Novel supports to the assessment of cognitive functions through the combined use of technologies and subjective and objective measurements

Tesis por compendio[ES] Las funciones cognitivas incluyen todos los procesos a través de los cuales un individuo percibe, registra, mantiene, manipula, usa y expresa información que está involucrada en cualquier actividad cotidiana. Las principales herramientas estandarizadas se pueden dividir en tres grupos principales: escalas cortas de pruebas de seguimiento cognitivo: cuestionarios, baterías neuropsicológicas generales y pruebas específicas. Estas herramientas están bien validadas y son confiables, pero, en la última década, varias investigaciones han demostrado que algunos pacientes pueden realizar bien estas pruebas neuropsicológicas, incluso cuando tienen dificultades significativas para adaptar sus comportamientos a las actividades de la vida diaria. De acuerdo con esto, más recientemente, un nuevo enfoque ha aumentado sustancialmente, lo que podría proporcionar una mayor validez ecológica en la evaluación de las capacidades cognitivas funcionales que el enfoque estandarizado: el uso de sistemas tecnológicos avanzados para la evaluación neuropsicológica (STAEN). STAEN se refiere a un conjunto de dispositivos y aplicaciones de software tales como pruebas computarizadas, juegos divertidos e interactivos de fantasía (JS) y / o sistemas de realidad virtual simulada (RV) y / o aumentada (RA) que van más allá de las pruebas de evaluación tradicionales y que Brindar la posibilidad de entregar estímulos controlados y dinámicos, en entornos ecológicamente válidos y seguros. Partiendo de estas premisas, el objetivo principal de la tesis era diseñar, desarrollar y validar un SG 2D no inmersivo versus un JS 3D inmersivo y una actividad de la vida diaria en un entorno 3D RV inmersivo versus un RA para la evaluación de funciones cognitivas, comparando la eficacia y efectividad de ellos. El primer estudio 2D incluyó 354 sujetos sanos y se encontraron correlaciones entre el juego y los métodos tradicionales, lo que sugiere que el juego podría ser una herramienta válida para evaluar las funciones cognitivas en adultos. El segundo estudio, comparó la versión 2D versus una versión 3D STAEN, involucró a 94 sujetos sanos y mostró que la versión 3D fue capaz de generar tiempos más bajos y respuestas correctas más altas que la 2D, lo que sugiere evidencia inicial de la eficacia de un sistema más inmersivo en comparación con un sistema no-inmersivo. Aunque este resultado destaca una posible limitación en el uso de diferentes sistemas tecnológicos debido a las diferencias en los dos métodos de interacción (el sistema 2D aplicó el mouse y el teclado; los controladores de dos manos virtuales 3D) y el registro de datos de latencia de hardware y software. Con respecto a la variabilidad individual en edad, género y educación, los hallazgos mostraron consistencia con la literatura de referencia. Específicamente, los más jóvenes mostraron un mayor rendimiento que los mayores; niveles educativos más altos reflejados en una mejor puntuación y sobre género, los resultados mostraron un panorama más compuesto. Además, para mejorar la validez ecológica de la evaluación, el último estudio de esta tesis comparó el rendimiento conductual y las respuestas fisiológicas, durante una tarea de cocina ecológica, entre un sistema virtual y un sistema aumentado en 50 sujetos sanos. La tarea de cocinar consistió en 4 niveles que aumentaron en dificultad. A medida que el nivel aumentó, aparecieron actividades adicionales. Los resultados de comportamiento mostraron que los tiempos son siempre más bajos en realidad virtual que en RA, aumentando constantemente de acuerdo con la dificultad de las tareas. Con respecto a las respuestas fisiológicas, los hallazgos mostraron que la condición RA produjo más excitación y activación individual que la realidad virtual. Para concluir, STAEN está demostrando ser herramientas confiables y efectivas para la evaluación de las funciones cognitivas en adultos, proporcionando más validez ec[CA] Les funcions cognitives inclouen tots els processos a través dels quals un individu percep, registra, manté, manipula, usa i expressa informació que està involucrada en qualsevol activitat quotidiana. Les principals ferramentes estandarditzades es poden dividir en tres grups principals: escales curtes de proves de seguiment cognitiu: qüestionaris, bateries neuropsicológiques generals i proves específiques. Estes ferramentes estan ben validades i són confiables, però, en l'última dècada, diverses investigacions han demostrat que alguns pacients poden realitzar bé estes proves neuropsicológiques, inclús quan tenen dificultats significatives per a adaptar els seus comportaments a les activitats de la vida diària. D'acord amb açò, més recentment, un nou enfocament ha augmentat substancialment, la qual cosa podria proporcionar una major validesa ecològica en l'avaluació de les capacitats cognitives funcionals que l'enfocament estandarditzat: l'ús de sistemes tecnològics avançats per a l'avaluació neuropsicológica (STAEN). STAEN es referix a un conjunt de dispositius i aplicacions de software com ara proves computaritzades, jocs divertits i interactius de fantasia (JS) i / o sistemes de realitat virtual simulada (RV) i / o augmentada (RA) que van més enllà de les proves d'avaluació tradicionals i que brinden la possibilitat de presentar estímuls controlats i dinàmics, en entorns ecològicament vàlids i segurs. Partint d'estes premisses, l'objectiu principal de la tesi era dissenyar, desenrotllar i validar un SG 2D no inmersiu versus un JS 3D inmersiu i una activitat de la vida diària en un entorn 3D RV inmersiu versus un RA per a l'avaluació de funcions cognitives, comparant l'eficàcia i