Emotion Recognition Performance Research Articles

Adaptive Spatial–Temporal Aware Graph Learning for EEG-Based Emotion Recognition

An intelligent emotion recognition system based on electroencephalography (EEG) signals shows considerable potential in various domains such as healthcare, entertainment, and education, thanks to its portability, high temporal resolution, and real-time capabilities. However, the existing research in this field faces limitations stemming from the nonstationary nature and individual variability of EEG signals. In this study, we present a novel EEG emotion recognition model, named GraphEmotionNet, designed to enhance the accuracy of EEG-based emotion recognition through the incorporation of a spatiotemporal attention mechanism and transfer learning. The proposed GraphEmotionNet model can effectively learn the intrinsic connections between EEG channels and construct an adaptive graph. This graph’s adaptive nature is crucial in optimizing spatial–temporal graph convolutions, which in turn enhances spatial–temporal feature characterization and contributes to the process of emotion classification. Moreover, an integration of domain adaptation aligns the extracted features across different domains, further alleviating the impact of individual EEG variability. We evaluate the model performance on two benchmark databases, employing two types of cross-validation protocols: within-subject cross-validation and cross-subject cross-validation. The experimental results affirm the model’s efficacy in extracting EEG features linked to emotional semantics and demonstrate its promising performance in emotion recognition.

Exploring the Direction of English Translation of Environmental Articles Based on Emotional Artificial Intelligence Learning Models

Abstract In this paper, we improve the performance of machine translation emotion recognition by fusing word features and linguistic features of environmentally friendly text with LSTM neural networks. Multi-channel LSTM’s unbalanced emotion classification method can address the problem of unbalanced numbers of samples in each emotion category in the emotion classification task. The bilingual semantic similarity degree feature is added to the translation process of English environmental protection data so as to make the translation of English environmental protection data more accurate. The proposed method is validated through experiments, and the results show that the translation quality of the LSTM model is generally higher than that of other models, and the mean value of the translation quality of the LSTM on the test17 dataset is 50.4. In terms of environmental protection terminology, mistranslations, common word mistranslations, etc. There is a significant reduction of 2–3 times in the absolute number of the LSTM model compared to the traditional method. A total of 94,788 corpora were obtained from 10 environmental translations, such as Silent Spring and Solace of the Heart, and 2,311 favorite emotion words were identified. Comparative analysis of the samples reveals that most of the translation results of some environmental terms are in the range of 0.7–0.9, and the range of semantic overlay between the machine translation results of the LSTM model and the standard translations is large. Most users believe the model improves the translator’s translation level and diction accuracy.

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns effectively ensure the robustness of the emotion recognition system. In this work, to realize the efficient decoding of emotional EEG, we propose a graph learning system Graph Convolutional Network framework with Brain network initial inspiration and Fused attention mechanism (BF-GCN) inspired by the brain cognitive mechanism to automatically learn graph patterns from emotional EEG and improve the performance of EEG emotion recognition. In the proposed BF-GCN, three graph branches, i.e., cognition-inspired functional graph branch, data-driven graph branch, and fused common graph branch, are first elaborately designed to automatically learn emotional cognitive graph patterns from emotional EEG signals. And then, the attention mechanism is adopted to further capture the brain activation graph patterns that are related to emotion cognition to achieve an efficient representation of emotional EEG signals. Essentially, the proposed BF-CGN model is a cognition-inspired graph learning neural network model, which utilizes the spectral graph filtering theory in the automatic learning and extracting of emotional EEG graph patterns. To evaluate the performance of the BF-GCN graph learning system, we conducted subject-dependent and subject-independent experiments on two public datasets, i.e., SEED and SEED-IV. The proposed BF-GCN graph learning system has achieved 97.44% (SEED) and 89.55% (SEED-IV) in subject-dependent experiments, and the results in subject-independent experiments have achieved 92.72% (SEED) and 82.03% (SEED-IV), respectively. The state-of-the-art performance indicates that the proposed BF-GCN graph learning system has a robust performance in EEG-based emotion recognition, which provides a promising direction for affective computing.

