Basic Emotions Research Articles

On role models and Joe LeDoux.

Joseph LeDoux is a pioneering neuroscientist who has made profound discoveries that continue to impact our understanding of the neural basis of emotion and memory, particularly the role of the amygdala in threat conditioning. LeDoux's trailblazing and elegant studies were some of the first to examine the circuit basis of behavior. His work combined techniques to trace pathways into and out of the amygdala important for threat conditioning and related behaviors. Since that time, these types of circuit tracing studies have exploded in popularity across neuroscience, and I would argue, we all owe a debt to LeDoux for this. LeDoux has made numerous additional contributions to neuroscience and, by bringing emotion back to neuroscience, has helped unite neuroscience with psychology. A gifted writer with a knack for communicating complicated scientific ideas in an accessible manner, LeDoux has become an ambassador of science who uses his love of music to help educate and inspire. Perhaps more important than these laudable scientific achievements, LeDoux is also a true "gentleman" of science, showing that science need not be a contact sport. Here, I give a personal account on why Joseph LeDoux is one of my scientific role models.

Decline of Affective Prosody Recognition With a Positivity Bias Among Older Adults: A Systematic Review and Meta-Analysis.

Understanding how older adults perceive and interpret emotional cues in speech prosody contributes to our knowledge of cognitive aging. This study provides a systematic review with meta-analysis to investigate the extent of the decline in affective prosody recognition (APR) among older adults in terms of overall and emotion-specific performance and explore potential moderators that may cause between-studies heterogeneity. The literature search encompassed five electronic databases, with a specific emphasis on studies comparing the APR performance of older adults with that of younger adults. This comparison was focused on basic emotions. Meta-regression analyses were executed to pinpoint potential moderators related to demographic and methodological characteristics. A total of 19 studies were included in the meta-analysis, involving 560 older adults with a mean age of 69.15 years and 751 younger adults with a mean age of 23.02 years. The findings indicated a substantial negative effect size (g = -1.21). Furthermore, the magnitude of aggregated effect sizes showed a distinct valence-related recognition pattern with positive prosody exhibiting smaller effect sizes. Language background and years of education were found to moderate the overall and emotion-specific (i.e., disgust and surprise) performance effect estimate, and age and gender significantly influenced the effect estimate of happiness. The results confirmed a significant decline in APR ability among older adults compared to younger adults, but this decline was unbalanced across basic emotions. Language background and educational level emerged as significant factors influencing older adults' APR ability. Moreover, participants with a higher mean age exhibited notably poorer performance in recognizing happy prosody. These findings underscore the need to further investigate the neurobiological mechanisms for APR decline associated with aging. https://doi.org/10.23641/asha.26407888.

The impact of disgust learning on memory processes for neutral stimuli: a classical conditioning approach

ABSTRACT Disgust is a basic emotion that promotes pathogen avoidance and can contaminate nearby neutral stimuli. This study investigates how neutral stimuli, which have acquired disgust value through classical conditioning, are processed in episodic memory. The Category Conditioning paradigm was utilised to assign emotional significance to neutral stimuli, followed by a recognition test conducted immediately or 24 h after conditioning (Experiment 1). The results revealed that neutral stimuli that acquired disgust value were recognised with greater accuracy and higher liberal bias compared to other neutral stimuli in the recognition test conducted after 24 h, but not immediately. Present study also indicates that the memory enhancement observed with disgust did not manifest in the context of fear (Experiment 2). Additionally, the results varied when neutral stimuli associated with disgust were presented with disgusting stimuli in recognition test (Experiment 3). Thus, the present study demonstrates that the memory advantage of disgust extends to associated stimuli when they are presented in a list without disgusting stimuli.