efectivitat d'ells. El primer estudi 2D va incloure 354 subjectes sans i es van trobar correlacions entre el joc i els mètodes tradicionals, la qual cosa suggerix que el joc podria ser una ferramenta vàlida per a avaluar les funcions cognitives en adults. El segon estudi, va comparar la versió 2D versus una versió 3D STAEN, va involucrar a 94 subjectes sans i va mostrar que la versió 3D va ser capaç de generar temps més baixos i respostes correctes més altes que la 2D, la qual cosa suggerix evidència inicial de l'eficàcia d'un sistema més inmersiu en comparació amb un sistema no-inmersiu. Encara que este resultat destaca una possible limitació en l'ús de diferents sistemes tecnològics a causa de les diferències en els dos mètodes d'interacció (el sistema 2D va aplicar el ratolí i el teclat; els controladors de dos mans virtuals 3D) i el registre de dades de latència de hardware i software. Respecte a la variabilitat individual en edat, gènere i educació, les troballes van mostrar consistència amb la literatura de referència. Específicament, els més jóvens van mostrar un major rendiment que els majors; nivells educatius més alts reflectits en una millor puntuació i sobre gènere, els resultats van mostrar un panorama més compost. A més, per a millorar la validesa ecològica de l'avaluació, l'últim estudi d'esta tesi va comparar el rendiment conductual i les respostes fisiològiques, durant una tasca de cuina ecològica, entre un sistema virtual i un sistema augmentat en 50 subjectes sans. La tasca de cuinar va consistir en 4 nivells que van augmentar en dificultat. A mesura que el nivell va augmentar, van aparéixer activitats addicionals. Els resultats de comportament van mostrar que els temps són sempre més baixos en realitat virtual que en RA, augmentant constantment d'acord amb la dificultat de les tasques. Respecte a les respostes fisiològiques, les troballes van mostrar que la condició RA va produir més excitació i activació individual que la realitat virtual. Per a concloure, STAEN està demostrant ser ferramentes confiables i efectives per a l'avaluació de les funcions cognitives en adults, proporcionant més validesa ecològica i objectivitat que els mètodes tradicio[EN] Cognitive functions include all the processes through which an individual perceives, records, maintains, manipulates, uses and expresses information that are involved in any everyday activity. The main standardized tools can be divided in three main groups: short scales of cognitive tracking tests - questionnaires, general neuropsychological batteries, and specific tests. These tools are well-validated and reliable but, in the last decade, several research have shown that some patients can perform these neuropsychological tests well, even when they have significant difficulties in adapting their behaviours to daily life activities. According to this, more recently, a new approach has substantially increased, potentially providing a higher ecological validity in functional cognitive abilities assessment than standardized approach: the use of advanced technological systems for neuropsychological assessment (ATSNA). ATSNA refer to a set of devices and software applications such as computerized tests, fun and interactive fantasy serious games (SG), and/or simulated virtual (VR) and/or augmented (AR) reality systems that go beyond traditional assessment tests and that supply the possibility to deliver controlled and dynamic stimuli, in ecologically valid, and secure environments. Starting from these premises, the main objective of the thesis was to design, develop, and validate a non-immersive 2D SG versus an immersive 3D SG and a daily life activity in an immersive 3D VR environment versus an AR for the assessment of cognitive functions, comparing the efficacy and effectiveness of them. The first 2D study involved 354 healthy subjects and correlations were found between the game and traditional methods, suggesting that the game could be a valid tool for assessing cognitive functions in adults. The second study, compared 2D versus a 3D ATSNA version, it involved 94 healthy subjects and showed that 3D version was able to generate lower times and higher correct answers that the 2D, suggesting initial evidence of efficacy of a more immersive system compared to a non-immersive system. Although this result highlights a potential limitation on using different technological systems due to the differences on the two interaction methods (the 2D system applied mouse and keyboard; the 3D two virtual hands' controllers) and hardware and software latency data recording. Regarding individual variability on age, gender, and education, the findings showed consistency with the reference literature. Specifically, younger showed higher performance that older; higher educational levels reflected on a better score and about gender, results showed a more composite panorama. Furthermore, to enhance the ecological validity of assessment, the last study of this thesis compared the behavioural performance and physiological responses, during an ecological cooking task, between a virtual and an augmented system on 50 healthy subjects. The cooking task consisted of 4 levels that increased in difficulty. As the level increased, additional activities appeared. The behavioural 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. To conclude, ATSNA are proving to be reliable and effective tools for the assessment of cognitive functions in adults, providing more ecological validity and objectivity than traditional methods of assessment.Chicchi Giglioli, IAM. (2020). Novel supports to the assessment of cognitive functions through the combined use of technologies and subjective and objective measurements [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/139075TESISCompendi

