Emotion Recognition Studies Research Articles

A review of EEG features for emotion recognition

Emotion recognition is an important research topic in the human-machine interaction field, and it can be applied to medicine, education, psychology, military, and other areas. Electroencephalogram (EEG) signals are mostly used among various indices of emotion recognition. High accuracy of emotion classifiers can be achieved by extracting the most relevant and discriminant features of emotion states. This study surveys EEG features that are extensively used in current emotion recognition studies by introducing EEG features from the following four viewpoints: time domain, frequency domain, time–frequency domain, and space domain. An SLDA algorithm is imported to three public EEG-emotion datasets (SEED, DREAMER, and CAS-THU) to evaluate feature capabilities that distinguish emotion valence. Existing problems and future investigations are also discussed in this paper.

Spanish validation of the “Reading the Mind in the Voice” task: A study of complex emotion recognition in adults with autism spectrum conditions

BackgroundSocial cognition includes a range of cognitive processes that help individuals understand how others think and feel. Recent proposals outline the relevance of this cognitive domain in a broad set of clinical pictures, as well as its diagnostic and prognostic value. In this study, we present the Spanish validation of the Reading the Mind in the Voice (RMV-SV) task, (Rutherford et al., 2002), an instrument for the recognition of mental states and complex emotions through prosody. MethodA group of adults diagnosed with autism spectrum disorders (ASD, n = 45) were compared to a control group from the general population (n = 51) in several emotion-recognition tasks. ResultsThe ASD group performed worse than controls overall on complex emotion and mental state recognition from voices (ŋ2 = .322; p < .01), statistically significant differences were found on 18/33 specific emotions. A positive correlation was found between verbal and nonverbal IQ and emotion recognition task scores. Using RMV-SV scores, 87.5% of the participants were correctly allocated to their original condition group. ConclusionsThe results confirmed the alteration of the recognition of mental states and complex emotions by prosody among adults diagnosed with ASD. The RMV-SV showed suitable validity and reliability indexes, so the adapted task could facilitate the evaluation of complex emotion recognition through prosody in Spanish.

Fuzzy emotion: a natural approach to automatic facial expression recognition from psychological perspective using fuzzy system.

Many studies in automatic facial expression recognitions merely limit their focus on recognizing basic emotions, ignoring the fact that humans show various emotions in their daily life. Moreover, from psychological perspective humans express multiple emotions simultaneously. Up to now, researchers recognize two basic emotions at the same time, called mixed emotions. Nevertheless, the mixed emotion still does not reflect how humans express the emotion naturally. This paper advances the concept of mixed emotion into a generalized fuzzy emotion. Fuzzy emotion captures multiple emotions in a single image using fuzzy inference engine. We propose a fuzzy emotion framework which consists of processing system and knowledge system. The processing system extracts facial expression parameters, and the knowledge system employs a fuzzy knowledge-based engine, elicited from the psychologist knowledge to recognize facial expressions. Some advantages are offered: (1) no facial template comparison; (2) no training efforts needed; (3) moreover, fuzzy emotion can recognize ambiguous facial expressions adaptively. The experiment gives a recognition result with the highest accuracy rate of 0.90. A research agenda for future study of mixed emotion recognition is proposed.

The Fusion of Electroencephalography and Facial Expression for Continuous Emotion Recognition

Recently, the study of emotion recognition has received increasing attentions by the rapid development of noninvasive sensor technologies, machine learning algorithms and compute capability of computers. Compared with single modal emotion recognition, the multimodal paradigm introduces complementary information for emotion recognition. Hence, in this work, we presented a decision level fusion framework for detecting emotions continuously by fusing the Electroencephalography (EEG) and facial expressions. Three types of movie clips (positive, negative, and neutral) were utilized to elicit specific emotions of subjects, the EEG and facial expression signals were recorded simultaneously. The power spectrum density (PSD) features of EEG were extracted by time-frequency analysis, and then EEG features were selected for regression. For the facial expression, the facial geometric features were calculated by facial landmark localization. Long short-term memory networks (LSTM) were utilized to accomplish the decision level fusion and captured temporal dynamics of emotions. The results have shown that the proposed method achieved outstanding performance for continuous emotion recognition, and it yields 0.625± 0.029 of concordance correlation coefficient (CCC). From the results, the fusion of two modalities outperformed EEG and facial expression separately. Furthermore, different numbers of time-steps of LSTM was applied to analyze the temporal dynamic capturing.

