Virtual reality stimulation and organizational neuroscience for the assessment of empathy.

This study aimed to evaluate the viability of a new procedure based on machine learning (ML), virtual reality (VR), and implicit measures to discriminate empathy. Specifically, eye-tracking and decision-making patterns were used to classify individuals according to their level in each of the empathy dimensions, while they were immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centered design approach. Interaction and gaze patterns were recorded for 82 participants, who were classified as having high or low empathy on each of the following empathy dimensions: perspective-taking, emotional understanding, empathetic stress, and empathetic joy. The dimensions were assessed using the Cognitive and Affective Empathy Test. An ML-based model that combined behavioral outputs and eye-gaze patterns was developed to predict the empathy dimension level of the participants (high or low). The analysis indicated that the different dimensions could be differentiated by eye-gaze patterns and behaviors during immersive VR. The eye-tracking measures contributed more significantly to this differentiation than did the behavioral metrics. In summary, this study illustrates the potential of a novel VR organizational environment coupled with ML to discriminate the empathy dimensions. However, the results should be interpreted with caution, as the small sample does not allow general conclusions to be drawn. Further studies with a larger sample are required to support the results obtained in this study.

Combining Virtual Reality and Machine Learning for Leadership Styles Recognition.

The aim of this study was to evaluate the viability of a new selection procedure based on machine learning (ML) and virtual reality (VR). Specifically, decision-making behaviours and eye-gaze patterns were used to classify individuals based on their leadership styles while immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centred design approach. Interaction and gaze patterns were recorded in 83 subjects, who were classified as having either high or low leadership style, which was assessed using the Multifactor leadership questionnaire. A ML model that combined behaviour outputs and eye-gaze patterns was developed to predict subjects' leadership styles (high vs low). The results indicated that the different styles could be differentiated by eye-gaze patterns and behaviours carried out during immersive VR. Eye-tracking measures contributed more significantly to this differentiation than behavioural metrics. Although the results should be taken with caution as the small sample does not allow generalization of the data, this study illustrates the potential for a future research roadmap that combines VR, implicit measures, and ML for personnel selection.

Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review.

The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.

The Virtual Cooking Task: A Preliminary Comparison Between Neuropsychological and Ecological Virtual Reality Tests to Assess Executive Functions Alterations in Patients Affected by Alcohol Use Disorder.

Alcohol use disorder (AUD) is a major global problem. Neuropsychological studies have shown that AUD causes deficits in executive functions (EFs), a set of higher order cognitive skills that govern individual behavior in everyday situations. Many standardized neuropsychological tests are used to evaluate EF. These are reliable and valid but have limitations in predicting real-life performance. To address this, we present a preliminary study to test the virtual cooking task (VCT) as an alternative to standardized neuropsychological tests. The VCT includes four subtasks developed to assess attentional, planning, and cognitive shifting abilities; it was tested in an immersive three-dimensional environment. To evaluate the VCT performance and standardized neuropsychological tests, data were gathered from a sample of healthy subjects (control group [CG]; n = 23) and AUD patients (n = 18). The standardized neuropsychological measures used consisted of questionnaires (Attentional Control Scale, Barratt Impulsiveness Scale, and Cognitive Flexibility Scale) and specific tests (Dot-probe task, Go/No-go test, Stroop test, the trail making test, and Tower of London test). The results showed significant higher correlations for AUD patients than for the CG for the VCT, questionnaires, and specific tests, mainly related to planning and cognitive shifting abilities. Furthermore, comparative analyses of the VCT performance showed that the AUD patients made more errors and had higher latency times than the CG. The present study provides initial evidence that a more ecologically valid assessment can be a useful tool to detect cognitive impairments in many neuropsychological and mental disorders, affecting daily activities.

Are 3D virtual environments better than 2D interfaces in serious games performance? An explorative study for the assessment of executive functions.

Executive functions refer to higher-order cognitive processes that supervise and guide goal-directed and adaptive behaviors in response to everyday situations. The traditional measures used to assess executive functions include paper-and-pencil tests and/or computerized tests that have been found to have a moderate level of ecological validity in predicting real-world performance. Serious games (SG) represent a novel methodological approach, allowing investigating subjects' performance in real-simulated situations. Serious games are computer games whose primary purposes include investigating human behaviors and changes. Furthermore, SG can also vary according to the technology used and the interaction. Indeed, a SG can be rendered via a nonimmersive screen-based (2D) or via an immersive virtual reality game (3D). Starting from these premises, we compared a narrative-contextualized SG in 2D and 3D, correlating them with traditional tests. Findings showed different condition correlations with the traditional tasks and the comparison between the two systems have revealed that 3D is able to generate lower reaction times, higher correct answers, and lower perseverative responses in attentional abilities, inhibition control, and cognitive shifting than 2D condition. The present study yielded evidence on the use of more ecological tools to identify the functional cognitive status in real-simulated contexts along with traditional evaluation.