Virtual Reality as an Emerging Methodology for Leadership Assessment and Training

[EN] In developed countries, companies are now substantially reliant on the skills and abilities of their leaders to tackle a variety of complex issues. There is a growing consensus that leadership development training and assessment methods should adopt more holistic methodologies, including those associated with the emotional and neuroendocrine aspects of learning. Recent research into the assessment of leadership competencies has proposed the use of objective methods and measurements based on neuroscience. One of the challenges to be faced in the development of a performance-based methodology to measure leadership skills is how to generate real-life situations with triggers that allow us to study management competencies under controlled laboratory conditions. A way to address this question is to take advantage of virtual environments to recreate real-life situations that might arise in performance-based assessments. We propose virtual reality (VR) as a very promising tool to observe various leadership related behavioral patterns during dynamic, complex and realistic situations. By seamlessly embedding assessment methods into virtual learning environments, VR can provide objective assessment methods with high ecological validity. VR also holds unlimited opportunities for leadership training providing subjects with intelligent tutoring systems that adapts situations in real time according to the observed behaviors.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded project "Advanced Therapeutic Tools for Mental Health" (DPI2016-77396-R).Alcañiz Raya, ML.; Parra Vargas, E.; Chicchi-Giglioli, IA. (2018). Virtual Reality as an Emerging Methodology for Leadership Assessment and Training. Frontiers in Psychology. 9. https://doi.org/10.3389/fpsyg.2018.01658S

Why do we take risks? Perception of the situation and risk proneness predict domain-specific risk taking

