Public speaking is a vital skill for making good impressions, effectively exchanging ideas, and influencing others. Yet, public speaking anxiety (PSA) ranks as a top social phobia. Recent advancements in wearable devices and ubiquitous virtual reality …
In interpersonal spoken interactions, individuals tend to adapt to their conversation partner’s vocal characteristics to become similar, a phenomenon known as entrainment. A majority of the previous computational approaches are often knowledge driven and linear and fail to capture the inherent nonlinearity of entrainment. In this article, we present an unsupervised deep learning framework to derive a representation from speech features containing information relevant for vocal entrainment. We investigate both an encoding based approach and a more robust triplet network based approach within the proposed framework. We also propose a number of distance measures in the representation space and use them for quantification of entrainment. We first validate the proposed distances by using them to distinguish real conversations from fake ones. Then we also demonstrate their applications in relation to modeling several entrainment-relevant behaviors in observational psychotherapy, namely agreement, blame and emotional bond.
Drug addicts are characterized by difficulty neglecting monetary reward, but its underlying neural mechanisms remain unclear. The current study aimed to investigate the behavioral and electrophysiological signatures of abnormal attentional bias based on different amounts of reward in abstinent heroin addicts (AHAs). We used a modified attentional capture task while recording EEG in 18 AHAs and 18 age-, gander-, and education-matched healthy controls (HCs). We analyzed the attentional distribution of the relative positional changes in space of the target and reward-related stimulus. When targets integrated reward-related colors, participants were more responsive and deployed more attention to targets, especially those with high-value colors. When targets and reward-related distractors were spatially separated, high-value distractors captured the AHA’s attention and slowed their responses. Moreover, AHAs had weaker attentional control than HCs, exhibiting an inability to suppress the attentional bias driven by high-value stimuli. Overall, these results demonstrated that AHAs was hypersensitive to task-irrelevant and previous reward-related stimuli, possibly due to damage to brain reward circuits caused by chronic heroin abuse. Our work provides novel behavioral and neurophysiological evidence that are closely associated with the maintenance and relapse of addiction.
Emotion analysis of students plays an important role in teaching effect evaluation. To develop robust algorithms for emotion analysis of students, a database from real classrooms is required. However, most existing databases were collected from adults …
In this article, we address Emotion Recognition in Conversation (ERC) where conversational data are presented in a multimodal setting. Psychological evidence shows that self and inter-speaker influence are two central factors to emotion dynamics in con…
While existing prediction models built on popular deep architectures have shown promising results in facial depression recognition, they still lack sufficient discriminative power due to the issues of 1) limited amount of labeled depression data for de…
Personality not only plays essential roles in people’s real lives, but also becomes an important factor for various online services. Traditional approaches to personality assessment usually ask users to answer a long list of questions and are thus not practical in many online systems. It is more desirable to use some universally available data in the system to perform personality assessment. This article reports a controlled study to investigate common mouse operations as a potential new type of data source for online personality assessment. We establish an elaborate personality-mouse behavior dataset from 146 subjects and propose kinematic and adjustment features to characterize mouse motion patterns. Statistical approaches and machine learning algorithms are employed to examine the connections between personality preferences and mouse motion features via correlation analysis, discernibility analysis, and personality recognition experiments. The results reveal some interesting expressions of cognitive personality perspectives reflected in mouse motion patterns, such as fast starting acceleration and better controller. Performance evaluation shows it is possible to recognize different personality preferences using mouse motion features with accuracies ranging from 60.6 to 78.3 percent. Our findings suggest a potential to use mouse operational behaviors as a new data source for personality assessment in various information systems.
Recent psychological and neurological studies suggest that human motor preparation and execution are largely affected by the subjective emotional state. Thus, external emotion stimuli can be a potential tool to enhance the detectability of movement int…
Speech conveys not only the verbal communication, but also emotions, manifested as facial expressions of the speaker. In this article, we present deep learning frameworks that directly infer facial expressions from just speech signals. Specifically, th…
In this article, we explore whether and how linguistic and pragmatic context can change individual word valence and emotionality in two parts. In the first part, we investigate whether sentence contexts retrieved from a domain-specific corpus (soccer) …