Research on the application of vocal music teaching methods and techniques in colleges and universities based on proportional integral differentiation algorithm

Abstract With the continuous development of modern information technology, the combination of intelligent audio processing technology and vocal music teaching has gradually become a research hot spot. In this paper, we first build a vocal music teaching system based on music emotion and instrument recognition, optimize the support vector machine using the PSO algorithm, construct the music emotion recognition and instrument recognition method based on SVM, and control and optimize the vocal music teaching system through multi-objective proportional integral differentiation algorithm. Through the comparison experiments of different models of music emotion recognition and musical instrument recognition, the performance of music emotion recognition and musical instrument recognition of this paper’s model is explored. Then, the application effect analysis of the vocal music teaching system is carried out. The results show that the SVM model optimized by PSO has a more satisfactory effect on music emotion recognition, with a recognition accuracy 16.67% higher than the comparison model and an average adaptability of 70%–90%. In addition, this model has a higher instrument recognition rate of 18.17% and 7.45% compared to the other two models. After using the vocal teaching system, 63.04% of the students thought that it could promote learning, 47.83% of the student’s classroom interest increased, and more than 70% of the students were more satisfied with its functions. In this paper, the vocal teaching system can be applied to college vocal teaching to promote the improvement of the vocal teaching effect.

The Historical Requirements and Epochal Characteristics Analysis of the Chinese Periodization of Marxist Ethnic Theory Integrating Multimodal Information

Abstract In order to continuously explore the new realm of Chineseization and modernization of Marxism’s ethnic theory, this paper proposes the study of Marxism’s ethnic theory based on multimodal information. Using LXMERT to initially encode the multimodal corpus of Marxist Ethnic Theory, the encoded multimodal features of Marxist Ethnic Theory are inputted into the noise filtering layer for training, which can more accurately determine the relationship between images and texts. In order to fuse the information of text and image in Marxist national theory multimodal, the multimodal attention mechanism is used to fuse the text and image context vectors, and the quality and efficiency of Marxist national theory multimodal features are improved by decoding. The specific types of manifestations of Marxist national theory that combine multimodal information are analyzed, and the research on the Chineseization of Marxist theory is also examined. According to the data, Marxist national theory has a multimodal sentiment recognition rate of 81.6%. Compared to the better performance of unimodal emotion recognition. The correct rate of students’ cognition of the era characteristics of Marxist national theory reaches 69.27%, which indicates that the students’ knowledge of the era characteristics of the Chineseization of Marxist national theory is mainly considered to be in general agreement and has not yet reached a perfect state.

Selective Acoustic Feature Enhancement for Speech Emotion Recognition With Noisy Speech.

A speech emotion recognition (SER) system deployed on a real-world application can encounter speech contaminated with unconstrained background noise. To deal with this issue, a speech enhancement (SE) module can be attached to the SER system to compensate for the environmental difference of an input. Although the SE module can improve the quality and intelligibility of a given speech, there is a risk of affecting discriminative acoustic features for SER that are resilient to environmental differences. Exploring this idea, we propose to enhance only weak features that degrade the emotion recognition performance. Our model first identifies weak feature sets by using multiple models trained with one acoustic feature at a time using clean speech. After training the single-feature models, we rank each speech feature by measuring three criteria: performance, robustness, and a joint rank ranking that combines performance and robustness. We group the weak features by cumulatively incrementing the features from the bottom to the top of each rank. Once the weak feature set is defined, we only enhance those weak features, keeping the resilient features unchanged. We implement these ideas with the low-level descriptors (LLDs). We show that directly enhancing the weak LLDs leads to better performance than extracting LLDs from an enhanced speech signal. Our experiment with clean and noisy versions of the MSP-Podcast corpus shows that the proposed approach yields a 17.7% (arousal), 21.2% (dominance), and 3.3% (valence) performance gains over a system that enhances all the LLDs for the 10dB signal-to-noise ratio (SNR) condition.

