Affect Recognition Research Articles

EEG channel selection using Gramian Angular Fields and spectrograms for energy data visualization

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have a wide range of applications in affect recognition. The usage of irrelevant information channels when decoding brain activity from different regions can negatively impact the task at hand. Therefore, further research is needed to determine the ideal channels for detecting superior performances. In this study, deep learning models and 2-D image representations are used to assess the efficacy of EEG channels for subjective valence responses to energy data visualizations. The EEG signals are converted into spectrograms and Gramian Angular Field (GAF) images. A hybrid Convolution Neural Network (CNN)-Long-Short Term Memory (LSTM) model and LSTM network are used to extract feature sets from the converted images. These feature sets, after reducing their dimensionality using a principal component analysis (PCA) method, are fed into a boosting classifier (AdaBoost). The performance metrics of both models and 2-D representations are compared. The LSTM and CNN-LSTM models with GAFs and spectrograms achieve state-of-the-art accuracies. The CNN-LSTM model achieves the highest performance when using spectrograms for the F8 channel, while the GAF method performs relatively lower for the F3 channel. The CNN-LSTM model with the spectrogram method produces reliable results, and hence, this method is used for further analysis of the remaining channel pairs. This study suggests that using a single EEG channel is more effective than using multiple channels for recognizing emotions in energy data visualizations. The proposed methodology is an efficient way to select channels for this purpose.

An exploratory study of the effect of age and gender on face scanning during affect recognition in immersive virtual reality

A person with impaired emotion recognition is not able to correctly identify facial expressions represented by other individuals. The aim of the present study is to assess eyes gaze and facial emotion recognition in a healthy population using dynamic avatars in immersive virtual reality (IVR). For the first time, the viewing of each area of interest of the face in IVR is studied by gender and age. This work in healthy people is conducted to assess the future usefulness of IVR in patients with deficits in the recognition of facial expressions. Seventy-four healthy volunteers participated in the study. The materials used were a laptop computer, a game controller, and a head-mounted display. Dynamic virtual faces randomly representing the six basic emotions plus neutral expression were used as stimuli. After the virtual human represented an emotion, a response panel was displayed with the seven possible options. Besides storing the hits and misses, the software program internally divided the faces into different areas of interest (AOIs) and recorded how long participants looked at each AOI. As regards the overall accuracy of the participants’ responses, hits decreased from the youngest to the middle-aged and older adults. Also, all three groups spent the highest percentage of time looking at the eyes, but younger adults had the highest percentage. It is also noteworthy that attention to the face compared to the background decreased with age. Moreover, the hits between women and men were remarkably similar and, in fact, there were no statistically significant differences between them. In general, men paid more attention to the eyes than women, but women paid more attention to the forehead and mouth. In contrast to previous work, our study indicates that there are no differences between men and women in facial emotion recognition. Moreover, in line with previous work, the percentage of face viewing time for younger adults is higher than for older adults. However, contrary to earlier studies, older adults look more at the eyes than at the mouth.Consistent with other studies, the eyes are the AOI with the highest percentage of viewing time. For men the most viewed AOI is the eyes for all emotions in both hits and misses. Women look more at the eyes for all emotions, except for joy, fear, and anger on hits. On misses, they look more into the eyes for almost all emotions except surprise and fear.

Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition

Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition

An ecological momentary cognitive assessment study of over-attribution of threat and suicide risk factors in people with serious mental illness

IntroductionPeople with serious mental illness (SMI; schizophrenia, schizoaffective disorder, bipolar disorder) are at increased risk of suicidal ideation (SI). Over-attribution of social threat, or attributing threatening emotions to neutral faces, may contribute to social isolation through increased social avoidance and decreased social approach motivation. These factors are related to suicide, as well as perceived burdensomeness (PB) and thwarted belongingness (TB). This study examined how over-attribution of threat relates to PB, TB, and social motivations. MethodN = 273 participants with SMI were assessed for current SI and behavior, and were stratified into SI (N = 130) vs. non-SI (N = 143) groups. Participants completed smartphone surveys (via ecological momentary assessments [EMA]) 3×/day for 10 days. They also completed the Mobile Ecological Test of Emotion Recognition (METER) 1×/day. Linear mixed models and multi-level mediation tested the relationships between over-attribution of threat, METER performance, PB/TB, and social motivations. ResultsParticipants with and without SI did not significantly differ in over-attribution of threat or METER performance. In separate models, there was a relationship of over-attribution of threat with increased PB (B = 1.00, SE = 0.21, t = 4.72, p < .001), reduced social approach motivation (B = -0.74, SE = 0.22, t = −3.33, p < .001), and increased social avoidance (B = 0.90, SE = 0.24, t = 3.70, p < .001), all significant when adjusting for facial affect recognition ability. A model examining social motivations as a mediator between over-attribution of threat and PB/TB was not significant. ConclusionThese results suggest that over-attribution of threat relates to interpersonal constructs related to SI irrespective of facial affect abilities. This study may inform understanding of social cognitive processes related to suicide in SMI.

