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Introduction: Expressing emotions play a special role in daily communication, and one of the most essential methods in detecting emotions is to detect facial emotional states. Therefore, one of the crucial aspects of the natural human-machine interaction is the recognition of facial expressions and the creation of feedback, according to the perceived emotion. Methods: To implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing on them. The next step is to use the combination of 3D convolutional neural network (3DCNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing 3DCNN in our model is that no dimension is removed from the images, and considering the temporal information in dynamic images leads to more efficient and better classification. In addition, the training of the 3DCNN network in calculating the backpropagation error is adjusted by LA so that both the efficiency of the proposed model is increased, and the working memory part of the SOAR model can be implemented. Results and discussion: Due to the importance of the topic, this article presents an efficient method for recognizing emotional states from facial images based on a mixed deep learning and cognitive model called SOAR. Among the objectives of the proposed model, it is possible to mention providing a model for learning the time order of frames in the movie and providing a model for better display of visual features, increasing the recognition rate. The accuracy of recognition rate of facial emotional states in the proposed model is 85.3%. To compare the effectiveness of the proposed model with other models, this model has been compared with competing models. By examining the results, we found that the proposed model has a better performance than other models. [ABSTRACT FROM AUTHOR]

IntroductionExpressing emotions play a special role in daily communication, and one of the most essential methods in detecting emotions is to detect facial emotional states. Therefore, one of the crucial aspects of the natural human–machine interaction is the recognition of facial expressions and the creation of feedback, according to the perceived emotion.MethodsTo implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing on them. The next step is to use the combination of 3D convolutional neural network (3DCNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing 3DCNN in our model is that no dimension is removed from the images, and considering the temporal information in dynamic images leads to more efficient and better classification. In addition, the training of the 3DCNN network in calculating the backpropagation error is adjusted by LA so that both the efficiency of the proposed model is increased, and the working memory part of the SOAR model can be implemented.Results and discussionDue to the importance of the topic, this article presents an efficient method for recognizing emotional states from facial images based on a mixed deep learning and cognitive model called SOAR. Among the objectives of the proposed model, it is possible to mention providing a model for learning the time order of frames in the movie and providing a model for better display of visual features, increasing the recognition rate. The accuracy of recognition rate of facial emotional states in the proposed model is 85.3%. To compare the effectiveness of the proposed model with other models, this model has been compared with competing models. By examining the results, we found that the proposed model has a better performance than other models.

Individuals with bipolar disorder (BD) often struggle with emotional regulation and social interactions, partly due to difficulties in accurately recognizing facial emotions. From September 2021 to February 2023, 69 BD individuals-comprising 23 with bipolar manic/hypomanic episode (BME), 23 with bipolar depressive episode (BDE), 23 with bipolar euthymic (EUT)-and 23 healthy controls (HCs) were enrolled. Diagnosis adhered to DSM-IV criteria using M.I.N.I 5.0, alongside assessments via Hamilton Depression Scale 17 and Young Manic Rating Scale. Recognition tasks involving 84 facial expression images across six categories. The Wilcoxon rank-sum test compares two groups, while the Kruskal-Wallis test compares multiple groups with subsequent adjusted pairwise comparisons. The overall correct recognition rate of facial expressions in the BD group (79 %) was significantly lower than that of the HC group (83 %) (P =0.004). Primary differences were noted in neutral (93 % vs. 100 %, P =0.012) and fear (79 % vs. 86 %, P =0.023) expressions. Within the BD group, correct recognition rates were 71 % for BME, 80 % for BDE, and 80 % for EUT, all lower than in the HC group. Significant differences in correct recognition rates of neutral, fear, and joy expressions were observed among the four groups (P <0.05), with the BME group exhibiting the lowest rate. Misidentification of facial expressions was more frequent in the BD group compared to the HC group, particularly among negative expressions. Patients with BD demonstrate lower correct recognition and higher misidentification rates of facial expressions, with those experiencing manic episodes showing impaired recognition of neutral, joy, and fear expressions. • BD individuals have a lower facial expression recognition rate compared to HCs. • Manic patients show impaired recognition of neutral, joy, and fear expressions. • BD misidentify facial expressions more than HCs, showing varied patterns. • Research is needed on neural mechanisms of emotion recognition deficits in BD. [ABSTRACT FROM AUTHOR]

