Emotional Prosody Recognition Enhances and Progressively Complexifies From Childhood to Adolescence

Background: Emotional prosody is the result of the dynamic variation of acoustical non-verbal aspects of language that allow people to convey and recognize emotions. Understanding how this recognition develops during childhood to adolescence is the goal of the present paper. We also aim to test the maturation of the ability to perceive mixed emotions in voice. Methods: We tested 133 children and adolescents, aged between 6 and 17 years old, exposed to 4 kinds of emotional (anger, fear, happiness, and sadness) and neutral linguistic meaningless stimuli. Participants were asked to judge the type and degree of perceived emotion on continuous scales. Results: By means of a general linear mixed model analysis, as predicted, a significant interaction between age and emotion was found. The ability to recognize emotions significantly increased with age for all emotional and neutral vocalizations. Girls recognized anger better than boys, who instead confused fear with neutral prosody more than girls did. Across all ages, only marginally significant differences were found between anger, happiness, and neutral versus sadness, which was more difficult to recognize. Finally, as age increased, participants were significantly more likely to attribute mixed emotions to emotional prosody, showing the progressive complexification of the emotional content representation that young adults perceived in emotional prosody. Conclusions: The ability to identify basic emotions from linguistically meaningless stimuli develops from childhood to adolescence. Interestingly, this maturation was not only evidenced in the accuracy of emotion detection, but also in a complexification of emotion attribution in prosody.

Risk-Taking Behavior Under the Effect of Emotional Stimuli Among Children and Adults

Risk-taking has an important role in human’s life, either positive or negative. Thus, finding a method to control or drive this in a particular way could affect individuals and communities’ health by discouraging negative risks such as reckless driving or encouraging positive risks. Emotion induction is one of the methods that can enhance or reinforce risk-taking according to the perceived emotion. Among the studies which had taken, most of them focus on adolescents’ which is known as the peaked age of risk-taking behavior, while from a developmental learning point of view if there is a way to control or educate people’s behavior childhood could be the best time. Thus, this study along with the introduction of a new risk-taking task, aims to investigate two less studied groups (children and adults) risk-taking behavior, and also their behavioral response after they influence by positive or negative emotional pictures, to test whether these affect their risk-taking or not. 21 children and 20 adults participate in this experiment. Their risk-taking behavior is obtained using a new version of game of dice task combined with emotional stimuli. Results show that children have higher tendency to choose riskier options while they affected by positive emotion while adults are more risk-averse after primed by negative emotion. These findings could be helpful for policy makers and tutoring planners to control risk-taking behavior over different ages using priming effect of positive and negative emotions.

Facial movements have over twenty dimensions of perceived meaning that are only partially captured with traditional methods

Central to science and technology are questions about how to measure facial expression. The current gold standard is the facial action coding system (FACS), which is often assumed to account for all facial muscle movements relevant to perceived emotion. However, the mapping from FACS codes to perceived emotion is not well understood. Six prototypical configurations of facial action units (AU) are sometimes assumed to account for perceived emotion, but this hypothesis remains largely untested. Here, using statistical modeling, we examine how FACS codes actually correspond to perceived emotions in a wide range of naturalistic expressions. Each of 1456 facial expressions was independently FACS coded by two experts (r = .84, κ = .84). Naive observers reported the emotions they perceived in each expression in many different ways, including emotions (N = 666); valence, arousal and appraisal dimensions (N =1116); authenticity (N = 121), and free response (N = 193). We find that facial expressions are much richer in meaning than typically assumed: At least 20 patterns of facial muscle movements captured by FACS have distinct perceived emotional meanings. Surprisingly, however, FACS codes do not offer a complete description of real-world facial expressions, capturing no more than half of the reliable variance in perceived emotion. Our findings suggest that the perceived emotional meanings of facial expressions are most accurately and efficiently represented using a wide range of carefully selected emotion concepts, such as the Cowen & Keltner (2019) taxonomy of 28 emotions. Further work is needed to characterize the anatomical bases of these facial expressions.

Emotion Transfer for 3D Hand and Full Body Motion Using StarGAN

In this paper, we propose a new data-driven framework for 3D hand and full-body motion emotion transfer. Specifically, we formulate the motion synthesis task as an image-to-image translation problem. By presenting a motion sequence as an image representation, the emotion can be transferred by our framework using StarGAN. To evaluate our proposed method’s effectiveness, we first conducted a user study to validate the perceived emotion from the captured and synthesized hand motions. We further evaluate the synthesized hand and full body motions qualitatively and quantitatively. Experimental results show that our synthesized motions are comparable to the captured motions and those created by an existing method in terms of naturalness and visual quality.