Dynamic facial emotion recognition and affective prosody recognition are associated in patients with temporal lobe epilepsy

Deficits in facial emotion recognition have frequently been established in temporal lobe epilepsy (TLE). However, static, rather than dynamic emotion recognition paradigms have been applied. Affective prosody has been insufficiently studied in TLE, and there is a lack of studies investigating associations between auditory and visual emotion recognition. We wished to investigate potential deficits in a dynamic morph task of facial emotion recognition and in an affective prosody recognition task, as well as associations between both tasks. 25 patients with TLE and 24 healthy controls (CG) performed a morph task with faces continuously changing in their emotional intensity. They had to press a button, as soon as they were able to recognize the emotion expressed, and label it accordingly. In the auditory task, subjects listened to neutral sentences spoken in varying emotional tones, and labeled the emotions. Correlation analyses were conducted across both tasks. TLE patients showed significantly reduced prosody recognition compared to CG, and in the morph task, there was a statistical trend towards significantly reduced performance for TLE. Recognition rates in both tasks were significantly associated. TLE patients show deficits in affective prosody recognition, and they may also be impaired in a morph task with dynamically changing facial expressions. Impairments in basic social-cognitive tasks in TLE seem to be modality-independent.

Perception and memory-based representations of facial emotions: Associations with personality functioning, affective states and recognition abilities

Personality traits and affective states are associated with biases in facial emotion perception. However, the precise personality impairments and affective states that underlie these biases remain largely unknown. To investigate how relevant factors influence facial emotion perception and recollection, Experiment 1 employed an image reconstruction approach in which community-dwelling adults (N = 89) rated the similarity of pairs of facial expressions, including those recalled from memory. Subsequently, perception- and memory-based expression representations derived from such ratings were assessed across participants and related to measures of personality impairment, state affect, and visual recognition abilities. Impairment in self-direction and level of positive affect accounted for the largest components of individual variability in perception and memory representations, respectively. Additionally, individual differences in these representations were impacted by face recognition ability. In Experiment 2, adult participants (N = 81) rated facial image reconstructions derived in Experiment 1, revealing that individual variability was associated with specific visual face properties, such as expressiveness, representation accuracy, and positivity/negativity. These findings highlight and clarify the influence of personality, affective state, and recognition abilities on individual differences in the perception and recollection of facial expressions.

The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

Understanding and detecting human emotions is crucial for enhancing mental health, cognitive performance and human–computer interactions. This field in affective computing is relatively unexplored, and gaining knowledge about which external factors impact emotions could enhance communication between users and machines. Furthermore, it could also help us to manage affective disorders or understand affective physiological responses to human spatial and digital environments. The main objective of the current study was to investigate the influence of external stimulation, specifically the influence of different light conditions, on brain activity while observing affect-eliciting pictures and their classification. In this context, a multichannel electroencephalography (EEG) was recorded in 30 participants as they observed images from the Nencki Affective Picture System (NAPS) database in an art-gallery-style Virtual Reality (VR) environment. The elicited affect states were classified into three affect classes within the two-dimensional valence–arousal plane. Valence (positive/negative) and arousal (high/low) values were reported by participants on continuous scales. The experiment was conducted in two experimental conditions: a warm light condition and a cold light condition. Thus, three classification tasks arose with regard to the recorded brain data: classification of an affect state within a warm-light condition, classification of an affect state within a cold light condition, and warm light vs. cold light classification during observation of affect-eliciting images. For all classification tasks, Linear Discriminant Analysis, a Spatial Filter Model, a Convolutional Neural Network, the EEGNet, and the SincNet were compared. The EEGNet architecture performed best in all tasks. It could significantly classify three affect states with 43.12% accuracy under the influence of warm light. Under the influence of cold light, no model could achieve significant results. The classification between visual stimulus with warm light vs. cold light could be classified significantly with 76.65% accuracy from the EEGNet, well above any other machine learning or deep learning model. No significant differences could be detected between affect recognition in different light conditions, but the results point towards the advantage of gradient-based learning methods for data-driven experimental designs for the problem of affect decoding from EEG, providing modern tools for affective computing in digital spaces. Moreover, the ability to discern externally driven affective states through deep learning not only advances our understanding of the human mind but also opens avenues for developing innovative therapeutic interventions and improving human–computer interaction.

GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions

In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities. Our approach is based on a high-level representation of a face, which is synthesized into an anonymized version based on a generative adversarial network (GAN). The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face. Additionally, the performance of preserving facial expressions was evaluated on several affect recognition datasets and outperformed the state-of-the-art methods in most categories. Finally, our approach was analyzed for its ability to remove various facial traits, such as jewelry, hair color, and multiple others. Here, it demonstrated reliable performance in removing these attributes. Our results suggest that GANonymization is a promising approach for anonymizing faces while preserving facial expressions.

Mental Health of School-Aged Children Treated with Propranolol or Atenolol for Infantile Hemangioma and Their Parents

Background: Infants with infantile hemangioma (IH) have been effectively treated with propranolol or atenolol. Concerns were raised about the mental health of these children at school age, due to central nervous system effects of propranolol and visible nature of IH. Objective: This study aimed to compare the mental health at school age of children treated with propranolol to children treated with atenolol for IHs and their parents. Methods: This two-centered cross-sectional study included children aged ≥6 years and treated with either propranolol or atenolol for IH during infancy. Children’s outcomes were performance-based affect recognition (Dutch version of the Developmental Neuropsychological Assessment-II [NEPSY-II-NL]), parent-reported emotional and behavioral functioning (Child Behavioral Checklist [CBCL]), and health-related quality of life (KIDSCREEN-27). Parents’ outcome was parenting stress (Parenting Stress Questionnaire [OBVL]). Results: Data of 105 children (36 propranolol, 69 atenolol; 6.0–11.8 years) were analyzed. Mental health outcomes did not differ between both β-blocker groups. Although overall functioning was in line with norms, children presented specific problems concerning affect recognition, parent-reported attention, and social quality of life. Parents showed increased physical symptoms, depressive symptoms, and parent-child relationship problems. Conclusion: No difference in mental health at school age was found between children treated with propranolol or atenolol for IH. Although few overall mental health problems were found, specific problems require follow-up. Follow-up of children should be directed toward affect recognition, attention, and social functioning in daily life. Problems reported by parents could be ameliorated by mental health support during and after their infant’s β-blocker treatment.

Proactive inhibitory control as a cognitive contributor to social difficulties in adolescents with ADHD traits

Adolescents with attention deficit hyperactivity disorder (ADHD) represent a significantly heterogenous group in terms of cognitive and social ability. Given the high co-occurrence of ADHD and autism, it is unclear whether the social difficulties seen in ADHD may be attributed to the same underlying factors which contribute to social difficulties in autism, or are a unique feature of ADHD. This study explores differences in cognition in adolescents with clinically elevated ADHD symptoms with and without social difficulties, and adolescents with ADHD symptoms and autism. Thirty-nine adolescents completed objective measures of cognition (attention, working memory, inhibition) and social cognition (affect recognition, theory of mind [ToM]). Adolescents with ADHD symptoms and social difficulties had poorer proactive inhibition than both those without social difficulties, and those with autism. Lower cognitive performance was only significantly related to social functioning for adolescents with ADHD who experienced social difficulties. ToM was weakest in those with autism. Social difficulties in adolescents with clinically elevated ADHD symptoms are associated with poor proactive inhibition, working memory and affect recognition. This highlights the potential benefit of cognitive strategies for remediating social difficulties in ADHD, and the need for ADHD specific interventions. Lay abstract Around half of teenagers with attention deficit hyperactivity disorder (ADHD) experience significant difficulties forming and maintaining friendships. This in turn often has negative effects on schooling and mental health. There is a large body of research that suggests the social difficulties commonly seen in ADHD may be due to weaknesses in skills such as impulse control, working memory, recognising emotions, and perspective taking. Given teenagers with autism often present with similar weaknesses and social difficulties, it is not known whether the social difficulties experienced by teenagers with ADHD have the same underlying causes. If so, this may suggest that social difficulties in ADHD represent undiagnosed autism, or low-level autism traits. This study compared these key skills in teenagers with clinical ADHD symptoms who experience social difficulties, with those who don’t experience social difficulties, and those with co-occurring autism. The results showed that social difficulties in ADHD and autism appear to have different underlying causes, suggesting that the social difficulties seen in teenagers with ADHD are unique to this disorder, and are not due to underlying autism, or autism traits. This highlights the need for social skills interventions, particularly those that target impulse control, emotion recognition and working memory, that are specifically tailored to the needs and weaknesses of teenagers with ADHD, rather than using existing interventions designed for teenagers with autism.

