Dancer perceptions of the cognitive,social, emotional,and physical benefits of modern styles of partnered dancing

ObjectiveTo study dancers’ perceptions of the physical, cognitive, affective, and social benefits of partnered dancing.Method225 dancers (71% female) were recruited through a community ballroom dance center and completed an online survey designed to measure their perceptions of the physical, cognitive, affective, and social benefits of modern, partnered dance styles (swing, Lindy Hop, and ballroom dancing). Subgroups were formed for analyses. For one set of analyses, groups based on length of dance participation were formed: experienced (dancing for more than 2 years) or novice (dancing for less than a year) dancers. For another set of analyses, groups based on frequency of dance practice were formed: committed (dancing at least one or more times per week) or occasional (dancing two or fewer times per month).ResultsThe majority of participants reported perceived benefits in physical fitness, cognition, affect, and social functioning. Experienced dancers reported significantly greater self-perceived physical, social, and cognitive benefits than novice dancers. Committed dancers were more likely than occasional dancers to report improvements in physical fitness, U = 6942, z = 2.38, r = 0.16, p < 0.05. A Mann-Whitney test indicated that self-reported improvements in mood (i.e., feeling less depressed and more happy) were greater for women than for men, U = 3945, z = −3.07, r = 0.20, p < 0.001. Length and frequency of dance participation significantly predicted perceived physical benefits [Χ² (1,6) = 35.463, p <0.001, R² = 0.16] and social benefits [Χ² (1,6) = 15.776, p < 0.05, R² = 0.07], but not cognitive benefits.ConclusionsResults suggest that participation in partnered dance styles is associated with perceived improvements in physical fitness, cognitive functioning, social functioning, mood, and self-confidence, and that perceived benefits may increase as individuals dance more frequently and over longer periods of time.     

HOSTILITY AS A MODERATOR OF PHYSICAL REACTIVITY AND RECOVERY TO STRESS

This experiment was designed to assess differences in physiological reactivity and recovery to stress among low- and high-hostile men. Specifically, 25 low- and 25 high-hostile undergraduates were identified using the Cook-Medley Hostility Scale (Cook & Medley, 1954). To ensure homogeneity, all subjects were right-handed and had a general right hemibody preference, as indicated by a score of +7 or higher on the Coren, Porac, and Duncan Laterality Test (Corer, Porac, & Duncan, 1979). All subjects underwent a traditional cold-pressor stressor test. Physiological measures heart rate, systolic blood pressure, and diastolic blood pressure were recorded before the stressor (Prestress), after the stressor (Poststress), and then again 9 min later (Recovery). Increased physiological arousal between pre- and poststress measurements was used as an indicator of reactivity. Subsequent decreases in physiological arousal were used as recover measures. Given the current models of negative emotion and hostility, it was expected that high-hostiles, relative to low-hostiles, would evidence increased physio logical arousal and decreased recovery to stress. Interestingly, high-hostiles experienced significantly greater reactivity to stress in heart rate only, and no group differences were found with regard to recovery. Results are discussed in terms of previous research and current models of emotion.     

Smart environment architecture for emotion detection and regulation

This paper introduces an architecture as a proof-of-concept for emotion detection and regulation in smart health environments. The aim of the proposal is to detect the patient’s emotional state by analysing his/her physiological signals, facial expression and behaviour. Then, the system provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible. The current state-of-the-art in emotion regulation through music and colour/light is implemented with the final goal of enhancing the quality of life and care of the subject. The paper describes the three main parts of the architecture, namely “Emotion Detection”, “Emotion Regulation” and “Emotion Feedback Control”. “Emotion Detection” works with the data captured from the patient, whereas “Emotion Regulation” offers him/her different musical pieces and colour/light settings. “Emotion Feedback Control” performs as a feedback control loop to assess the effect of emotion regulation over emotion detection. We are currently testing the overall architecture and the intervention in real environments to achieve our final goal.     

Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals

The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task, by identifying the experience that a user has while interacting with an AAL technology under particular conditions. This may help designing better products and improve user engagement and adoption of AAL solutions. However, evaluating the UX of AAL technologies is a difficult task, due to the inherent limitations of their subjects and of the evaluation methods. In this study, we validated the feasibility of assessing the UX of older adults while they use a cognitive stimulation application using a neural network trained to recognize pleasant and unpleasant emotions from electroencephalography (EEG) signals by contrasting our results with those of additional self-report and qualitative analysis UX evaluations. Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%. As future work we will conduct additional UX evaluation studies using the three different methods, in order to appropriately validate these results.     