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition.

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46 %, 92.15/5.13 %, 97.60/1.68 %(valence), and 97.48/1.42 %(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.

A Comprehensive Study on State-Of-Art Learning Algorithms in Emotion Recognition

The potential uses of emotion recognition in domains like human-robot interaction, marketing, emotional gaming, and human-computer interface have made it a prominent research subject. Better user experiences can result from the development of technologies that can accurately interpret and respond to human emotions thanks to a better understanding of emotions. The use of several sensors and computational algorithms is the main emphasis of this paper's thorough analysis of the developments in emotion recognition techniques. Our results show that using more than one modality improves the performance of emotion recognition when a variety of metrics and computational techniques are used. This paper adds to the body of knowledge by thoroughly examining and contrasting several state-of-art computational techniques and measurements for emotion recognition. The study emphasizes how crucial it is to use a variety of modalities along with cutting-edge machine learning algorithms in order to attain more precise and trustworthy emotion assessment. Additionally, we pinpoint prospective avenues for additional investigation and advancement, including the incorporation of multimodal data and the investigation of innovative features and fusion methodologies. This study contributes to the development of technology that can better comprehend and react to human emotions by offering practitioners and academics in the field of emotion recognition insightful advice.

Regression-based Analytical Approach for Speech Emotion Prediction based on Multivariate Additive Regression Spline (MARS)

Using regression analysis techniques for speech-emotion recognition (SER) is an excellent method of resource efficiency. The labeled speech emotion data has high emotional complexity and ambiguity, making this research difficult. The maximum average difference is used to consider the marginal agreement between the source and target domains without focusing on the distribution of the previous classes in the two domains. To address this issue, we propose emotion recognition in speech using a regression analysis technique based on local domain adaptation. The results of this study show that the model's generalization ability with the function of the local additive method is very good for improving speech emotion recognition performance. Even though it provides excellent benefits in resource efficiency, regression analytical techniques are rarely used in the SER field; however, we believe this method is the best solution for SER problems. Using the Multivariate Additive Regression Spline, this study developed a predictive model for the existence of angry and non-angry emotions (MARS). Using probability analysis of error values, this approach can overcome regression on data that is not typically distributed. This method yields an ideal basis function that significantly impacts changes in emotional form. This study generates a prediction model with a Mean Square Error (MSE) of 0.0130, a Generalized Cross Validation (GCV) value of 0.0062, and a R Square (RSQ) value of 0.9721, yielding test results with a 97% accuracy rate.

Incongruity-aware multimodal physiology signals fusion for emotion recognition

Various physiological signals can reflect the human’s emotional states objectively. How to take advantage of the common as well as complementary properties of different physiological signals in representing the emotional states is an interesting problem. Although various models have been constructed to fuse multimodal physiological signals for emotion recognition, the possible incongruity existing among different physiological signals in representing the emotional states and the redundancy resulted from the fusion, which may affect the performance of the fusion schemes seriously, were seldom considered. To this end, a fusion model, which can eliminate the incongruity among different physiological signals and reduce the information redundancy to some extent, is proposed. First, one physiological signal is chosen as the primary modality due to its prominent performance in emotion recognition, and the remaining physiological signals are viewed as the auxiliary modalities. Secondly, the Cross Modal Transformer (CMT) is adopted to optimize the features of the auxiliary modalities by eliminating the incongruity among them, and then Low Rank Fusion (LRF) is performed to eliminate information redundancy caused by fusion. Thirdly, the modified CMT (MCMT) is constructed to enhance the feature of the primary modality by that of each optimized auxiliary modality feature. Fourthly, Self-Attention Transformer (SAT) is performed on the concatenation result of all the enhanced primary modality features to take full advantage of the common as well complementary properties among them in representing the emotional states. Finally, the enhanced primary modality feature and the optimized auxiliary features are fused by concatenation for emotion recognition. Extensive experimental results on DEAP and WESAD datasets demonstrate that (i) The incongruity does exist among different physiological signals, and the CMT-based auxiliary modality feature optimization strategy can eliminate the incongruity prominently; (ii) The emotion prediction accuracy of the primary modality can be enhanced by the auxiliary modality; (iii) All the key modules in the proposed model, CMT, LRF, and MCMT, contribute to the performance enhancement of the proposed model; iv) The proposed model outperforms State-Of-The-Art (SOTA) models in emotion recognition task.