Multi-head Network based Students Behaviour Prediction with Feedback Generation for Enhancing Classroom Engagement and Teaching Effectiveness

Emotions are pivotal in the learning process, highlighting the importance of identifying students' emotional states within educational settings. While neural network models, particularly those rooted in deep learning, have demonstrated remarkable accuracy in detecting primary emotions like happiness, sadness, fear, disgust, and anger from facial expressions in videos, these emotions occur infrequently in learning environments. Conversely, cognitive emotions such as engagement, confusion, frustration, and boredom are significantly more prevalent, transpiring five times more frequently than basic emotions. However, unlike basic emotions which are relatively distinct, cognitive emotions present a subtler distinction, necessitating the utilization of more sophisticated models for accurate recognition. The proposed work presents an efficient Facial Expression Recognition (FER) model for monitoring the student engagement in a learning environment by considering their facial expressions like boredom, frustration, confusion and engagement. The proposed methodology includes certain pre-processing steps followed by facial expression recognition founded on Efficient-Net B3 CNN in which the learning parameters are optimized using Circle-Inspired Optimization Algorithm (CIOA). Finally, the post processing stage estimates the frame-wise group engagement level (GEL) of students based on certain expression labels. Based on the acquired results, it is noted that the suggested Efficient-Net B3 CNN-CIOA based FER model provides promising results in terms of accuracy by 99.5%, precision by 99.2%, recall by 99.5% and f1-score by 99.6%, when compared with some state-of-art facial expression recognition approaches. Also, the suggested approach computational complexity is very much less than the compared existing approaches.

Embodiment of emotions in adolescents’ musical expression

Music has been actively studied from the perspectives of emotional expression and body movement, but not during adolescence. The current study addressed music as a forum for adolescent embodied emotion expression. Based on prior research, we hypothesised that adolescents would be able to differentiate between emotions in their music-related expressive body movements based on valence and arousal characteristics. Participants ( N = 60, 17 male, mean age 14.72 years) played djembe to express five basic emotions (happiness, tenderness, sadness, anger, fear) while body movements were motion captured. Correlations of movement features with emotion-regulation tendencies, as measured by the Emotion Regulation Questionnaire (ERQ), were additionally explored. Adolescents demonstrated great capacity to use all measured movement features to express emotions: movement speed, variance, area and length all differed significantly between emotions. In particular, the results confirm the hypothesised connection of high arousal to high speed and acceleration and further suggest that positive valence relates to wider area and longer performance. In addition, adolescents scoring high on cognitive reappraisal gave faster and more stable performances. We discuss creative body movement as part of youth emotional development.

Similar, not universal: the cognitive dimensions of conceptual prototypes of basic emotions in English and in Polish.

The current study explores the differences in conceptualisation of the prototypical basic emotion lexicalisations (anger, disgust, fear, joy, sadness, surprise) in English and in Polish. Measures of concreteness, imageability and context availability were collected and analysed across the six semantic categories of basic emotions, across different parts of speech and between the self-determined genders of the study participants. The initial results indicate that within these cognitive dimensions the conceptualisations of basic emotions in English and in Polish are only similar on the more general but not the higher levels of conceptualisation. The folk-psychological division between positive and negative emotions and the grammatical parts of speech reveal similar patterns in basic emotion concepts in both Polish and in English. However, on the higher levels of conceptualisations that include specific basic emotion semantic categories and self-identified gender, marked language-specific differences become apparent. Different negative emotions drive the statistical differences in Polish and in English, and the gender effects on the measures of concreteness, imageability and context availability are opposite from one language to the other. In other words, basic emotions may be broadly mutually intelligible, but not exactly the same when communicated across languages and cultures.

Comparison of emotional responses between young and elderly individuals under different sound source conditions

In the paper, four types of sound sources were selected, including animal sounds, human behavior sounds, natural sounds, and machine sounds. Each type of sound source contains eight types of sound sources. Six discrete emotions with anger, happiness, sadness, disgust, surprise, and fear are evaluated by young and elderly people through listening and questionnaire surveys under these sound source conditions. The results showed that machine sounds are more likely to cause emotions of anger and disgust, Natural sounds are more likely to trigger feelings of joy, sadness, surprise, or fear. Most of the six basic emotions are correlated with each other. In terms of the difference in evaluation between young and elderly people, most emotional response trends and degrees to various sound sources between young and elderly people are similar basically, but there are some sound sources that are obviously more likely to trigger related emotional reactions in young people, and less likely to trigger emotional reactions in elderly people. It provides a certain experimental reference basis for the sound environment design of urban public spaces.