[EN] Risk taking (RT) is a component of the decision-making process in situations that involve uncertainty and in which the probability of each outcome - rewards and/or negative consequences - is already known. The influence of cognitive and emotional processes in decision making may affect how risky situations are addressed. First, inaccurate assessments of situations may constitute a perceptual bias in decision making, which might influence RT. Second, there seems to be consensus that a proneness bias exists, known as risk proneness, which can be defined as the propensity to be attracted to potentially risky activities. In the present study, we take the approach that risk perception and risk proneness affect RT behaviours. The study hypothesises that locus of control, emotion regulation, and executive control act as perceptual biases in RT, and that personality, sensation seeking, and impulsivity traits act as proneness biases in RT. The results suggest that locus of control, emotion regulation and executive control influence certain domains of RT, while personality influences in all domains except the recreational, and sensation seeking and impulsivity are involved in all domains of RT. The results of the study constitute a foundation upon which to build in this research area and can contribute to the increased understanding of human behaviour in risky situations.This work was supported by the European Union's Horizon 2020 funded project "Modelling and predicting human decision making using measures of subconscious brain processes through mixed reality interfaces and biometric signals (RHUMBO)" (No 813234), the Spanish Ministry of Economy, Industry and Competitiveness funded project "Assessment and training on decision making in risk environments (ATEMIN)" (RTC-20176523-6; MINECO/AEI/FEDER, UE), and by the Generalitat Valenciana funded project "Mixed reality and brain decision (REBRAND)" (PROMETEO/2019/105).Juan-Ripoll, CD.; Chicchi-Giglioli, IA.; Llanes-Jurado, J.; Marín-Morales, J.; Alcañiz Raya, ML. (2021). Why do we take risks? Perception of the situation and risk proneness predict domain-specific risk taking. Frontiers in Psychology. 12:1-12. https://doi.org/10.3389/fpsyg.2021.562381S1121

An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures

[EN] The tool presented in this article can be applied as an ecological measure for evaluating decision-making processes in risky situations. It can be used in different contexts from both Occupational Safety and Health practices and for research purposes. Risk taking (RT) measurement constitutes a challenge for researchers and practitioners and has been addressed from different perspectives. Personality traits and temperamental aspects such as sensation seeking and impulsivity influence the individual's approach to RT, prompting risk-seeking or risk-aversion behaviors. Virtual reality has emerged as a suitable tool for RT measurement, since it enables the exposure of a person to realistic risks, allowing embodied interactions, the application of stealth assessment techniques and physiological real-time measurement. In this article, we present the assessment on decision making in risk environments (AEMIN) tool, as an enhanced version of the spheres and shield maze task, a previous tool developed by the authors. The main aim of this article is to study whether it is possible is to discriminate participants with high versus low scores in the measures of personality, sensation seeking and impulsivity, through their behaviors and physiological responses during playing AEMIN. Applying machine learning methods to the dataset we explored: (a) if through these data it is possible to discriminate between the two populations in each variable; and (b) which parameters better discriminate between the two populations in each variable. The results support the use of AEMIN as an ecological assessment tool to measure RT, since it brings to light behaviors that allow to classify the subjects into high/low risk-related psychological constructs. Regarding physiological measures, galvanic skin response seems to be less salient in prediction models.This research was funded by the Spanish Ministry of Economy and Competitiveness funded project "Assessment and Training on Decision Making in Risk Environments", grant number RTC-2017-6523-6, by the Gerenaliat Valenciana funded project "Rebrand", grant number PROMETEU/2019/105, and by the European Union ERDF (European Regional Development Fund) program of the Valencian Community 2014-2020 funded project "Interfaces de realidad mixta aplicada a salud y toma de decisiones", grant number IDIFEDER/2018/029.Juan-Ripoll, CD.; Llanes-Jurado, J.; Chicchi-Giglioli, IA.; Marín-Morales, J.; Alcañiz Raya, ML. (2021). An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures. Applied Sciences. 11(2):1-21. https://doi.org/10.3390/app11020825S12111