Objectives: Sleep deprivation (SD) has detrimental effects on cognition. Emotional processing, a critical component of social cognition, is also affected by SD. However, current research on how SD affects emotion recognition and the specific emotion recognition that declines with SD is inconsistent. The present study meta-analyzed results of studies examining emotion category recognition changes in SD compared with controls. Methods: PubMed, Web of Science, Embase, PsychINFO, MEDLINE, and China National Knowledge Infrastructure databases were searched. Studies on the impact of acute SD or insomnia on emotional recognition and participants aged 18 years or older were included in this review. The JBI Critical Appraisal Checklist and GRADE approach were used to assess the quality of the studies and evidence. Results: Twelve studies with 414 SD and 399 control participants were included in this meta-analysis. The SD group performed poorer on facial sadness (MD = -4.35; 95% CI, -7.99 to -0.71) and happiness (MD = -1.75; 95% CI, -3.25 to -0.26) recognition than the control group (normal sleep condition). The reaction time of the SD group was significantly longer than that of the control group for all emotional categories. The intensity rating of facial emotions showed no difference between the two groups. Conclusions: Sleep deprivation slows individuals' reactions in facial emotion recognition tasks and weakens their ability to recognize sadness and happiness. Future studies should identify the effects of SD, SD duration, and recovery time on different types of emotion recognition. [ABSTRACT FROM AUTHOR]

Objectives: Sleep deprivation (SD) has detrimental effects on cognition. Emotional processing, a critical component of social cognition, is also affected by SD. However, current research on how SD affects emotion recognition and the specific emotion recognition that declines with SD is inconsistent. The present study meta-analyzed results of studies examining emotion category recognition changes in SD compared with controls. Methods: PubMed, Web of Science, Embase, PsychINFO, MEDLINE, and China National Knowledge Infrastructure databases were searched. Studies on the impact of acute SD or insomnia on emotional recognition and participants aged 18 years or older were included in this review. The JBI Critical Appraisal Checklist and GRADE approach were used to assess the quality of the studies and evidence. Results: Twelve studies with 414 SD and 399 control participants were included in this meta-analysis. The SD group performed poorer on facial sadness (MD = –4.35; 95% CI, –7.99 to –0.71) and happiness (MD = –1.75; 95% CI, –3.25 to –0.26) recognition than the control group (normal sleep condition). The reaction time of the SD group was significantly longer than that of the control group for all emotional categories. The intensity rating of facial emotions showed no difference between the two groups. Conclusions: Sleep deprivation slows individuals’ reactions in facial emotion recognition tasks and weakens their ability to recognize sadness and happiness. Future studies should identify the effects of SD, SD duration, and recovery time on different types of emotion recognition.

The correct identification of basic emotions (joy, fear, disgust, sadness, surprise and anger) plays an important role in social relationships and behaviors linked to one's survival. In Alzheimer's disease (AD), the ability to recognize emotions may already be impaired at early stages of the disease. Therefore, the main objective of the present study was to carry out a systematic review of the literature on facial emotional recognition in people with AD, identifying possible modulating variables. A search on the Web of Science and PsycInfo databases was carried out. After applying the inclusion-exclusion criteria, a total of 28 articles were selected. Static images were the most used stimuli to evaluate the recognition of basic emotions, especially the photographs developed by Paul Ekman and collaborators. Results of the present review indicate that people with AD generally display deficits in emotional recognition, although data are not conclusive, which could be related to the diversity of methodologies applied. In relation to the variables evaluated (sex and age), the need of further research in order to study how these factors could modulate emotional recognition in AD patients is highlighted. [ABSTRACT FROM AUTHOR]