A multi-modal driver emotion dataset and study: Including facial expressions and synchronized physiological signals

To address the limitations of databases in the field of emotion recognition and to cater to the trend of integrating data from multiple sources, we have established a multi-modal emotional dataset based on spontaneous expression of drivers. By selecting emotional induction materials and inducing emotions before each driving task, facial expression videos and synchronous physiological signals of the drivers during driving were collected. The dataset includes records of 64 participants under five different emotions (neutral, happy, angry, sad, and fear), and the emotional valence, arousal, and peak time of all participants in each driving task were recorded. To analyze the dataset, spatio-temporal convolutional neural networks were designed to analyze the different modalities of data with varying durations in the dataset, aiming to investigate their performance in emotion recognition. The results demonstrate that the fusion of multi-modal data significantly improves the accuracy of driver's emotion recognition, with accuracy increases of 11.28% and 6.83% compared to using only facial video signals or physiological signals, respectively. Therefore, the publication and analysis of multi-modal emotional data for driving scenarios is crucial to support further research in the fields of multimodal perception and intelligent transportation engineering.

Convolutional neural network and ensemble machine learning model for optimizing performance of emotion recognition in wild

Convolutional neural network and ensemble machine learning model for optimizing performance of emotion recognition in wild

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes a teacher-student network without prior professional information for automated filtering of useful frame-level features by a gated mechanism and extracting high-level features by using knowledge distillation to capture the results of EEG emotion recognition from a teacher network and student networks. Then, the results from subnetworks are integrated by using the novel decision module, which, motivated by the voting mechanism, adjusts the composition of feature vectors and improves the weight of accurate prediction to optimize the integration effect. During training, an innovative data privacy protection mechanism is applied for avoiding data sharing, where each student network only inherits weights from all trained networks and does not inherit the training dataset. Here, the framework can be repeatedly optimized and improved by only training the next student subnetwork on new EEG signals. Experimental results show that our framework improves the accuracy of EEG emotion recognition by more than 5% and gets state-of-the-art performance for EEG emotion recognition in the subject-independent mode.

EESCN: A novel spiking neural network method for EEG-based emotion recognition

Background and ObjectiveAlthough existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG. MethodsWe propose an Emo-EEGSpikeConvNet (EESCN), a novel emotion recognition method based on spiking neural network (SNN). It consists of a neuromorphic data generation module and a NeuroSpiking framework. The neuromorphic data generation module converts EEG data into 2D frame format as input to the NeuroSpiking framework, while the NeuroSpiking framework is used to extract spatio-temporal features of EEG for classification. ResultsEESCN achieves high emotion recognition accuracies on DEAP and SEED-IV datasets, ranging from 94.56% to 94.81% on DEAP and a mean accuracy of 79.65% on SEED-IV. Compared to existing SNN methods, EESCN significantly improves EEG emotion recognition performance. In addition, it also has the advantages of faster running speed and less memory footprint. ConclusionsEESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints.

