Total VREcall: using biosignals to recognize emotional autobiographical memory in virtual reality

Our memories and past experiences contribute to guiding our perception and action of future affective experiences. Virtual Reality (VR) experiences are more vividly memorized and recalled than non-VR ones, but there is little research on how to detect this recall in VR. We investigate the feasibility of recognizing autobiographical memory (AM) recall in VR using physiological cues: skin conductance, heart-rate variability, eye gaze, and pupillary response. We devised a methodology replicating an existing AM Test in VR. We conducted a user study with 20 participants recalling AM using three valence categories cue words: positive, negative, and neutral. We found a significant effect of AM recalls on EDA peak, and eye blink rate, with a generalized recognition accuracy of 77.1% and person dependent accuracy of up to 95.1%. This shows a promising approach for detecting AM recall in VR and we discuss the implications for VR experience design

Multimodal assessment of emotional responses by physiological monitoring: novel auditory and visual elicitation strategies in traditional and virtual reality environments

This doctoral thesis explores novel strategies to quantify emotions and listening effort through monitoring of physiological signals. Emotions are a complex aspect of the human experience, playing a crucial role in our survival and adaptation to the environment. The study of emotions fosters important applications, such as Human-Computer and Human-Robot interaction or clinical assessment and treatment of mental health conditions such as depression, anxiety, stress, chronic anger, and mood disorders. Listening effort is also an important area of study, as it provides insight into the listeners’ challenges that are usually not identified by traditional audiometric measures. The research is divided into three lines of work, each with a unique emphasis on the methods of emotion elicitation and the stimuli that are most effective in producing emotional responses, with a specific focus on auditory stimuli. The research fostered the creation of three experimental protocols, as well as the use of an available online protocol for studying emotional responses including monitoring of both peripheral and central physiological signals, such as skin conductance, respiration, pupil dilation, electrocardiogram, blood volume pulse, and electroencephalography. An emotional protocol was created for the study of listening effort using a speech-in-noise test designed to be short and not induce fatigue. The results revealed that the listening effort is a complex problem that cannot be studied with a univariate approach, thus necessitating the use of multiple physiological markers to study different physiological dimensions. Specifically, the findings demonstrate a strong association between the level of auditory exertion, the amount of attention and involvement directed towards stimuli that are readily comprehensible compared to those that demand greater exertion. Continuing with the auditory domain, peripheral physiological signals were studied in order to discriminate four emotions elicited in a subject who listened to music for 21 days, using a previously designed and publicly available protocol. Surprisingly, the processed physiological signals were able to clearly separate the four emotions at the physiological level, demonstrating that music, which is not typically studied extensively in the literature, can be an effective stimulus for eliciting emotions. Following these results, a flat-screen protocol was created to compare physiological responses to purely visual, purely auditory, and combined audiovisual emotional stimuli. The results show that auditory stimuli are more effective in separating emotions at the physiological level. The subjects were found to be much more attentive during the audio-only phase. In order to overcome the limitations of emotional protocols carried out in a laboratory environment, which may elicit fewer emotions due to being an unnatural setting for the subjects under study, a final emotional elicitation protocol was created using virtual reality. Scenes similar to reality were created to elicit four distinct emotions. At the physiological level, it was noted that this environment is more effective in eliciting emotions. To our knowledge, this is the first protocol specifically designed for virtual reality that elicits diverse emotions. Furthermore, even in terms of classification, the use of virtual reality has been shown to be superior to traditional flat-screen protocols, opening the doors to virtual reality for the study of conditions related to emotional control

The future of China’s U.S. listed firms: legal and political perspectives on possible decoupling

There is a long history of Chinese firms raising capital on leading U.S. exchanges and these shares are estimated at $1 trillion value. Yet these investments are now on shaky ground on both sides of the U.S.-China faultline. Chinese firms have benefited from U.S. capital without complying with investor protections, particularly audit inspections, owing to differences in approaches to company data and opaque structures that have misled some investors. As a result, U.S. listed Chinese companies also face the looming threat of delistings under the Holding Foreign Companies Accountable Act (HFCAA) and China is unlikely to come to the rescue of all. Chinese listings of nonstate firms had depended on a largely laissez-faire approach by China to the expansion of this sector through foreign listings but heightened levels of state involvement in the affairs of nonstate companies are now bringing strain, as Communist Party policies have changed dramatically in recent years. The HFCAA was the result of a geopolitical standoff following prolonged disputes over access to financial audits of U.S. listed Chinese companies, which had been resisted by Chinese agencies for data security reasons and those data concerns have not gone away. These concerns, together with some other Chinese state strategies have partly been allayed by an agreement as to inspections. Uncertainties remain and there may yet be the potential for future delistings, presenting risks for U.S. investors, as well as slowing the stream of U.S. listings by Chinese companies. It will not yet amount to a decoupling, but investors should be wary

Deception Detection: Using Eye-Tracking Technology to Measure Faking in a Simulated Applicant Setting

The impact of faking on selection assessments and the need for methods to detect such behavior has drawn increased attention of researchers in the selection field over the last quarter century. The overarching purpose of this study was to assess the validity of utilizing eye-tracking technology in the detection of applicant faking on personality measures. Specifically, this study examined the physiological cues of response latency, eye fixation, and pupil dilation and their association with deception in the context of personality assessment in a job seeking scenario. The results indicated that individuals engaged in faking behavior had significantly more eye fixations and recorded significantly higher scores on the paper and pencil measure of cognitive load. In addition, results suggest that the experimental conditions likely accounted for the alterations in cognitive load regardless of the level of social desirability of items

The psychophysiology primer : A guide to methods and a broad review with a focus on human-computer interaction

Publisher Copyright: © 2016 B. Cowley, M. Filetti, K. Lukander.Peer reviewe

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task