Exploration of Complementary Features for Speech Emotion Recognition Based on Kernel Extreme Learning Machine

Previous studies of speech emotion recognition using either empirical features (e.g., F0, energy, and voice probability) or spectrogram-based statistical features. The empirical features can highlight the human knowledge of emotion recognition, while the statistical features enable a general representation, but they do not emphasize human knowledge sufficiently. However, the use of these two kinds of features together can complement some features that may be unconsciously used by humans in daily life but have not been realized yet. Based on this consideration, this paper proposes a dynamic fusion framework to utilize the potential advantages of the complementary spectrogram-based statistical features and the auditory-based empirical features. In addition, a kernel extreme learning machine (KELM) is adopted as the classifier to distinguish emotions. To validate the proposed framework, we conduct experiments on two public emotional databases, including Emo-DB and IEMOCAP databases. The experimental results demonstrate that the proposed fusion framework significantly outperforms the existing state-of-the-art methods. The results also show that the proposed method, by integrating the auditory-based features with spectrogram-based features, could achieve a notably improved performance over the conventional methods.

Reconocimiento facial de emociones en trastornos neurológicos: una revisión narrativa

Facial emotion recognition refers to a person's interpretation of facial features of another to identify a particular emotional state. It is essential in human evolution and encompasses distinct neural networks. Facial emotion recognition is altered in most neurodegenerative diseases, but literature just focus on single neurological pathologies or limited comparison with psychiatric pathologies. It is unknown if a common pattern of affection through pathologies exists or if facial emotion recognition changes according to the underlying pathology. This review discusses its development in healthy population, synthesizes facial emotion recognition studies regarding most common neurological diseases, as well as most relevant findings in neuroimaging and current treatments. Facial emotion recognition, especially negative emotions, is altered in all described neurodegenerative diseases and could constitutes an early marker of cognitive deterioration.

A Machine Learning Approach for FEMG Pattern Recognition System to Control Hands-Free Wheelchair

Recently the classification of dissimilar facial emotion using facial Electromyogram (FEMG) signal has gained increased attention of the researcher in order to interface with the bio- tribology application. The facial emotion recognition study is a cumbersome analysis where classification accuracy is a key factor. In this paper, EMGs of five facial emotion were acquired from twenty subjects. The EMG signals were recorded using a wireless data acquisition device with one pair of surface electrodes used in a bipolar configuration. Before analysis, the recorded FEMG signals were pre-processed and segmented into well-defined portions. Proposed wavelet decomposition method, transformed the FEMG signal into a set of various levels of coefficients sub-bands. Different features were extracted from this decomposed sub-bands coefficients. The extracted features sets were classified into five different classes i.e. Happiness, Anger, Disgust, Fear and Surprise using Artificial Neural Network. Finally, 93.3% classification accuracy has been achieved by using our proposed method. The findings of this work are in the process of practical applications for the processing and recognition of the facial emotion, essentially, in order to design reliable interfaces for controlling a hands-free wheelchair. This study can also be extended in the fields of Tribology in Bio-Medical applications.

Recognition of facial emotions of varying intensities by three-year-olds.

Early facial emotion recognition is hypothesized to be critical to later social functioning. However, relatively little is known about the typical intensity thresholds for recognizing facial emotions in preschoolers, between 2 and 4 years of age. This study employed a behavioral sorting task to examine the recognition of happy, fearful, and angry expressions of varying intensity in a large sample of 3-year-old children (N = 208). Thresholds were similar for all expressions; accuracy, however, was significantly lower for fear. Fear and anger expressions above threshold were significantly more confused with one another than with other expressions. In contrast, neutral faces were significantly more often interpreted as happy than as angry or fearful. These results provide a comparison point for future studies of early facial emotion recognition in typical and atypical populations of children in this age group. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Sleep deprivation selectively enhances interpersonal emotion recognition from dynamic facial expressions at long viewing times: An observational study