MSA-GCN: Multiscale Adaptive Graph Convolution Network for gait emotion recognition

Gait emotion recognition plays a crucial role in the intelligent system. Most existing approaches identify emotions by focusing on local actions over time. However, some valuable observational facts that the effective distances of different emotions in the time domain are different, and the local actions during walking are quite similar, are put aside in those methods. And this ignorance often ends up impairing performance of emotion recognition. To address the issues, a novel model, named MSA-GCN (MultiScale Adaptive Graph Convolution Network), is proposed to utilize the valuable observational knowledge for improving emotion recognition performance. In the proposed model, an adaptive spatio-temporal graph convolution is designed to dynamically select convolution kernels to learn the spatio-temporal features of different emotions. Moreover, a Cross-Scale Mapping Interaction mechanism (CSMI) is proposed to construct an adaptive adjacency matrix for high-quality aggregation of the multiscale information. Extensive experimental results on public datasets indicate that, compared with the state-of-the-art methods, the proposed approach achieves better performance in terms of emotion recognition accuracy, and shows the proposed approach is promising.

DSE-Mixer: A pure multilayer perceptron network for emotion recognition from EEG feature maps

Background:Decoding emotions from brain maps is a challenging task. Convolutional Neural Network (CNN) is commonly used for EEG feature map. However, due to its local bias, CNN is unable to efficiently utilize the global spatial information of EEG signals which limits the accuracy of emotion recognition. New Methods:We design the Dual-scal EEG-Mixer(DSE-Mixer) model for EEG feature map processing. Its brain region mixer layer and electrode mixer layer are designed to fuse EEG information at different spatial scales. For each mixer layer, the structure of alternating mixing of rows and columns of the input table enables cross-regional and cross-Mchannel communication of EEG information. In addition, a channel attention mechanism is introduced to adaptively learn the importance of each channel. Results:On the DEAP dataset, the DSE-Mixer model achieved a binary classification accuracy of 95.19% for arousal and 95.22% for valence. For the four-class classification across valence and arousal, the accuracies were HVHA: 92.12%, HVLA: 89.77%, LVLA: 93.35%, and LVHA: 92.63%. On the SEED dataset, the average recognition accuracy for the three emotions (positive, negative, and neutral) is 93.69%. Comparison with existing methods:In the emotion recognition research based on the DEAP and SEED datasets, DSE-Mixer achieved a high ranking performance. Compared to the two commonly used model in computer vision field, CNN and Vision Transformer(VIT), DSE-Mixer achieved significantly higher classification accuracy while requiring much less computational complexity. Conclusions:DSE-Mixer provides a novel brain map processing model with a small size, demonstrating outstanding performance in emotion recognition.

Hierarchical multimodal-fusion of physiological signals for emotion recognition with scenario adaption and contrastive alignment

The lack of complementary affective responses from both the central and peripheral nervous systems could limit the performance of emotion recognition with the single-modal physiological signal. However, when integrating multimodalities, a direct fusion may ignore the heterogeneous nature of multiple feature domains from one modality to another. Besides, there is a risk that the distribution of the multimodal physiological responses may vary across different affective scenarios for stimulating an identical emotional category. The inter-individual variation may also increase due to the superposition of the biometric information from the multimodal features. To tackle these issues, we present a hierarchical multimodal network for robust heterogeneous physiological representations (RHPRNet). First, we applied a spatial-frequency pattern extractor to identify the electroencephalogram (EEG) representations in both the spatial and frequency domains. Next, inter-domain and inter-modality affective encoders are separately applied to the statistic-complexity EEG features and multimodal peripheral features, respectively. All the learned representations are integrated via a hierarchical fusion module. To model the multi-peak patterns stimulated by different affective scenarios, we designed a scenario-adapting pretraining stage. A random contrastive training loss was also applied to mitigate the inter-individual variance. In the end, we performed adequate experiments to examine the performance of the RHPRNet based on three publicly available multimodal databases combined with two validation approaches.