Multisession tDCS combined with intrastimulation training improves emotion recognition in adolescents with autism spectrum disorder

Previous studies indicate that transcranial direct current stimulation (tDCS) is a promising emerging treatment option for autism spectrum disorder (ASD) and its efficacy could be augmented using concurrent training. However, no intrastimulation social cognition training for ASD has been developed so far. The objective of this two-armed, double-blind, randomized, sham-controlled clinical trial is to investigate the effects of tDCS combined with a newly developed intrastimulation social cognition training on adolescents with ASD. Twenty-two male adolescents with ASD were randomly assigned to receive 10 sessions of either anodal or sham tDCS at F3/right supraorbital region together with online intrastimulation training comprising basic and complex emotion recognition tasks. Using baseline magnetic resonance imaging data, individual electric field distributions were simulated, and brain activation patterns of the training tasks were analyzed. Additionally, questionnaires were administered at baseline and following the intervention. Compared to sham tDCS, anodal tDCS significantly improved dynamic emotion recognition over the course of the sessions. This task also showed the highest activations in face processing regions. Moreover, the improvement was associated with electric field density at the medial prefrontal cortex and social awareness in exploratory analyses. Both groups showed high tolerability and acceptability of tDCS, and significant improvement in overall ASD symptoms. Taken together, multisession tDCS improved dynamic emotion recognition in adolescents with ASD using a task that activates brain regions associated with the social brain network. The variability in the electric field might diminish tDCS effects and future studies should investigate individualized approaches.

Multimodal Spatio-Temporal Framework for Real-World Affect Recognition

Deep learning models show great potential in applications involving video-based affect recognition, including human-computer interaction, robotic interfaces, stress and depression assessment, and Alzheimer's disease detection. The low complex Multimodal Diverse Spatio-Temporal Network (MDSTN) has been analysed to effectively capture spatio-temporal information from audio-visual modalities for affect recognition using the Acted Facial Expressions in the Wild (AFEW) dataset. The scarcity of data is handled by data augmented parallel feature extraction for visual network. Visual features extracted by carefully reviewing and customizing Convolutional 3D architecture over different ranges are combined to train a neural network for classification. Multi-resolution Cochleagram (MRCG) features from speech, along with spectral and prosodic audio features, are processed by a supervised classifier. The late fusion technique is explored to integrate audio and video modalities, considering their processing over different temporal spans. The MDSTN approach significantly boosts the accuracy of basic emotion recognition to 71.54% on the AFEW dataset. It demonstrates exceptional proficiency in identifying emotions such as disgust and surprise, thus exceeding current benchmarks in real-world affect recognition.

인공지능 로봇의 도덕감정 함양 가능성에 대한 검토 -초등 도덕과를 중심으로-

This study examined the possibility of cultivating moral emotions of artificial intelligence robots that are developing recently. The 2022 revised elementary moral curriculum provides an opportunity for thought by asking questions about the relationship between artificial intelligence robots and humans. For the study, we first investigated the nature and expression process of emotions from ancient Western philosophy to modern brain science and neuroscience. Based on this, we explored what kind of emotional view the currently developed artificial intelligence robots express emotions based on. As a result, current artificial intelligence robots showed responses to humans using information accumulated through facial movements and facial expressions, focusing on basic emotions, according to Ekman's theory. This may be seen as a passive form based on human physical characteristics. However, moral emotion has a complex and active character that combines understanding of moral problem situations, empathy for others, and appropriate responses to situations. Accordingly, it was recommended to clearly teach the status and limitations of artificial intelligence robots in the field of current moral emotions in elementary moral education and educate students to form desirable relationships with artificial intelligence robots.