Affective Region Recognition and Fusion Network for Target-Level Multimodal Sentiment Classification

With the development of multimodal sentiment analysis tasks, target-level/aspect-level multimodal sentiment analysis has received more attention, aiming to intelligently judge the sentiment orientation of target words using visual and textual information. Most existing methods mainly rely on combining the whole image and text while ignoring the implicit affective regions in the image. We introduce a novel affective region recognition and fusion network (ARFN) for target-level multimodal sentiment classification, which focuses more on the alignment of multimodal fusion of visual and textual. First, to produce a visual representation with sentiment elements, ARFN employs the Yolov5 algorithm to extract the object region of the image and selects the emotional area according to the strategy. Next, this method learns target-sensitive visual representations and text semantic representations through a multi-head attention mechanism and pre-trained models BERT, respectively. Moreover, ARFN fuses textual and visual representations through a multimodal interaction method to perform target-level multimodal sentiment classification tasks. We achieve state-of-the-art performance on two available multimodal Twitter datasets, and experimental results show the effectiveness of our approach.

Personalized Feature Importance Ranking for Affect Recognition From Behavioral and Physiological Data

Designing affect-based personalized technology involves dealing with large datasets. Machine learning (ML) algorithms are employed to predict affect and similar human factors from in-game metrics, behavioral patterns, or physiological responses. The classification performance is usually presented as a global point estimate without providing user-specific interpretations. This approach is incompatible with effective personalization in games because it disregards the variability of body responses between players. This paper proposes a methodology to classify subjective human factors from large multimodal data. A public VR dataset (CEAP-360VR) was used to extensively compare three ML classifiers and five feature importance techniques. The produced models could reduce the original feature space by 82% (from 113 to 20 features) without compromising predictive performance (F1 score). A random forest (RF) using forward sequential feature selection (fSFS) yielded the best prediction of binary valence (F1=0.761) and arousal (F1=0.748). Finally, feature importance rankings are discussed with emphasis on global and user-specific patterns that may improve affect recognition. The proposed methodology is envisioned to help game designers and researchers create customized user-centric games and VR experiences inferring possible explanations from multimodal datasets.

An (E)Affective Bind: Situated Affectivity and the Prospect of Affect Recognition

Several prominent criticisms have recently challenged the possibility of algorithmically determining or recognising human affect. This paper ethically evaluates one underexplored avenue for overcoming such deficiencies in categorical affect recognition technologies (ARTs). Specifically, the emerging literature on “situated affectivity” offers valuable guidance on three fronts. First, it conceptually substantiates existing criticism by stressing the contextual dimensions of human affect. Second, in highlighting the breadth of information required for more accurate ARTs, it suggest that they may necessitate penetrating forms of surveillance the ethical literature has already provided good reason to resist. Third, it corroborates postphenomenological insights concerning the socially co-constituting character of technology; and thereby grounds the need for a “situated ethics” sensitive to the ways that ARTs might reconstruct human affectivity (and reinforce existing power hierarchies). The deep worry is that this concern is likely to persist even if concerns over accuracy and surveillance are mitigated. A detailed consideration of this novel ethical concern suggests that the ethically acceptable use of ARTs may only be possible in highly contained settings. Yet doubts over such containment places advocates of ARTs in a "double bind” they need to address.