Observational and experimental studies have shown that sleep deprivation disinhibits emotional responses to disturbing and rewarding external events. On the other hand, most studies on sleep deprivation and interpersonal emotion recognition report that sensitivity to others’ emotions is dampened during sleep deprivation. This is at odds with current neuroscientific theories of social cognition that assume that affective experiences and emotion recognition in others are closely tied at the neural and physiological level. In this observational study we show that sleep deprivation can actually increase emotion recognition accuracy from dynamically unfolding facial expressions if they are viewed sufficiently long. Participants viewed 2–4 s or 8–10s video clips of female senders who facially communicated anger, disgust, fear or sadness to their romantic partner and evaluated the sender’s affective state in a forced-choice paradigm, either during sleep deprivation after a night shift (N = 40) or after normal night sleep (N = 50). All participants showed a significant increase in emotion recognition accuracy from 2–4 s to 8–10 s stimulus presentation times. Emotion recognition accuracy did not differ between sleep-deprived and control participants for 2–4 s videos, but sleep-deprived participants showed significantly higher emotion recognition accuracy than control participants for 8–10 s videos. We surmise that this effect might be due to the break-down of prefrontal activity associated with sleep deprivation, which might not only disinhibit affective responses to external events but might also release simulation-based neural processes that contribute to interpersonal emotion recognition from dynamic facial expressions at longer time scales than usually investigated in emotion recognition studies.

Influence of emotional contexts on facial emotion attribution in schizophrenia

Recent emotion recognition studies in schizophrenia have reported misattribution of emotional content to emotionally neutral faces. While in these studies faces are presented in the absence of any contextual reference, in daily life facial expressions are typically perceived within a specific situational context. However, there is no evidence on the possible modulatory role of contextual aids on emotion attribution to neutral faces. We address this issue in the present study. Thirty schizophrenia patients and thirty paired controls performed an emotion categorization task (by choosing one among six labels of emotions) with neutral target faces that were superimposed on affectively positive, negative or neutral scenes. In presence of positive contexts, patients categorized neutral faces as happy and fearful more frequently than controls. When negative contexts were present, patients also categorized neutral faces as fearful more frequently than controls. However, in the presence of neutral contexts patients and controls did not differ in their categorization pattern. These results suggest that explicit presence of a neutral context seems to compensate for the bias showed by patients. With the purpose of correcting emotion misattribution in schizophrenia, emotionally neutral contexts might be incorporated to treatments aimed at improving social cognition performance in this patient population.

A Developmental Twin Study of Emotion Recognition and Its Negative Affective Clinical Correlates

Youth with psychiatric disorders distinguished by irritability, including depression and associated trait neuroticism, show deficits in the ability to recognize facial expressions of emotion, particularly happiness. However, the contribution of genetic and environmental factors to this ability remains unknown. The present study examined this trait in twins to assess the genetic and environmental influences on face-emotion recognition abilities and their association with irritability, neuroticism, and depression. Child and adolescent twins (N= 957 from 496 families) 9 to 17 years old rated their irritability (on the Affective Reactivity Index), neuroticism (on the Junior Eysenck Personality Questionnaire), and depression (on the Short Mood and Feelings Questionnaire) and completed a face-emotion labeling task. Faces depicting anger, disgust, fear, happiness, sadness, and surprise were morphed with a neutral face, yielding 10 levels of increasing emotional expressivity. Biometrical twin analyses evaluated contributions of genetic and environmental factors to the etiology of face-emotion recognition and its association with irritability, neuroticism, and depression. Recognition of each emotion was heritable; common and specific sets of genetic factors influenced all emotions and individual emotions, respectively. Irritability, neuroticism, and depression were modestly and negatively correlated with emotion recognition, particularly the recognition of happiness. For irritability and neuroticism, this correlation appeared largely due to genetic factors. This study maps genetic and environmental contributions to face-emotion recognition and its association with irritability, neuroticism, and depression. Findings implicate common genetic factors in deficits regarding the recognition of happiness associated with irritability and neuroticism in childhood and adolescence.

EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution.

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of –. Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

Using behavioral features in tablet-based auditory emotion recognition studies

The recognition of emotions in spoken words is one of the most important aspects in human communication and social relationships. Traditional approaches to the study of vocal emotional recognition involve instructing listeners to choose which one of several words describing emotion categories best characterize linguistically neutral utterances or vocalizations uttered by actors portraying various emotional states. To this end, generic experiment control software is usually used, which has some disadvantages. In this paper, we present a system that digitalizes the whole process involved in understanding how people perceive and understand vocal emotions, improving data collection, processing and analysis. Moreover, this system provides a new group of features that allows a more comprehensive characterization of the behavioral dimension underlying vocal emotional recognition. In this paper we describe this system and analyze the relationship between emotional perception, gender, age and Human–Computer Interaction.