Research on the tourism destination image and sustainable development of off-season ecotourism based on tourists’ perception -- A case study of Daoxiang Lake in Beijing

In this paper, we study the question of relationship and influence between tourists’ perception and the image of off-season ecotourism destination and sustainable development of tourism, which is based on the web text. Firstly, we analyze the tendency of high-frequency words as tourism perception and customer attitude using the data of network text which is from review websites. Secondly, we study the image perception differences of ecotourism destinations in low and peak seasons through text replacement, perceived category comparison, perceived emotion comparison, and social network and network semantics comparison. Finally, we provide relevant suggestions and opinions on the sustainable development of off-season ecotourism, which is from the aspects of tourism attraction management, tourism publicity and ecological environment protection.

Speech emotion recognition based on listener-dependent emotion perception models

This paper presents a novel speech emotion recognition scheme that leverages the individuality of emotion perception. Most conventional methods simply poll multiple listeners and directly model the majority decision as the perceived emotion. However, emotion perception varies with the listener, which forces the conventional methods with their single models to create complex mixtures of emotion perception criteria. In order to mitigate this problem, we propose a majority-voted emotion recognition framework that constructs listener-dependent (LD) emotion recognition models. The LD model can estimate not only listener-wise perceived emotion, but also majority decision by averaging the outputs of the multiple LD models. Three LD models, fine-tuning, auxiliary input, and sub-layer weighting, are introduced, all of which are inspired by successful domain-adaptation frameworks in various speech processing tasks. Experiments on two emotional speech datasets demonstrate that the proposed approach outperforms the conventional emotion recognition frameworks in not only majority-voted but also listener-wise perceived emotion recognition.

Do nonlinear vocal phenomena signal negative valence or high emotion intensity?

Nonlinear vocal phenomena (NLPs) are commonly reported in animal calls and, increasingly, in human vocalizations. These perceptually harsh and chaotic voice features function to attract attention and convey urgency, but they may also signal aversive states. To test whether NLPs enhance the perception of negative affect or only signal high arousal, we added subharmonics, sidebands or deterministic chaos to 48 synthetic human nonverbal vocalizations of ambiguous valence: gasps of fright/surprise, moans of pain/pleasure, roars of frustration/achievement and screams of fear/delight. In playback experiments ( N = 900 listeners), we compared their perceived valence and emotion intensity in positive or negative contexts or in the absence of any contextual cues. Primarily, NLPs increased the perceived aversiveness of vocalizations regardless of context. To a smaller extent, they also increased the perceived emotion intensity, particularly when the context was negative or absent. However, NLPs also enhanced the perceived intensity of roars of achievement, indicating that their effects can generalize to positive emotions. In sum, a harsh voice with NLPs strongly tips the balance towards negative emotions when a vocalization is ambiguous, but with sufficiently informative contextual cues, NLPs may be re-evaluated as expressions of intense positive affect, underlining the importance of context in nonverbal communication.

Multilevel Associations of Daily Skill Use and Effectiveness With Anxiety, Stress, and Depression in a Transdiagnostic Sample Undergoing Dialectical Behavior Therapy Skills Training

Dialectical Behavior Therapy (DBT) is relatively effective at treating disorders of emotion dysregulation. However, given researchers’ focus on group-level differences within disorder-specific treatments, it is unclear which transdiagnostic mechanisms influence these effects. Participants (n=19, Mage=31.8, 68% female, 95% Caucasian) with elevated emotion dysregulation completed daily reports of DBT skill use, perceived effectiveness, anxiety, stress, and depression during a DBT skills training group (1,344 observations). We tested whether within-person skill use was associated with same-day negative affect, predicted next-day changes in negative affect, and was moderated by perceived emotion regulation effectiveness. Participants used more within-person skills on days of greater stress and anxiety, which predicted next-day decreases in stress and anxiety. On days of high, but not low, perceived effectiveness, people used more skills in response to more intense negative affect. The use, and perceived effectiveness, of more skills may be mechanisms by which DBT skills groups promote improved emotional functioning.