Systematic Review of the Link Between Social Cognition and Suicidal Ideation and Behavior in People With Serious Mental Illness.

People with serious mental illness (SMI; psychotic and affective disorders with psychosis) are at an increased risk of suicide, yet there is limited research on the correlates of suicide in SMI. Social cognitive impairments are common among people with SMI and several studies have examined social cognition and suicidal ideation (SI) and behavior. This systematic review aims to evaluate the links between various domains of social cognition, SI, and suicidal behavior in SMI. Electronic databases (PubMed and PsycInfo) were searched through June 2023. Records obtained through this search (N = 618) were screened by 2 independent reviewers according to inclusion criteria. Relevant data were extracted, and study quality was assessed. Studies (N = 16) from 12 independent samples were included in the systematic review (N = 2631, sample sizes ranged from N = 20 to N = 593). Assessments of social cognition and SI and behavior varied widely between studies. Broadly, effects were mixed. Better emotion recognition of negative affect was linked to SI and a history of suicide attempts, though there is little consistent evidence for the relationship of emotion recognition and SI or behavior. On the other hand, better theory of mind ability was linked to SI and a history of suicide attempts. Furthermore, negative attributional bias was linked to current SI, but not a history of SI or attempt. This review suggests mixed associations between social cognition, SI, and behavior in SMI. Future research should evaluate additional mediators and moderators of social cognition and suicide, employing prospective designs.

Resisting Deportation Live: Affective Witnessing and Recognition of Airplane Deportation Protests

Resisting Deportation Live: Affective Witnessing and Recognition of Airplane Deportation Protests

An enhanced affective computing-based framework using machine learning &amp; medical IoT for the efficient pre-emptive decision-making of mental health problems

Globally, the two main causes of young people dying are mental health issues and suicide. A mental health issue is a condition of physiological disorder that inhibits with the vital process of the brain. The amount of individuals with psychiatric illnesses has considerably increased during the past several years. The majority of individuals with mental disorders reside in India. The mental illness can have an impact on a person’s health, thoughts, behaviour, or feelings. The capacity of controlling one’s thoughts, emotions, and behaviour might help an individual to deal with challenging circumstances, build relationships with others, and navigate life’s problems. With a primary focus on the healthcare domain and human-computer interaction, the capacity to recognize human emotions via physiological and facial expressions opens up important research ideas as well as application-oriented potential. Affective computing has recently become one of the areas of study that has received the greatest interest from professionals and academics in a variety of sectors. Nevertheless, despite the rise in articles published, the reviews of a particular aspect of affective computing in mental health still are limited and have certain inadequacies. As a result, a literature survey on the use of affective computing in India to make decisions about mental health issues is discussed. As a result, the paper focuses on how traditional techniques used to monitor and assess physiological data from humans by utilizing deep learning and machine learning approaches for humans’ affect recognition (AR) using Affective computing (AfC) which is a combination of computer science, AI, and cognitive science subjects (such as psychology and psychosocial).

Social participation of school-aged survivors of pediatric brain tumors: A daily diary report.

Difficulties with social functioning are common among survivors of pediatric brain tumors. Social participation is an understudied measure of social functioning that is associated with emotional health across the lifespan. This paper uses a diary method to assess the social participation of survivors of pediatric brain tumors in middle childhood. Survivors of pediatric brain tumors (N=47; age 10.6±1.4years; 51.1% male, 89.4% White) who were 5.3 (SD=2.4, range: 2-9.9) years post therapy completed a daily diary assessment of social interaction (5-7days) and an objective measure of facial affect recognition. The participant's caregiver completed the NIH Toolbox Emotion Measures and a background information questionnaire. Overall, frequency and quality of reported social interactions were low for survivors, with a large subset of survivors (n=16, 34%) endorsing fewer than 10 social interactions over the course of a typical school week, and almost half of parents (48.9%) reporting that their child participates in zero social activities outside of school during a typical week. Participants engaged in more positive social participation exhibited stronger social skills (facial affect recognition (F(2,44) =4.85, p<.05). School-aged survivors of pediatric brain tumors seemed to be infrequently engaged in social participation and quality interaction with peers. More specifically, the interactions most commonly reported on the diary assessment are not indicative of friendship development and maintenance. Survivors of pediatric brain tumors would likely benefit from interventions designed to increase quality time spent with peers.