BackgroundClinical and experimental findings suggest that insomnia is associated with changes in emotional processing and impairments in cognitive functioning. In the present study, we investigate the relationship between facial emotion recognition and executive functioning among individuals with insomnia as well as healthy controls.MethodA total of 11 individuals (mean age 31.3 ± 9.4) diagnosed with insomnia disorder and 15 control participants (mean age 24.8 ± 4.6) took part in the study. Participants responded to a facial emotion recognition task which presented them with static and dynamic stimuli, and were evaluated with regard to cognition, sleep, and mood.ResultsCompared to controls, we found that participants with insomnia performed worse in the recognition of the facial emotion of fear (p = 0.001;ηp2 = 0.549; β = 0.999) and had lower scores in tests of verbal comprehension and perceptual organization (104.00 vs. 115.00, U = 135.5; p = 0.004; Cohen’s, 2013d = 1.281). We also found a relationship between facial emotion recognition and performance in cognitive tests, such as those related to perceptual organization, cognitive flexibility, and working memory.ConclusionResults suggest that participants with insomnia may present some impairment in executive functions as well as in the recognition of facial emotions with negative valences (fear and sadness).

The integration of visual features is important for recognizing objects as a coherent whole, a key domain of difficulty in autism spectrum disorder (ASD). We tested the hypothesis that ASD patients exhibit difficulties in facial emotional recognition via perceptual binding difficulties due to weak coherence. We assessed 18 ASD and 27 typically developing individuals for their ability to identify emotional expressions from faces in pictures moving behind a narrow vertical and horizontal slit. In this task, only a single local piece of facial information was provided at any one time through the slit. Using a voxel‐based analysis of neurite‐orientation dispersion and density imaging (NODDI), we examined the relationship between NODDI index values at each voxel and the behavioral performance of ASD patients in the slit‐viewing paradigm. ASD patients demonstrated impaired recognition of facial emotional expression only in horizontal slit‐viewing. This deficit was associated with deficits in communication ability. Voxel‐based analysis revealed significant negative correlations between behavioral deficits in horizontal slit‐viewing and NODDI index values in clusters including the ventral occipital complex region, superior temporal/parietal association areas, and forceps major of the corpus callosum. Our results indicated deficits for the first time in perceptual integration of facial expression across hemispheres in ASD patients due to microstructural disturbances in the corpus callosum and areas related to viewing of the human face. This may underscore the difficulties faced by ASD patients in understanding the emotions of other people, contributing to impairments in communication ability in ASD patients. Autism Res 2020, 13: 729–740. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: We assessed ASD and typically developing individuals for their ability to identify emotional expressions from faces in pictures moving behind a narrow vertical and horizontal slit. ASD patients demonstrated impaired recognition of facial emotional expression only in horizontal slit‐viewing. Voxel‐based analysis revealed significant negative correlations between behavioral deficits and NODDI index values in clusters including the corpus callosum. Our results indicated deficits in perceptual integration of facial expression across hemispheres in ASD patients potentially resulting from microstructural disturbances. [ABSTRACT FROM AUTHOR]

Background: Clinical and experimental findings suggest that insomnia is associated with changes in emotional processing and impairments in cognitive functioning. In the present study, we investigate the relationship between facial emotion recognition and executive functioning among individuals with insomnia as well as healthy controls. Method: A total of 11 individuals (mean age 31.3 ± 9.4) diagnosed with insomnia disorder and 15 control participants (mean age 24.8 ± 4.6) took part in the study. Participants responded to a facial emotion recognition task which presented them with static and dynamic stimuli, and were evaluated with regard to cognition, sleep, and mood. Results: Compared to controls, we found that participants with insomnia performed worse in the recognition of the facial emotion of fear (p = 0.001; η p 2 = 0.549; β = 0.999) and had lower scores in tests of verbal comprehension and perceptual organization (104.00 vs. 115.00, U = 135.5; p = 0.004; Cohen's, 2013 d = 1.281). We also found a relationship between facial emotion recognition and performance in cognitive tests, such as those related to perceptual organization, cognitive flexibility, and working memory. Conclusion: Results suggest that participants with insomnia may present some impairment in executive functions as well as in the recognition of facial emotions with negative valences (fear and sadness). [ABSTRACT FROM AUTHOR]

