IEEE Transactions on Affective Computing

Multi-party conversation modeling plays a vital role in emotion recognition in conversation (ERC). Aside from the intra- and inter-speaker dependencies between different speakers, the difficulty also lies in the fact that each conversation may contain ...

We report on a naturalistic study investigating the effects of routine driving on cardiovascular activation. We recruited 21 healthy young adults from a broad geographic area in the Southwestern United States. Using the participants’ own ...

Psychology-oriented research extensively studied how affect influences human decision making. Particularly, the cognitive tuning assumption suggests that mood can serve to regulate between shallow and deliberative decisions – a more negative mood ...

Soft skills, such as personality characteristics, communication skills and leadership, affect personal career performance greatly. Therefore, predicting the soft skills of interviewees can provide interviewers with a strong reference for the decision of ...

The proliferation of COVID-19 fake news on social media poses a severe threat to the health information ecosystem. We show that affective computing can make significant contributions to combat this infodemic. Given that fake news is often presented with ...

Automatic depression diagnosis is a challenging problem, that requires integrating spatial-temporal information and extracting features from audio-visual signals. In terms of privacy protection, the development trend of recognition algorithms based on ...

Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on deep ...

Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood for applications in education, healthcare, and entertainment, among others. Deep convolutional neural networks show promising results in ...

The use of multiple raters to label datasets is an established practice in affective computing. The principal goal is to reduce unwanted subjective bias in the labelling process. Unfortunately, this leads to the key problem of identifying a ground truth ...

Individual differences in preferences allow for integrative (win–win) solutions in negotiations. However, reaching an integrative solution is difficult as each party's preferences and limits are private and must be inferred. We hypothesized ...

Clinical rating scales can be used to assess the severity of bipolar disorder; however, their use involves clinician–patient interactions, which is labor-intensive. Therefore, this study proposes a digital-phenotyping-based system that provides ...

Body motion is an important channel for human communication and plays a crucial role in automatic emotion recognition. This work proposes a multiscale spatio-temporal network, which captures the coarse-grained and fine-grained affective information ...

Exploring effective approaches to enhance face-to-face interactions and interpersonal relationships is an important topic in the applications of affective computing. According to the co-actualization model, we propose a face-to-face co-participation ...

An affective brain-computer interface (aBCI) can detect affective states based on brain signals and might assist people in improving their emotion regulation abilities. However, individual differences in emotional brain patterns make cross-subject emotion ...

Social signal recognition techniques based on nonverbal behavioral sensing allow conversational robots to understand the user’s social signals, thereby enabling them to adopt interaction strategies based on internal states inferred from the social ...

Workers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, ...

Although facial expression recognition based on deep learning has become a major trend, existing methods have been found to prefer learning spurious statistical correlations and non-robust features during training. This degenerates the model's ...

Currently, much progress has been achieved on adult facial expressions recognition. Few attentions have been paid to child facial expression analysis. A lack of publicly available large-scale child facial expression databases hinders the development of ...

Emotion causes constitute a pivotal component in the comprehension of emotional conversations. Recently, a new task named Causal Emotion Entailment (CEE) has been proposed to identify the causal utterances for the target emotional utterance in a ...

The view of embodied emotion believes that emotions are the emotions of the body. While emotion-specific patterns of self-reported bodily sensation have been previously reported, the physiological bodily representation across emotions remains to be ...

The ability to perceive emotions is an important criterion for judging whether a machine is intelligent. To this end, a large number of emotion recognition algorithms have been developed especially for visual information such as video. Most previous ...

In this paper, we describe a general-purpose dialogue management framework used to design SOPHIE (Standardized Online Patient for Healthcare Interaction Education). SOPHIE simulates a virtual standardized cancer patient that allows physicians to practice ...

Recent advances in AI-based learning models have significantly increased the accuracy of Automatic Personality Recognition (APR). However, these methods either require training data from the same subject or the meta-information from the training set to ...

Affective computing is an important subfield of artificial intelligence, and with the rapid development of brain-computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to ...

Deep learning based video depression analysis has been recently an interesting and challenging topic. Most of existing works focus on learning single-scale facial dynamics of participants for depression detection. Besides, they usually adopt expensive ...

Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states is not feasible ...

Research on social psychology has revealed the existence of an affective mechanism in a human group, which is the group members spread their emotions to one another, the emotions of the group members form the group emotion, and the group emotion as a ...

The fight or flight phenomena is of evolutionary origin and responsible for the type of defensive behaviours enacted, when in the face of threat. This review attempts to draw the link between fear and aggression as motivational levers for fight or flight ...

As social robots and other intelligent machines enter the home, artificial emotional intelligence (AEI) is taking center stage to address users’ desire for deeper, more meaningful human-machine interaction. To accomplish such efficacious ...

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be ...