Improvement of emotion classification performance using multi-resolution variational mode decomposition method

Automated speech emotion recognition (SER) has been gaining popularity among researchers for three decades because of its vast number of applications in the real world. It is helpful to improve the relationship between humans and machines, online marketing and education, customer relations, medical treatment, safe driving, online search, etc. Researchers adopt various methods to improve emotion recognition performance from speech signals, like using different combinations of features (acoustic, non-acoustic, or both), classifiers (machine learning, deep learning, or both). Our study tried to improve emotion classification performance using features based on the multi-resolution variational mode decomposition (MRVMD) method. We first decompose each signal frame into several sub-signals known as modes or intrinsic mode functions (IMFs) using the MRVMD method. Then the proposed features, multi-resolution variational mode mel-frequency cepstral coefficient (MRVMMFCC), multi-resolution variational mode approximate entropy (MRVMAE), and multi-resolution variational mode permutation entropy (MRVMPE), were extracted using the MRVMD-decomposed IMF signals. Finally, different combinations of the proposed features are used to classify the emotion using a deep neural network (DNN) classifier. From the experimental results, we found that combination of the proposed feature (MRVMMFCC + MRVMAE + MRVMPE) performs better than the other combination in recognizing emotion using speech signals. The proposed feature combination with a DNN classifier achieved an emotion classification accuracy of 83.4%, 85.01%, and 90.51% for the SAVEE, EMOVO, and EMO-DB datasets, respectively. We found that the proposed MRVMD method performed better than the state-of-the-art methods.

DA-CapsNet: A multi-branch capsule network based on adversarial domain adaption for cross-subject EEG emotion recognition

Due to inter-individual variances, cross-subject electroencephalogram (EEG)-based emotion recognition is a challenging task. In this paper, we construct a multi-branch Capsule network (named DA-CapsNet) based on domain adaptation to improve the performance of cross-subject EEG emotion recognition. To fully capture the various intensity characteristics of a single emotion, firstly, DA-CapsNet decomposes the source and the target domain EEG signals into four frequency bands and homomorphically groups the data in each band, and then extracts the differential entropy (DE) features for each group separately. Taking into account the spatial arrangement of the electrodes, the DE features are mapped into a two-dimensional matrix to form a homomorphic difference cube sequence (HDCS). Second, to enhance the feature information of the same emotion and accelerate the run efficiency of the network, a parallel structured multi-branch primary Capsual network (CapsNet) is constructed in this paper. The multi-branch primary CapsNet can effectively extract the aforementioned sequence discriminative features and fuse them as the input features of the capsule emotion classifier. Finally, to lessen inter-domain distribution discrepancies, we brought adversarial domain adaptation to improve the performance of cross-subject emotion recognition. Numerous tests are run on the three public datasets of EEG, and the results show that the proposed algorithm in this paper works well.

Assessing the diametrical model of schizotypal and autistic traits in emotion recognition and social functioning in a community sample.

Some research suggests that schizotypal and autistic traits can produce opposing effects on the mentalizing domain of social cognition. Although such findings support a diametrical model proposing that psychotic and autistic traits represent opposite extremes of the social brain continuum, results from recent studies have been more inconsistent, and the applicability of this model to other social cognition domains remains unclear. To test the diametrical model more broadly, this study examined the interactions between schizotypal and autistic traits on emotion recognition and social functioning. A total of 791 participants recruited from the general population self-reported schizotypal traits using the Schizotypal Personality Questionnaire-Brief Revised (SPQ-BR) and autistic traits using the Broad Autism Phenotype Questionnaire (BAPQ). Participants also completed the Emotion Recognition 40 task and the Specific Levels of Functioning (SLOF) scale. The SPQ subscales of interpersonal relationships and disorganized symptoms interacted significantly with social BAP on overall emotion recognition performance and the accuracy of identifying neutral faces. Supporting the diametrical model, elevated levels of both schizotypal and autistic traits contributed to higher emotion recognition accuracy compared to elevations on only one trait. For social functioning, however, the diametrical model was not supported. A main effect was found such that higher interpersonal relationship difficulties on SPQ predicted lower work skills on SLOF, and higher levels of both schizotypal and autistic traits combined to produce even lower social functioning. These findings suggest that the diametrical model may be more relevant to social cognition than to social functioning.