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

2〜4歳児における情動認知能力の発達的検討 : 課題遂行時の聴覚・視覚情報処理モデルの提案

2〜4歳児における情動認知能力の発達的検討 : 課題遂行時の聴覚・視覚情報処理モデルの提案

Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders.

Emotion recognition is disrupted in many mental health disorders, which may reflect shared genetic aetiology between this trait and these disorders. We explored genetic influences on emotion recognition and the relationship between these influences and mental health phenotypes. Eight‐year‐old participants (n = 4,097) from the Avon Longitudinal Study of Parents and Children (ALSPAC) completed the Diagnostic Analysis of Non‐Verbal Accuracy (DANVA) faces test. Genome‐wide genotype data was available from the Illumina HumanHap550 Quad microarray. Genome‐wide association studies were performed to assess associations with recognition of individual emotions and emotion in general. Exploratory polygenic risk scoring was performed using published genomic data for schizophrenia, bipolar disorder, depression, autism spectrum disorder, anorexia, and anxiety disorders. No individual genetic variants were identified at conventional levels of significance in any analysis although several loci were associated at a level suggestive of significance. SNP‐chip heritability analyses did not identify a heritable component of variance for any phenotype. Polygenic scores were not associated with any phenotype. The effect sizes of variants influencing emotion recognition are likely to be small. Previous studies of emotion identification have yielded non‐zero estimates of SNP‐heritability. This discrepancy is likely due to differences in the measurement and analysis of the phenotype.

Spitefulness and psychopathy: A contribution for an Italian adaptation

IntroductionPsychopathy has been individuated as an important predictor of criminal recidivism. As a consequence, a growing number of studies has examined factors associated with psychopathic traits in criminal population. While spitefulness has been associated with a range of destructive behaviors, there is a paucity of instruments that evaluate the spitefulness (Marcus &amp; Zeigler-Hill, 2015).ObjectiveTesting the validity and reliability of an Italian version of the Spitefulness Scale.AimsCorrelate Spitefulness Scale scores and other indices of psychological functioning. We recruited an offenders sample (n = 400) and a community sample (n = 400). We administered the Spitefulness Scale (Marcus, 2014), along with the following measures: Dirty Dozen (Carmines &amp; Zeller, 1979), Aggression Questionnaire (Buss &amp; Perry, 1992), Difficulties in Emotion Regulation Scale (Gratz &amp; Roemer, 2004), and SRP (Hare, 1980).ResultsOffenders participants showed higher levels of spitefulness. Further, the construct validity of the scale was confirmed by associations with measures of psychopathy, emotion dysregulation, and interpersonal problems.ConclusionsThe use of the Italian version of the Spitefulness Scale seemed promising for the study of emotion recognition in both clinical and nonclinical samples.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Down Syndrome Cognitive Constraints to Recognize Negative Emotion Face Information: Eye Tracking Correlates

Eye gaze correlates to emotion face recognition were obtained from a sample with Down syndrome (DS) as well as from a sample of typical individuals to look for gaze pattern differences between both. The goal was to determine if possible face scanning patterns might be related to different styles of cognitive automatic processing of emotion faces information. First, after IQ control, participants were required to take an affective priming study. This emotion recognition studies allowed appropriate selection of DS showing typical negative face recognition difficulties. Then, both samples took a formative eye tracking study in order to identify gaze correlates typifying them. Results showed that participants with DS have atypical eye fixation patterns regarding emotion faces recognition. In particular, they seem to intentionally avoid fixating on the eyes of presented photographs of emotion faces. This face scanning patterns might contribute to their difficulties to recognize negative face information. It is argued that this kind of cognitive processing of emotion facial information obeys to an acquired affective style.