Video-based continuous affect recognition of children with Autism Spectrum Disorder using deep learning

Affect recognition is currently an active research area for machine learning researchers. Researchers are paying more and more attention to the assessment and the classification of emotions in people with mental disorders in order to propose monitoring healthcare systems and then to better assist patients. In this paper, a supervised learning method to classify Autism Spectrum Disorder (ASD) and to evaluate affect levels among autistic children is proposed. The use of methodologies for clinical patient monitoring and management is the focus of our research. YouTube video frames acquired in unconstrained environments and conditions of autistic children demonstrating typical autistic behaviors, as well as images of neurotypical people are used to evaluate the performance of the proposed approach. This paper also proposes an extended version of a dataset, including the additional affect labels corresponding to the affect levels of autistic children. Finally, experiments using different models were conducted to determine which architecture performed best. These experiments have shown very promising results for continuous affect recognition of children with ASD. Results show also that using a model that has only been trained on neurotypical subjects does not perform as great when the subjects are ASD children.

Recent advances in the discipline of text based affect recognition

Recent advances in the discipline of text based affect recognition

38 Assessing Memory for Emotions Separately from Emotion Recognition

Objective:Accurate processing of facial displays of emotion is critical for effective communication. A robust literature has documented impairment in the ability to recognize facial affect in people with traumatic brain injury (TBI), but research is scarce about memory for facial affect. Disruptions in recognizing and remembering the emotions of others can undermine relationship quality and may result in psychosocial dysfunction. Importantly, the extant literature indicates that facial affect recognition dissociates from other cognitive abilities such that it is likely a distinct neuronal process. Thus, explicit measurement of affect recognition and memory for emotions may be critical for implementing and refining rehabilitation interventions. The present study examined the relationship between recognition and memory for emotions using a novel computerized task and explored its associations with other cognitive abilities.Participants and Methods:Participants were adults who were neurologically healthy (n = 31) or had a history of moderate to severe TBI (n = 26). The battery included the novel Assessment of Facial Affect Recognition and Memory (AFARM), Cambridge Face Memory Test (face memory without emotion), Wechsler Test of Adult Reading, Rey Auditory Verbal Learning Test, Judgment of Line Orientation, Oral Symbol Digit Modalities, Digit Span, FAS, Animal Fluency, and the Affect Intensity Measure (experienced emotion). Spearman correlations examined the relationship of AFARM performance with the test battery. Logistic regression models examined whether immediate-delay (ID-EM) and long-delay face emotion-memory (LD-EM) accounted for unique variance in group membership beyond recognition accuracy of facial affect and memory for faces.Results:AFARM demonstrated relationships with neuropsychological and mood variables in the expected directions across and within groups, with the strongest associations observed for memory for verbal information (rs = .51 to .58) and processing speed (rs = .48 to .57). Consistent with traditional list-learning tests, ID- and LD-EM were highly correlated (r = .85). Experienced affect intensity was inversely associated with ID-EM (r = -.29) and LD-EM (r = -.38) but not with recognition accuracy (r = -.10). Logistic regression examining ID-EM was significant, χ2(3) = 26.05, p &lt; .001, Nagelkerke R2 = .49. ID-EM accounted for unique variance in group status (p = .006; OR = 0.65) after accounting for recognition accuracy and face memory. Similarly, the model examining LD-EM was significant χ2(3) = 27.70, p &lt; .001, Nagelkerke R2 = .43; LD-EM was significant after accounting for other variables (p = .017; OR = 0.69).Conclusions:The findings are consistent with the hypothesis that memory for emotions represents a unique component of social cognition that is separate from recognition. Accuracy in identifying emotions, face recognition memory, and memory for emotions are strongly related but not wholly redundant processes. Consistent with prior literature, subjective experience of emotion had substantial effects on objective performance tasks, indicating that an individual's intense experience of their own emotions can disrupt sensitivity to the emotions of others. Future research should assess the extent to which memory for emotions relates to psychosocial outcomes such as the quality and quantity of interpersonal relationships.