Objectives: The aim of our study was to assess the differences in facial emotional recognition (FER) between patients with first-episode psychosis (FEP), patients with multi-episode schizophrenia (SCH), and healthy controls (HC) and to find possible correlations of FER with psychopathology in the two patient groups. Methods: We performed a cross-sectional study enrolling 160 patients from two psychiatric hospitals in Croatia (80 FEP and 80 SCH) and 80 HC during the period from October 2015 until October 2017. Patients were assessed once during their hospital treatment, using the Penn Emotion Recognition Task for assessment of FER, rating scales for psychopathology and depression and self-reporting questionnaires for impulsiveness, aggression, and quality of life. Results: The number of correctly identified emotions significantly decreased from HC to FEP [Δ −7%; 95% confidence interval (CI) [−12% to −3%], effect size r = 0.30] and more markedly in SCH (Δ −15%; 95% CI [−25% to −10%], effect size r = 0.59) after the adjustment for age and gender and correction for multiple testing. Correct FER for negative emotions, but not for happiness and neutral emotions, had a statistically significant negative correlation with some features on the scales of psychopathology, impulsivity and aggression in both patient groups. Conclusions: Impairment of FER is present from the first episode of schizophrenia and increases further with multiple psychotic episodes, but it may depend on or contribute to clinical symptoms. Therefore, assessment of FER should be included in the clinical assessment and integrated in the plan of treatment from the beginning of the illness. (JINS , 2019, 25 , 165–173) [ABSTRACT FROM AUTHOR]

Objective: No research has been conducted on facial emotional recognition on patients with borderline personality disorder (BPD) and schizotypal personality disorder (SPD). The present study aimed at comparing facial emotion recognition in these patients with the general population. The neurocognitive processing of emotions can show the pathologic style of these 2 disorders. Method: Twenty BPD patients, 16 SPD patients, and 20 healthy individuals were selected by available sampling method. Structural Clinical Interview for Axis II, Millon Personality Inventory, Beck Depression Inventory and Facial Emotional Recognition Test was were conducted for all participants. Discussion: The results of one way ANOVA and Scheffe’s post hoc test analysis revealed significant differences in neuropsychology assessment of facial emotional recognition between BPD and SPD patients with normal group (p = 0/001). A significant difference was found in emotion recognition of fear between the 2 groups of BPD and normal population (p = 0/008). A significant difference was observed between SPD patients and control group in emotion recognition of wonder (p = 0/04(. The obtained results indicated a deficit in negative emotion recognition, especially disgust emotion, thus, it can be concluded that these patients have the same neurocognitive profile in the emotion domain.

The symptoms of autism spectrum disorder (ASD) have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the 'disconnectivity' hypothesis has been used to explain characteristic impairments in 'socio-emotional' function. Therefore, in this study we compared the facial emotional recognition (FER) feature and the integrity of social-emotional-related white-matter tracts between children and adolescents with high-functioning ASD (HFA) and their typically developing (TD) counterparts. The correlation between the two factors was explored to find out if impairment of the white-matter tracts is the neural basis of social-emotional disorders. Compared with the TD group, FER was significantly impaired and the fractional anisotropy value of the right cingulate fasciculus was increased in the HFA group ( P < 0.01). In conclusion, the FER function of children and adolescents with HFA was impaired and the microstructure of the cingulate fasciculus had abnormalities. [ABSTRACT FROM AUTHOR]