Validity Study of an Emotional Face-Database in Iranian Community

Objective: To investigate the accuracy of facial emotion recognition in the Iranian community, a face database validated in this community is required. To do this, we conducted a validation study on the Radboud face database. The primary objective of this study was to evaluate the accuracy of recognizing emotions through faces in an Iranian sample and then to choose the pictures with high agreement in terms of detecting emotions. Method: This is a cross sectional study recruiting a total number of 142 males and females aged between 20 and 50 years old (Mean ± SD of age 31.7 ± 7.07). The participants were instructed to detect the type of emotion of each face as well as its valence and arousal. The percentage of participants’ agreement on evaluating each picture was assessed. To evaluate the effect of different variables on participants’ accuracy, one way and repeated measure ANOVA analyses were also used. Results: Emotional faces were recognized by around 84% of the participants. The highest accuracy belongs to happy (Mean ± SD of 98 ± 6.1%) and the lowest one to neutral (75 ± 18.06%) faces. The accuracy for detecting other emotions were as follows: sad (91 ± 8.7%), surprised (87 ± 10.64%), angry (77 ± 15.6%), and fearful (76 ± 15.26%). Additionally, we found no differences between male and female participants in terms of recognizing emotions. Then we selected the pictures with high agreement (above 85 percent) in labeling emotions among the participants. Conclusion: The current study provided a valid emotional face database based on Iranian participants’ responses in terms of recognizing basic emotions. The selected pictures can be used in designing tasks to evaluate emotion recognition ability in clinical and nonclinical populations. It can also be used in designing applications to improve detecting emotion in clinical samples such as individuals with autism spectrum disorder.

Evaluation of emotion classification schemes in social media text: an annotation-based approach

BackgroundEmotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis?MethodsWe proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval’s four emotions, Ekman’s six basic emotions, ancient China’s Seven Emotions, Plutchik’s eight primary emotions, and GoEmotions’ 27 emotions.ResultsThe AHP evaluation result shows that Ekman’s scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman’s six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions’ joy aligns with Ekman’s happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman’s corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman’s, such as approval, love, pride, amusement, etc.ConclusionEkman’s scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus.

How to Make Flower Borders Benefit Public Emotional Health in Urban Green Space: A Perspective of Color Characteristics

The emotional health benefits of urban green space have been widely recognized. Flower borders, as a perennial plant landscape, have gradually become a current form of plant application in urban green spaces due to their rich color configurations. However, the related research primarily focuses on the impact of urban green spaces on public health, with relatively little attention given to how the colors of flower borders affect public emotional health. This study explored the relationship between the flower borders color characteristics and the public emotional health. In this study, 24 sample images were used as experimental materials, which selected based on their color richness and harmony. Additionally, face recognition technology and online random questionnaires were utilized to measure the public basic emotions and pleasure, respectively. The result shows that, based on the HSV color model and expert recommendations, 19 color characteristics were identified. The correlation analysis of the results from the public emotion with these color characteristics revealed that 13 color characteristics correlated with public emotional pleasure. Among them, blue, neutral purple, and low saturation were positively correlated. Through factor analysis, these thirteen color characteristics were summarized and categorized into four common factors (F1–F4), three of which are related to color. They are “low saturation of blue-violet percentage” (F1), “color configuration diversity” (F2), “bright red percentage” (F3), and “base green percentage” (F4), with F1 having the largest variance explained (27.88%). Finally, an evaluation model of color characteristics was constructed based on the variance explained by these four factors, which was demonstrated to effectively predict the level of public emotional pleasure when viewing flower borders. The results shed light on the effects of color characteristics on public emotions and provide new perspectives for subsequent flower border evaluations. Our results provide a valuable reference for future flower border color design, aiming to better improve public emotional health.

