Specific facial emotion recognition deficits across the course of psychosis: A comparison of individuals with low-risk, high-risk, first-episode psychosis and multi-episode schizophrenia-spectrum disorders

Pena-Garijo, Josep; Lacruz, Maria; Masanet, Maria Jose; Palop-Grau, Ana; Plaza, Rut; Hernandez-Merino, Ana; Edo-Villamón, Silvia; Valllina, Oscar

doi:10.1016/j.psychres.2022.115029

Cited by 7 publications

(2 citation statements)

References 73 publications

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“…Emotion recognition is vital in fields like marketing, human-robot interaction, healthcare, mental health monitoring, and security [1]. In healthcare, affective computing is crucial for understanding various neurological disorders, including sleep disorders [2], schizophrenia [3], sleep quality assessment [4], autism spectrum disorder [5,6], and Parkinson's disease [7][8][9]. Emotions are also significant in identifying physiological states like fatigue, drowsiness, depression, and pain [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study

Kargarandehkordi,

Kaisti,

Washington

2024

Applied Sciences

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Emotion recognition, a rapidly evolving domain in digital health, has witnessed significant transformations with the advent of personalized approaches and advanced machine learning (ML) techniques. These advancements have shifted the focus from traditional, generalized models to more individual-centric methodologies, underscoring the importance of understanding and catering to the unique emotional expressions of individuals. Our study delves into the concept of model personalization in emotion recognition, moving away from the one-size-fits-all approach. We conducted a series of experiments using the Emognition dataset, comprising physiological and video data of human subjects expressing various emotions, to investigate this personalized approach to affective computing. For the 10 individuals in the dataset with a sufficient representation of at least two ground truth emotion labels, we trained a personalized version of three classical ML models (k-nearest neighbors, random forests, and a dense neural network) on a set of 51 features extracted from each video frame. We ensured that all the frames used to train the models occurred earlier in the video than the frames used to test the model. We measured the importance of each facial feature for all the personalized models and observed differing ranked lists of the top features across the subjects, highlighting the need for model personalization. We then compared the personalized models against a generalized model trained using data from all 10 subjects. The mean F1 scores for the personalized models, specifically for the k-nearest neighbors, random forest, and dense neural network, were 90.48%, 92.66%, and 86.40%, respectively. In contrast, the mean F1 scores for the generic models, using the same ML techniques, were 88.55%, 91.78% and 80.42%, respectively, when trained on data from various human subjects and evaluated using the same test set. The personalized models outperformed the generalized models for 7 out of the 10 subjects. The PCA analyses on the remaining three subjects revealed relatively little facial configuration differences across the emotion labels within each subject, suggesting that personalized ML will fail when the variation among data points within a subject’s data is too low. This preliminary feasibility study demonstrates the potential as well as the ongoing challenges with implementing personalized models which predict highly subjective outcomes like emotion.

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Section: Introductionmentioning

confidence: 99%

Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study

Kargarandehkordi,

Kaisti,

Washington

2024

Applied Sciences

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“…Furthermore, a reduced ability to recognize neutral emotions and higher accuracy in identifying fearful emotions were predictors of the transition to a psychotic disorder in ultra-high-risk participants (18). Pena-Garijo et al (19) reported the results of an interesting study in which FER was conducted on HC who were at low or high risk for psychosis, a group with first-episode psychosis, and a group with multi-episode schizophrenia spectrum disorders. They reported that FER is early impaired in high-risk individuals and increases along the psychosis continuum.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion-recognition deficits in patients with schizophrenia and unaffected first-degree relatives

Bae,

Cho,

Won

2024

Front. Psychiatry

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IntroductionThis study aimed to determine trait- and state-dependent markers of schizophrenia by investigating facial emotion-recognition (FER) deficits in remitted patients with schizophrenia and their first-degree relatives (FR).MethodsThree groups were included: the Schizophrenia group (n=66), their unaffected FR group (n=40), and healthy controls (n=50) who were matched for age, sex, and years of education. A facial-labeling task was used to examine FER deficits using the following eight standardized expressions: happy, fearful, disgusted, angry, sad, contemptuous, surprised, and neutral.ResultsThere was a poorer accuracy in the recognition of sadness and anger in the Schizophrenia group as well as in contempt in both the Schizophrenia and FR groups compared with healthy controls. The response times for the recognition of contempt, sadness, and neutral emotion were delayed in the Schizophrenia group and those for fear were delayed in the Schizophrenia and FR groups compared with healthy controls.ConclusionConcerning the accuracy in FER, sadness and anger can be considered state-dependent markers of remitted schizophrenia, and contempt is a trait-dependent marker of schizophrenia. Similarly, for response times in FER, contempt, sadness, and neutral emotion can be considered state-dependent markers of remitted schizophrenia, while fear is considered a trait-dependent marker of schizophrenia. These findings may contribute to the early diagnosis of schizophrenia and the development of relevant therapeutic interventions.

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The Emotion Authenticity Recognition (EAR) test: normative data of an innovative test using dynamic emotional stimuli to evaluate the ability to recognize the authenticity of emotions expressed by faces

Scarpazza,

Gramegna,

Costa

et al. 2024

Neurol Sci

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Despite research has massively focused on how emotions conveyed by faces are perceived, the perception of emotions’ authenticity is a topic that has been surprisingly overlooked. Here, we present the Emotion Authenticity Recognition (EAR) test, a test specifically developed using dynamic stimuli depicting authentic and posed emotions to evaluate the ability of individuals to correctly identify an emotion (emotion recognition index, ER Index) and classify its authenticity (authenticity recognition index (EA Index). The EAR test has been validated on 522 healthy participants and normative values are provided. Correlations with demographic characteristics, empathy and general cognitive status have been obtained revealing that both indices are negatively correlated with age, and positively with education, cognitive status and different facets of empathy. The EAR test offers a new ecological test to assess the ability to detect emotion authenticity that allow to explore the eventual social cognitive deficit even in patients otherwise cognitively intact.

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Specific facial emotion recognition deficits across the course of psychosis: A comparison of individuals with low-risk, high-risk, first-episode psychosis and multi-episode schizophrenia-spectrum disorders

Cited by 7 publications

References 73 publications

Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study

Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study

Facial emotion-recognition deficits in patients with schizophrenia and unaffected first-degree relatives

The Emotion Authenticity Recognition (EAR) test: normative data of an innovative test using dynamic emotional stimuli to evaluate the ability to recognize the authenticity of emotions expressed by faces

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