Exploring Emotion Recognition for VR-EBT Using Deep Learning on a Multimodal Physiological Framework

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality scenes. Machine learning has been used in medical areas such as rare diseases, oncology, medical data classification and psychiatry. This research used a public dataset that contained physiological recordings and emotional responses. The dataset was used to train a deep neural network, and a convolutional neural network to predict an individual’s valence, arousal and dominance. The results presented indicate that the deep neural network had the highest overall mean bounded regression accuracy and the lowest computational time

Psychophysiology in games

Psychophysiology is the study of the relationship between psychology and its physiological manifestations. That relationship is of particular importance for both game design and ultimately gameplaying. Players’ psychophysiology offers a gateway towards a better understanding of playing behavior and experience. That knowledge can, in turn, be beneficial for the player as it allows designers to make better games for them; either explicitly by altering the game during play or implicitly during the game design process. This chapter argues for the importance of physiology for the investigation of player affect in games, reviews the current state of the art in sensor technology and outlines the key phases for the application of psychophysiology in games.The work is supported, in part, by the EU-funded FP7 ICT iLearnRWproject (project no: 318803).peer-reviewe

The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Is There a Higher-Order Mechanism that Explains Performance Across Prediction Tasks?

People constantly make predictions about what will happen in the near future. People anticipate how other people around them will act, what other people will say, and what actions will help them achieve the greatest rewards. Because all of these behaviors are typically called prediction, it is easy to make the assumption that performance across all of these types of tasks is driven by the same underlying mechanism. However, there has been little investigation into whether the mechanisms underlying prediction are the same across multiple task modalities. Therefore, in the current study, 226 participants completed four types of tasks that putatively involve prediction to determine whether there is a common factor that can account for performance on these tasks. Fluid and crystallized intelligence were also assessed to ensure that general intelligence did not drive correlations among the tasks. Preliminary evidence from a recent study suggested that people with Posttraumatic Stress Disorder (PTSD) have difficulty with predicting future activity; therefore, participants also completed a questionnaire screening for symptoms of PTSD. Performance across the four prediction tasks was not correlated, and PTSD severity was not significantly correlated with any of the tasks in the study. These results suggest that there is not an integrative prediction mechanism in the brain, but rather that there are multiple prediction systems operating in parallel within the brain. In addition, these results suggest that PTSD may only be associated with a subset, if any, of prediction tasks. Future researchers studying prediction must be careful to investigate performance on various prediction tasks separately, rather than assuming that prediction performance is stable across tasks

Taxometric Investigation of PTSD: Data From Two Nationally Representative Samples

Current psychiatric nosology depicts posttraumatic stress disorder (PTSD) as a discrete diagnostic category. However, only one study has examined the latent structure of PTSD, and this study suggested that PTSD may be more accurately conceptualized as an extreme reaction to traumatic life events rather than a discrete clinical syndrome. To build on the existing literature base, the present research examined the latent structure of posttraumatic stress reactions by applying three taxometric procedures (MAXEIG, MAMBAC, and L-Mode) to data collected from large nationally representative samples of women (ns = 2684 and 3033) and adolescents (n = 3775). Results consistently provided evidence for a dimensional PTSD solution across samples and statistical procedures. These findings have important implications for the theory, assessment, and investigation of posttraumatic stress reactions