Linguistic expression of basic emotions in the modern English-language political media discourse

The article studies the linguistic expression of basic emotions in the modern political media discourse based on the material of the English-language media. The paper aims to identify the verbal expression means of basic emotions on the material of political articles from the English-speaking mass media. This aim is achieved by using the descriptive method, as well as lexical and semantic analysis, lexicographic and content analysis, discursive and grammatical analysis, stylistic and interpretive analysis. The article confirms the hypothesis that in the modern political media discourse lexical and stylistic means of linguistic expression of negative emotions prevail. The scientific novelty of the study is to identify the lexical features of basic emotions’ linguistic expression in the political media discourse in the XXI century. It has been established that along with the names of basic emotions, media texts use numerous synonyms for the names of emotions due to the various stylistic shades that they express. With their help, the addresser creates a certain emotional and psychological atmosphere in the media text, in which the addressee, who does not have critical thinking, forms the point of view necessary for the addresser. As a results of the study, it was revealed that the emotional nature of the presentation of information in media texts prevails over factual, especially in original articles. Theoretically, the results of the study contribute to understanding the role of emotions in political media activities, as well as establishing the connection of language with cognitive processes. Analysis of the verbal expression of emotions in different linguistic cultures allows us to trace how they are expressed and interpreted, and how they affect intercultural communication. In practical terms, the study of verbal expression of emotions in different types of media, primarily in a political media discourse, will create more effective media texts that will meet the needs and interests of both addressers and addressees. Also, the results of the study can be used in teaching courses in media linguistics, political linguistics, psycholinguistics and discursology. The perspective of the study is to identify grammatical and stylistic means of linguistic expression of emotions in the modern English-language political media discourse, i.e. in a comparative aspect.

Ethical leadership and reporting behavior of employees: social cognitive perspective of morality on the bases of emotions

Ethical leadership and reporting behavior of employees: social cognitive perspective of morality on the bases of emotions

2D/3D Expression Generation Using Advanced Learning Techniques and the Emotion Wheel

Facial expression analysis is a critical component in numerous applications, ranging from human- computer interaction to digital character animation. Despite the availability of extensive datasets, most focus predominantly on basic emotions, limiting the expressiveness and applicability of models trained on them. This article introduces a novel approach to generating compound emotional expressions by leveraging the Emotion Wheel, a principle that captures the complex interrelations between basic emotions. Our method integrates the EMOCA (Emotion Driven Monocular Face Capture and Animation) [1] framework, which enhances 3D facial reconstruction by incorporating emotion recognition, stacking models [2, 54] with a sophisticated expression blending algorithm to synthesize nuanced 2D and 3D facial animations. Utilizing the VKIST dataset, which includes high-resolution facial images of Vietnamese individuals, we build a comprehensive database of emotion parameters. Through principal component analysis (PCA) and correlation-driven blending, our approach not only enhances the realism of generated facial expressions but also preserves the subtle nuances that are characteristic of human emotions. Experimental results demonstrate that our method consistently outperforms traditional linear interpolation techniques, producing more distinct and recognizable blended expressions. A user study further validates the naturalness and quality of the generated expressions, with average ratings indicating a strong preference for the proposed method over existing approaches. These findings suggest significant potential for improving emotional expressiveness in both static and dynamic digital character representations.

Extending speech emotion recognition systems to non-prototypical emotions using mixed-emotion model

In the conventional approach to speech emotion recognition (SER), the classifier is usually trained on acted emotional speech data to predict individual basic emotions. In this work, we extend the SER systems with the realistic assumption of the coexistence of multiple basic emotions in an utterance. We utilize the MSP-Podcast database for developing the SER system, which contains spontaneous speech utterances. From the primary and secondary emotion annotations of this database, we organize six prototypical and seven non-prototypical emotions. We then propose the mixed-emotion model and express the non-prototypical emotions as a linear combination of prototypical emotions. The combination weights of the mixed-emotion model are computed using a normalized dominance scale based algorithm inspired by the integration of basic emotion theory and dimensional emotion theory in human psychology. We first train a prototypical SER model using the ECAPA-TDNN architecture. The softmax predictions from this model serve as emotion profile inputs to the mixed-emotion model, which then predicts the non-prototypical emotions. Assuming the coexistence of multiple emotions, we only apply the utterances with uniform emotion profiles to the mixed-emotion model. The developed system continues with the conventional SER model if the emotion profile tends to delta function owing to the probable occurrence of a single prototypical emotion. We develop the proposed mixed-emotion model based SER framework using MFCC and wav2vec 2.0 extracted features. Further, we show that due to human perception variations, there exist prominent annotation variations in the non-prototypical emotion ground truths. To address that, we extend the supervised evaluation protocols in four different formulations that capture the subjective variability at different levels. The proposed system shows a best-case performance improvement of 7.10% and 8.39% over the conventional prototypical SER model for the MFCC and wav2vec 2.0 features, respectively.