Machine Learning and Virtual Reality on Body Movements' Behaviors to Classify Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.

Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality.

OBJECTIVE: Sensory processing is the ability to capture, elaborate, and integrate information through the five senses and is impaired in over 90% of children with autism spectrum disorder (ASD). The ASD population shows hyper-hypo sensitiveness to sensory stimuli that can generate alteration in information processing, affecting cognitive and social responses to daily life situations. Structured and semi-structured interviews are generally used for ASD assessment, and the evaluation relies on the examiner's subjectivity and expertise, which can lead to misleading outcomes. Recently, there has been a growing need for more objective, reliable, and valid diagnostic measures, such as biomarkers, to distinguish typical from atypical functioning and to reliably track the progression of the illness, helping to diagnose ASD. Implicit measures and ecological valid settings have been showing high accuracy on predicting outcomes and correctly classifying populations in categories. METHODS: Two experiments investigated whether sensory processing can discriminate between ASD and typical development (TD) populations using electrodermal activity (EDA) in two multimodal virtual environments (VE): forest VE and city VE. In the first experiment, 24 children with ASD diagnosis and 30 TDs participated in both virtual experiences, and changes in EDA have been recorded before and during the presentation of visual, auditive, and olfactive stimuli. In the second experiment, 40 children have been added to test the model of experiment 1. RESULTS: The first exploratory results on EDA comparison models showed that the integration of visual, auditive, and olfactive stimuli in the forest environment provided higher accuracy (90.3%) on sensory dysfunction discrimination than specific stimuli. In the second experiment, 92 subjects experienced the forest VE, and results on 72 subjects showed that stimuli integration achieved an accuracy of 83.33%. The final confirmatory test set (n = 20) achieved 85% accuracy, simulating a real application of the models. Further relevant result concerns the visual stimuli condition in the first experiment, which achieved 84.6% of accuracy in recognizing ASD sensory dysfunction. CONCLUSION: According to our studies' results, implicit measures, such as EDA, and ecological valid settings can represent valid quantitative methods, along with traditional assessment measures, to classify ASD population, enhancing knowledge on the development of relevant specific treatments.

A Virtual Versus an Augmented Reality Cooking Task Based-Tools: A Behavioral and Physiological Study on the Assessment of Executive Functions.

Virtual reality (VR) and augmented reality (AR) are two novel graphics immersive techniques (GIT) that, in the last decade, have been attracting the attention of many researchers, especially in psychological research. VR can provide 3D real-life synthetic environments in which controllers allow human interaction. AR overlays synthetic elements to the real world and the human gaze to target allow hand gesture to act with synthetic elements. Both techniques are providing more ecologically environments than traditional methods, and most of the previous researches, on one side, have more focused on the use of VR for treatment and assessment showing positive effectiveness results. On the other, AR has been proving for the treatment of specific disorders but there are no studies that investigated the feasibility and effectiveness of AR in the neuropsychological assessment. Starting from these premises, the present study aimed to compare the performance and sense of presence using both techniques during an ecological task, such as cooking. The study included 50 cognitively healthy subjects. The cooking task consisted of four levels that increased in difficulty. As the level increased, additional activities appeared. The order of presentation of each exposure condition (AR and VR) was counterbalanced for each participant. The VR-cooking task has been performed through "HTC/VIVE" and AR through "Microsoft HoloLens." Furthermore, the study recorded and compared the psychophysiological changes [heart rate and skin conductance response (SCR)] during the cooking task in both conditions. To measure the sense of presence occurring during the two exposure conditions, subjects completed the Slater-Usoh-Steed Questionnaire (SUSQ) and the ITC-Sense of Presence Inventory (ITC-SOPI) immediately after each condition. The behavioral results showed that times are always lower in VR than in AR, increasing constantly in accordance with the difficulty of the tasks. Regarding physiological responses, the findings showed that AR condition produced more individual excitement and activation than VR. Finally, VR was able to produce higher levels of sense of presence than AR condition. The overall results support that VR currently represents the GIT with greater usability and feasibility compared to AR, probably due to the differences in the human-computer interaction between the two techniques.

User-Centered Virtual Reality for Promoting Relaxation: An Innovative Approach.