Startle modulation and explicit valence evaluations dissociate during backward fear conditioning

Blink startle magnitude is linearly modulated by affect such that, relative to neutral stimuli, startle magnitude is inhibited during pleasant stimuli and potentiated during unpleasant stimuli. Andreatta, Mühlberger, Yarali, Gerber, and Pauli (2010), however, report a dissociation between startle modulation and explicit valence evaluations during backward conditioning, a procedure in which the unconditional stimulus precedes the conditional stimulus (CS). Relative to controls, startles elicited during the CS were inhibited, suggesting that the CS had acquired positive valence, but participants still evaluated the CS as unpleasant after the experiment. In Experiment 1, we aimed to replicate this dissociation using a trial‐by‐trial measure of CS valence to measure startle modulation and CS valence simultaneously during forward and backward differential fear conditioning. In Experiment 2, we examined whether early and late portions of the CS could acquire differential valence by presenting startle probes at early and late probe positions during the CS. In both experiments, the dissociation between startle modulation and explicit valence evaluations in backward conditioning replicated, with CS+ evaluated as less pleasant than CS‐, but startles elicited during CS+ inhibited relative to CS‐. In Experiment 2, we provide preliminary evidence that this inhibition was present early, but not late, during the CS+. The results replicate the dissociation between implicit and explicit CS valence reported by Andreatta et al. (2010) using a trial‐by‐trial measure of valence. We also provide preliminary evidence that this dissociation may occur because the implicit and explicit measures are recorded at different times during the CS presentation.     

Effects of a synbiotic on symptoms,and daily functioning in attention deficit hyperactivity disorder – A double-blind randomized controlled trial

Some prebiotics and probiotics have been proposed to improve psychiatric symptoms in children with autism. However, few studies were placebo-controlled, and there is no study on persons with an attention deficit hyperactivity disorder (ADHD) diagnosis. Our aim was to study effects of Synbiotic 2000 on psychiatric symptoms and functioning in children and adults with ADHD without an autism diagnosis. Children and adults (n = 182) with an ADHD diagnosis completed the nine weeks randomized double-blind parallel placebo-controlled trial examining effects of Synbiotic 2000 on the primary endpoints ADHD symptoms, autism symptoms and daily functioning, and the secondary endpoint emotion regulation, measured using the questionnaires SNAP-IV, ASRS, WFIRS, SCQ, AQ and DERS-16. Levels at baseline of plasma C-reactive protein and soluble vascular cell adhesion molecule-1 (sVCAM-1), central to leukocyte-endothelial cell adhesion facilitating inflammatory responses in tissues, were measured using Meso Scale Discovery. Synbiotic 2000 and placebo improved ADHD symptoms equally well, and neither active treatment nor placebo had any statistically significant effect on functioning or sub-diagnostic autism symptoms. However, Synbiotic 2000, specifically, reduced sub-diagnostic autism symptoms in the domain restricted, repetitive and stereotyped behaviors in children, and improved emotion regulation in the domain of goal-directed behavior in adults. In children with elevated sVCAM-1 levels at baseline as well as in children without ADHD medication, Synbiotic 2000 reduced both the total score of autism symptoms, and the restricted, repetitive and stereotyped behaviors. In adults with elevated sVCAM-1 at baseline, Synbiotic 2000 significantly improved emotion regulation, both the total score and four of the five subdomains. To conclude, while no definite Synbiotic 2000-specific effect was detected, the analysis of those with elevated plasma sVCAM-1 levels proposed a reduction of autism symptoms in children and an improvement of emotion regulation in adults with ADHD.Trial registration number: ISRCTN57795429.     

The road not taken: Social vs. private comparisons in Asperger׳s syndrome and high functioning autism

Evaluation of the outcomes of our decisions may instigate comparisons of our actual outcome with those of others (social comparisons) or comparisons with alternative outcomes of choices not made (private comparisons). Previous research has suggested a deficit in attention to social information among individuals with autism spectrum disorders. As social comparison involves the processing of social information, here we investigated the orientation towards and sensitivity to social vs. private comparisons in individuals with autism spectrum disorders. We compared the sensitivity to social vs. private comparisons among individuals diagnosed with Asperger’s Syndrome (AS) or High Functioning Autism, using a task that entailed monetary rewards. Results showed that while individuals with AS generally demonstrate comparable sensitivity to absolute and relative rewards, they show less sensitivity to social comparison as compared to controls. Furthermore, they are characterized by a higher sensitivity to private rather than social comparison. These results suggest that low sensitivity to social comparisons is an important factor to consider in autism spectrum disorders.     

Emotion recognition from physiological signals

Emotion recognition is one of the great challenges in human–human and human–computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.