Objective: No research has been conducted on facial emotional recognition on patients with borderline personality disorder (BPD) and schizotypal personality disorder (SPD). The present study aimed at comparing facial emotion recognition in these patients with the general population. The neurocognitive processing of emotions can show the pathologic style of these 2 disorders. Method: Twenty BPD patients, 16 SPD patients, and 20 healthy individuals were selected by available sampling method. Structural Clinical Interview for Axis II, Millon Personality Inventory, Beck Depression Inventory and Facial Emotional Recognition Test was were conducted for all participants. Discussion: The results of one way ANOVA and Scheffe's post hoc test analysis revealed significant differences in neuropsychology assessment of facial emotional recognition between BPD and SPD patients with normal group (p = 0/001). A significant difference was found in emotion recognition of fear between the 2 groups of BPD and normal population (p = 0/008). A significant difference was observed between SPD patients and control group in emotion recognition of wonder (p = 0/04). The obtained results indicated a deficit in negative emotion recognition, especially disgust emotion, thus, it can be concluded that these patients have the same neurocognitive profile in the emotion domain. [ABSTRACT FROM AUTHOR]

Background: According to the mother's key roles in bringing up emotional and cognitive abilities of mentally retarded children and respect to positive psychology in recent decades, this research is administered to assess the relation between mother's happiness level with cognitive- executive functions (i.e. attention, working memory, inhibition and planning) and facial emotional recognition ability as two factors in learning and adjustment skills in mentally retarded children with Down syndrome. Methods: This study was an applied research and data were analyzed by Pearson correlation procedure. Population is included all school children with Down syndrome (9-12 yr) that come from Tehran, Iran. Overall, 30 children were selected as an in access sample. After selection and agreement of parents, the Wechsler Intelligence Scale for ChildrenRevised (WISC-R) was performed to determine the student's IQ, and then mothers were invited to fill out the Oxford Happiness Inventory (OHI). Cognitive-executive functions were evaluated by tests as followed: Continues Performance Test (CPT), N-Back, Stroop test (day and night version) and Tower of London. Ekman emotion facial expression test was also accomplished for assessing facial emotional recognition in children with Down syndrome, individually. Results: Mother's happiness level had a positive relation with cognitive-executive functions (attention, working memory, inhibition and planning) and facial emotional recognition in her children with Down syndrome, significantly. Conclusion: Parents’ happiness (especially mothers) is a powerful predictor for cognitive and emotional abilities of their children.

