial expressions have a communicatory function and the ability to read them is a prerequisite for understanding feelings and thoughts of other individuals. Impairments in recognition of facial emotional expressions are frequently found in patients with neurological conditions (e.g. stroke, traumatic brain injury, frontotemporal dementia). Hence, a standard neuropsychological assessment should include measurement of emotion recognition. However, there is debate regarding which tests are most suitable. The current study evaluates and compares three different emotion recognition tests. 84 healthy participants were included and assessed with three tests, in varying order: a. Ekman 60 Faces Test (FEEST) b. Emotion Recognition Task (ERT) c. Emotion Evaluation Test (EET). The tests differ in type of stimuli from static photographs (FEEST) to more dynamic stimuli in the form of morphed photographs (ERT) to videos (EET). Comparing performances on the three tests, the lowest total scores (67.3% correct answers) were found for the ERT. Significant, but moderate correlations were found between the total scores of the three tests, but nearly all correlations between the same emotions across different tests were not significant. Furthermore, we found cross-over effects of the FEEST and EET to the ERT; participants attained higher total scores on the ERT when another emotion recognition test had been administered beforehand. Moreover, the ERT proved to be sensitive to the effects of age and education. The present findings indicate that despite some overlap, each emotion recognition test measures a unique part of the construct. The ERT seemed to be the most difficult test: performances were lowest and influenced by differences in age and education and it was the only test that showed a learning effect after practice with other tests. This highlights the importance of appropriate norms.

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Emotion recognition in Parkinson’s disease: Static and dynamic factors.

Cited by 10 publications

References 30 publications

Social Cognition and Cognitive Decline in Patients with Parkinson’s Disease

Social Cognition and Cognitive Decline in Patients with Parkinson’s Disease

Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism

Comparing static and dynamic emotion recognition tests: Performance of healthy participants

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