How does context affect our perception of emotional faces? Using eye-tracking to explore differences in younger and older adults

Event Abstract Back to Event How does context affect our perception of emotional faces? Using eye-tracking to explore differences in younger and older adults Emily Salanitro-Chafei1* and Fiona Kumfor1, 2, 3 1 University of Sydney, Psychology, Australia 2 Brain and Mind Centre, University of Sydney, Australia 3 Centre of Excellence in Cognition and its Disorders (ARC), Australia Aims: Recognising emotional expressions is fundamental for successful social interactions. However, most emotion recognition studies use isolated, context-free stimuli. Furthermore, how contextual information influences perceptions of faces with age has largely been unexplored. The current study investigated how contextual cues, such as body language and scenes, that varied in congruency, influenced younger and older adults’ perception of emotional expressions. Additionally, we compared gaze patterns of age groups towards emotional stimuli, to determine whether older adults are more influenced by contextual information than younger adults. Method: Forty participants (21 younger; 19 older) completed three emotion recognition tasks (Face-Alone, Face-Body, Face-Scene) and were asked to identify facial emotional expressions. Eye-tracking data was simultaneously recorded to objectively measure the amount of time participants dwelled on the faces compared to the contexts, across tasks. Results: Contrary to previous findings, age groups did not differ in their ability to recognise context-free emotional stimuli. Notably, when the context was congruent (i.e. face and context emotion matched) participants performed better, irrespective of task (p =.02). Interestingly, when the context was incongruent on the Face-Body task, older participants performed worse than younger participants (p =.01). Eye-tracking results revealed that younger participants exhibited significantly longer dwell times towards the face and contextual interest areas compared to the older participants, in the Face-Body (p <.001) and Face-Scene (p =.001) tasks. However, on the incongruent condition of the Face-Body task, older participants significantly increased their gaze attention towards the faces (p =.004). Conclusions: Our behavioural and eye-tracking analyses demonstrate that older individuals are more strongly influenced by incongruent body language than younger individuals, performing worse in their emotion recognition performance despite increasing their gaze attention towards faces. Hence, it appears that older individuals are more susceptible to the influence of body language, revealing the importance of context in emotion recognition processes. Keywords: eye-tracking;, emotion recognition, Ageing and cognitive function, context effects, Congruency effect Conference: ASP2017: 27th Annual Meeting for the Australasian Society for Psychophysiology, Parramatta, Australia, 29 Nov - 1 Dec, 2017. Presentation Type: Oral Presentation Topic: Abstract (General) Citation: Salanitro-Chafei E and Kumfor F (2019). How does context affect our perception of emotional faces? Using eye-tracking to explore differences in younger and older adults. Conference Abstract: ASP2017: 27th Annual Meeting for the Australasian Society for Psychophysiology. doi: 10.3389/conf.fnhum.2017.224.00031 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Oct 2017; Published Online: 25 Jan 2019. * Correspondence: Ms. Emily Salanitro-Chafei, University of Sydney, Psychology, Sydney, Australia, emily.chafei@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Emily Salanitro-Chafei Fiona Kumfor Google Emily Salanitro-Chafei Fiona Kumfor Google Scholar Emily Salanitro-Chafei Fiona Kumfor PubMed Emily Salanitro-Chafei Fiona Kumfor Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Detecting facial emotion recognition deficits in schizophrenia using dynamic stimuli of varying intensities

BackgroundDeficits in facial emotion recognition have been associated with functional impairments in patients with Schizophrenia (SZ). Whilst a strong ecological argument has been made for the use of both dynamic facial expressions and varied emotion intensities in research, SZ emotion recognition studies to date have primarily used static stimuli of a singular, 100%, intensity of emotion. To address this issue, the present study aimed to investigate accuracy of emotion recognition amongst patients with SZ and healthy subjects using dynamic facial emotion stimuli of varying intensities. To this end an emotion recognition task (ERT) designed by Montagne (2007) was adapted and employed. Methods47 patients with a DSM-IV diagnosis of SZ and 51 healthy participants were assessed for emotion recognition. Results of the ERT were tested for correlation with performance in areas of cognitive ability typically found to be impaired in psychosis, including IQ, memory, attention and social cognition. ResultsPatients were found to perform less well than healthy participants at recognising each of the 6 emotions analysed. Surprisingly, however, groups did not differ in terms of impact of emotion intensity on recognition accuracy; for both groups higher intensity levels predicted greater accuracy, but no significant interaction between diagnosis and emotional intensity was found for any of the 6 emotions. Accuracy of emotion recognition was, however, more strongly correlated with cognition in the patient cohort. DiscussionWhilst this study demonstrates the feasibility of using ecologically valid dynamic stimuli in the study of emotion recognition accuracy, varying the intensity of the emotion displayed was not demonstrated to impact patients and healthy participants differentially, and thus may not be a necessary variable to include in emotion recognition research.