The role of the human insula in facial emotion recognition is controversially discussed, especially in relation to lesion-location-dependent impairment following stroke. In addition, structural connectivity quantification of important white-matter tracts that link the insula to impairments in facial emotion recognition has not been investigated. In a case-control study, we investigated a group of 29 stroke patients in the chronic stage and 14 healthy age- and gender-matched controls. Lesion location of stroke patients was analysed with voxel-based lesion-symptom mapping. In addition, structural white-matter integrity for tracts between insula regions and their primarily known interconnected brain structures was quantified by tractography-based fractional anisotropy. Our behavioural analyses showed that stroke patients were impaired in the recognition of fearful, angry and happy but not disgusted expressions. Voxel-based lesion mapping revealed that especially lesions centred around the left anterior insula were associated with impaired recognition of emotional facial expressions. The structural integrity of insular white-matter connectivity was decreased for the left hemisphere and impaired recognition accuracy for angry and fearful expressions was associated with specific left-sided insular tracts. Taken together, these findings suggest that a multimodal investigation of structural alterations has the potential to deepen our understanding of emotion recognition impairments after stroke., Competing Interests: The authors report no competing interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Neurological disease can impair emotional communication by several means: damaging the networks important in understanding the meaning of emotional stimuli (emotional semantics); damaging networks important in the perceptual recognition and production of emotional stimuli, and damaging the connections between networks supporting emotional semantics and recognition/production networks. Disorders of emotional expression, comprehension, and emotional semantics may improve with pharmacological or behavioral treatments. Pharmacological treatments can be used to redress naturally occurring or disease-related alterations in the computational properties of target neural systems. No drug treatment can replace a loss of cerebral knowledge related to the pathological loss of neural connectivity. Behavioral treatments that benefit either comprehension or expression of specific emotions may be of value if these emotions are particularly important in enabling human social interaction. However, behavioral treatments that achieve generalization, that is, improve performance with untrained exemplars and in daily life, are much to be preferred, even as they pose the greatest methodological challenges. This chapter will discuss possible mechanisms of generalization and then review what is known about the treatment of expressive and receptive affective aprosodia, deficits in recognition of facial emotions, and pseudobulbar affect. The final section of the chapter is devoted to a discussion of three disorders of emotional semantics, apathy, alexithymia, and impaired empathy., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The integration of visual features is important for recognizing objects as a coherent whole, a key domain of difficulty in autism spectrum disorder (ASD). We tested the hypothesis that ASD patients exhibit difficulties in facial emotional recognition via perceptual binding difficulties due to weak coherence. We assessed 18 ASD and 27 typically developing individuals for their ability to identify emotional expressions from faces in pictures moving behind a narrow vertical and horizontal slit. In this task, only a single local piece of facial information was provided at any one time through the slit. Using a voxel-based analysis of neurite-orientation dispersion and density imaging (NODDI), we examined the relationship between NODDI index values at each voxel and the behavioral performance of ASD patients in the slit-viewing paradigm. ASD patients demonstrated impaired recognition of facial emotional expression only in horizontal slit-viewing. This deficit was associated with deficits in communication ability. Voxel-based analysis revealed significant negative correlations between behavioral deficits in horizontal slit-viewing and NODDI index values in clusters including the ventral occipital complex region, superior temporal/parietal association areas, and forceps major of the corpus callosum. Our results indicated deficits for the first time in perceptual integration of facial expression across hemispheres in ASD patients due to microstructural disturbances in the corpus callosum and areas related to viewing of the human face. This may underscore the difficulties faced by ASD patients in understanding the emotions of other people, contributing to impairments in communication ability in ASD patients. Autism Res 2020, 13: 729-740. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We assessed ASD and typically developing individuals for their ability to identify emotional expressions from faces in pictures moving behind a narrow vertical and horizontal slit. ASD patients demonstrated impaired recognition of facial emotional expression only in horizontal slit-viewing. Voxel-based analysis revealed significant negative correlations between behavioral deficits and NODDI index values in clusters including the corpus callosum. Our results indicated deficits in perceptual integration of facial expression across hemispheres in ASD patients potentially resulting from microstructural disturbances., (© 2020 International Society for Autism Research, Wiley Periodicals, Inc.)

Introduction: The Klinefelter syndrome (KS) is a genetic condition characterized by an X supernumerary sex chromosome in males. The syndrome is frequently associated with cognitive impairment. Indeed, the different areas of the executive sphere can be affected such as inhibition, cognitive flexibility but also attentional and visual-spatial domain. Social cognition disorders, predominantly on emotional recognition processes, have also been documented. In addition, the syndrome may be associated with psychiatric symptoms., Material and Method: Our study aims to characterize of the various components of social cognition in the SK: facial emotional recognition, theory of mind and attributional style. For this two groups (SK group versus control group) of participants (n=16) matched for age and sociocultural level were recruited. Participants with intellectual disabilities, psychiatric or neurological disorders were excluded. Three social cognition tests were available: the TREF, the MASC, the AIHQ. Neurocognitive functions were assessed by the fNart, the subtest "logical memory" of the MEM-III, the subtests of the two VOSP battery, the d2, the TMT and the Stroop test., Results: The SK group had specific social cognition disorders in comparison to the control group. Two emotions in particular were less well recognized: fear and contempt. In addition, the SK group had significantly lower results in theory of mind. Regarding the hostile attribution bias, no significant difference was found. Finally, the results showed correlations between specific attentional disorders and facial emotional recognition., Discussion-Conclusion: Our study emphasizes social cognition disorders in SK. These disorders could be considered as a phenotypic trait in the syndrome. The interest of better characterizing the cognitive phenotype of genetic disorders that can affect the neurodevelopment is to offer specific cognitive remediation strategies., (Copyright © 2016. Published by Elsevier Masson SAS.)