Virtual reality has been used effectively to promote relaxation and reduce stress. It is possible to find two main approaches to achieve such aims across the literature. The first one is focused on generic environments filled with relaxing "narratives" to induce control over one's own body and physiological response, while the second one engages the user in virtual reality-mediated activities to empower his/her own abilities to regulate emotion. The scope of the present contribution is to extend the discourse on VR use to promote relaxation, by proposing a third approach. This would be based on VR with personalized content, based on user research to identify important life events. As a second step, distinctive features of such events may be rendered with symbols, activities or other virtual environments contents. According to literature, it is possible that such an approach would obtain more sophisticated and long-lasting relaxation in users. The present contribution explores this innovative theoretical proposal and its potential applications within future research and interventions.

EXPANSE: A novel narrative serious game for the behavioral assessment of cognitive abilities.

EFs are a set of processes that supports many cognitive domains as goal setting, monitoring, planning, and cognitive-behavioural flexible control. Currently, many standardized paper-and-pencil tests or scales are used to assess EFs. These tests are easy to administer, score, and interpret but present some limitations in terms of generalizability of behaviours in real life. More recently, Information and Communication Technology has provided a higher ecological validity in the EFs assessment. In order to increase the ecological validity, we have developed a serious game (SG), named EXPANSE, which aim was to compare the participants' game performance (latency times, and correct answers) with the results obtained in the traditional tasks and scales. 354 healthy subjects participated to the study and the findings showed significant correlations among standard tasks and the serious game. The exploratory nature of the present study, on one hand, highlighted that SG could be an additional behavioral tool to assess EFs and, on the other, we need further investigations, including clinical populations, for better defining the game sensitivity toward EF components. Finally, the results show that serious games are a promising technology for the evaluation of real cognitive behavior along with traditional evaluation.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising.

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.

A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents.

This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.

Analysis of distributed-collaborative activity during augmented reality exposure therapy for cockroach phobia.

Recent research presents Augmented Reality Exposure Therapy (ARET) for treatment of phobia of cockroaches as a potentially effective technique. However, to the authors' knowledge no studies have been published concerning the Human-Computer-Interaction issues of such a system. The aim of this paper is to report some preliminary data on how patients, therapists and an Augmented Reality system collaborate and interact during the therapeutic process. The results show that the therapeutic process is distributed between individuals (patient and therapist) and artifacts (e.g. AR cockroaches, a computer screen, a Head Mounted Display (HMD), a keyboard, a swatter and therapists' notes on paper). The results are discussed in terms of possible improvement of the ARET system.

CAD / CAM dental systems in implant dentistry: Update

CAD/CAM systems (computer-aided design / computer aided manufacturing) used for decades in restorative dentistry have expanded its application to implant dentistry. This study aimed to look through CAD/CAM systems used in implant dentistry, especially emphasizing implant abutments and surgical templates manufacturing. A search of articles published in English at Medline and Scopus databases at present was conducted, introducing “dental CAD/CAM”, “implants abutments” and “surgical guide CAD/CAM” as key words. These systems consist of three components: 1) data capture using optical systems or laser scanning, 2) CAD for the design of the restoration, and 3) CAM to produce the restoration through the information generated by computer. CAD/CAM abutments present the advantages of being specific to each patient and providing a better fit than the rest of abutments, in addition to being much more tough as they employ materials such as titanium, alumina and zirconium. In order to improve accuracy during implant placement we use stereolithography to manufacture CAD/CAM surgical templates. Using this method, minimally invasive surgery is performed without a flap, and the prosthesis is delivered, achieving immediate functional loading to the implants (AU)

CAD / CAM dental systems in implant dentistry: update.

CAD/CAM systems (computer-aided design / computer aided manufacturing) used for decades in restorative dentistry have expanded its application to implant dentistry. This study aimed to look through CAD/CAM systems used in implant dentistry, especially emphasizing implant abutments and surgical templates manufacturing. A search of articles published in English at Medline and Scopus databases at present was conducted, introducing "dental CAD/CAM" , "implants abutments" and "surgical guide CAD/CAM" as key words. These systems consist of three components: 1) data capture using optical systems or laser scanning, 2) CAD for the design of the restoration, and 3) CAM to produce the restoration through the information generated by computer. CAD/CAM abutments present the advantages of being specific to each patient and providing a better fit than the rest of abutments, in addition to being much more tough as they employ materials such as titanium, alumina and zirconium. In order to improve accuracy during implant placement we use stereolithography to manufacture CAD/CAM surgical templates. Using this method, minimally invasive surgery is performed without a flap, and the prosthesis is delivered, achieving immediate functional loading to the implants.

Mixed reality for neurosurgery: a novel prototype.

Microscopes and neuronavigators are often used in neurosurgical procedures. Our Mixed Reality prototype proposes to show neuronavigator imaging overlayed onto the surgical field of view. The proposal is to replace the traditional optical microscope with a digital one. The system is presented in this article. Firsts trials have been performed at the laboratory with satisfactory results.