Social cognition, psychosocial development and well-being in galactosemia

BackgroundClassic galactosemia is a rare inherited metabolic disease with long-term complications, particularly in the psychosocial domain. Patients report a lower quality of social life, difficulties in interactions and social relationships, and a lower mental health. We hypothesised that social cognition deficits could partially explain this psychological symptomatology. Eleven adults with galactosemia and 31 control adults participated in the study. We measured social cognition skills in cognitive and affective theory of Mind, and in basic and complex emotion recognition. We explored psychosocial development and mental well-being.ResultsWe found significant deficits on all 4 social cognition measures. Compared to controls, participants with galactosemia were impaired in the 2nd-order cognitive theory of mind, in affective theory of mind, and in basic and complex emotion recognition. Participants with galactosemia had a significant delay in their psychosexual development, but we found no delay in social development and no significant decrease in mental health.ConclusionSocial cognition processes seem impaired among our participants with galactosemia. We discuss the future path research may follow. More research is needed to replicate and strengthen these results and establish the links between psychosocial complications and deficits in social cognition.

Age-related changes in emotion recognition across childhood: A meta-analytic review.

Children's ability to accurately recognize the external emotional signals produced by those around them represents a milestone in their socioemotional development and is associated with a number of important psychosocial outcomes. A plethora of individual studies have examined when, and in which order, children acquire emotion knowledge over the course of their development. Yet, very few attempts have been made to summarize this body of work quantitatively. To address this, the present meta-analysis examined the age-related trajectories of emotion recognition across childhood and the extent to which typically developing children's recognition of external emotional cues (in the face, voice, and body) is influenced by a host of participant-, task-, and stimulus-related factors. We analyzed children's emotion recognition overall (independent of specific emotion categories) and for specific basic emotions. In total, k = 129 individual studies, investigating a total of N = 31,101 2-12-year-old children's emotion recognition abilities were included in our analyses. Children's recognition accuracy across all emotion categories was significantly above chance and improved with age in the same manner for all emotions. Emotion recognition accuracy was also moderated by region of study and task type. The order in which children became proficient at identifying specific emotions was consistent with previous qualitative reviews: Happiness was the easiest emotion to recognize, and disgust and fear were the most difficult to recognize across age. Task- and stimulus-related moderator variables also influenced specific emotion categories in different ways. We contextualize these results with regard to children's socioemotional development more broadly, and we discuss how our findings can be used to guide researchers and practitioners interested in children's social skills. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

‘The mirror of the soul?’ Inferring sadness in the eyes

The eyes are widely regarded as the mirror of the soul, providing reliable nonverbal information about drives, feelings, and intentions of others. However, it is unclear how accurate emotion recognition is when only the eyes are visible and whether inferring of emotions is altered across healthy adulthood. To fill this gap, the present piece of research was directed at comparing the ability to infer basic emotions in two groups of typically developing females that differed in age. We set a focus on females seeking group homogeneity. In a face-to-face study, in a two-alternative forced choice paradigm (2AFC), participants had to indicate emotions for faces covered by masks. The outcome reveals that although the recognition pattern is similar in both groups, inferring sadness in the eyes substantially improves with age. Inference of sadness is not only more accurate and less variable in older participants, but also positively correlates with age from early through mid-adulthood. Moreover, reading sadness (and anger) is more challenging in the eyes of male posers. A possible impact of poser gender and cultural background, both in expressing and inferring sadness in the eyes, is highlighted.