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

[EN] 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.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder. Journal of Clinical Medicine. 9(5):1-20. https://doi.org/10.3390/jcm9051260S12095https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disordersAnagnostou, E., Zwaigenbaum, L., Szatmari, P., Fombonne, E., Fernandez, B. A., Woodbury-Smith, M., … Scherer, S. W. (2014). Autism spectrum disorder: advances in evidence-based practice. Canadian Medical Association Journal, 186(7), 509-519. doi:10.1503/cmaj.121756Lord, C., Risi, S., DiLavore, P. S., Shulman, C., Thurm, A., & Pickles, A. (2006). Autism From 2 to 9 Years of Age. Archives of General Psychiatry, 63(6), 694. doi:10.1001/archpsyc.63.6.694Schmidt, L., Kirchner, J., Strunz, S., Broźus, J., Ritter, K., Roepke, S., & Dziobek, I. (2015). Psychosocial Functioning and Life Satisfaction in Adults With Autism Spectrum Disorder Without Intellectual Impairment. Journal of Clinical Psychology, 71(12), 1259-1268. doi:10.1002/jclp.22225Turner, M. (1999). Annotation: Repetitive Behaviour in Autism: A Review of Psychological Research. Journal of Child Psychology and Psychiatry, 40(6), 839-849. doi:10.1111/1469-7610.00502Lewis, M. H., & Bodfish, J. W. (1998). Repetitive behavior disorders in autism. Mental Retardation and Developmental Disabilities Research Reviews, 4(2), 80-89. doi:10.1002/(sici)1098-2779(1998)4:23.0.co;2-0Mahone, E. M., Bridges, D., Prahme, C., & Singer, H. S. (2004). Repetitive arm and hand movements (complex motor stereotypies) in children. The Journal of Pediatrics, 145(3), 391-395. doi:10.1016/j.jpeds.2004.06.014MacDonald, R., Green, G., Mansfield, R., Geckeler, A., Gardenier, N., Anderson, J., … Sanchez, J. (2007). Stereotypy in young children with autism and typically developing children. Research in Developmental Disabilities, 28(3), 266-277. doi:10.1016/j.ridd.2006.01.004Singer, H. S. (2009). Motor Stereotypies. Seminars in Pediatric Neurology, 16(2), 77-81. doi:10.1016/j.spen.2009.03.008Lidstone, J., Uljarević, M., Sullivan, J., Rodgers, J., McConachie, H., Freeston, M., … Leekam, S. (2014). Relations among restricted and repetitive behaviors, anxiety and sensory features in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 8(2), 82-92. doi:10.1016/j.rasd.2013.10.001GOLDMAN, S., WANG, C., SALGADO, M. W., GREENE, P. E., KIM, M., & RAPIN, I. (2009). Motor stereotypies in children with autism and other developmental disorders. Developmental Medicine & Child Neurology, 51(1), 30-38. doi:10.1111/j.1469-8749.2008.03178.xLord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659-685. doi:10.1007/bf02172145Volkmar, F. R., State, M., & Klin, A. (2009). Autism and autism spectrum disorders: diagnostic issues for the coming decade. Journal of Child Psychology and Psychiatry, 50(1-2), 108-115. doi:10.1111/j.1469-7610.2008.02010.xReaven, J. A., Hepburn, S. L., & Ross, R. G. (2008). Use of the ADOS and ADI-R in Children with Psychosis: Importance of Clinical Judgment. Clinical Child Psychology and Psychiatry, 13(1), 81-94. doi:10.1177/1359104507086343Torres, E. B., Brincker, M., Isenhower, R. W., Yanovich, P., Stigler, K. A., Nurnberger, J. I., … José, J. V. (2013). Autism: the micro-movement perspective. Frontiers in Integrative Neuroscience, 7. doi:10.3389/fnint.2013.00032Möricke, E., Buitelaar, J. K., & Rommelse, N. N. J. (2015). Do We Need Multiple Informants When Assessing Autistic Traits? The Degree of Report Bias on Offspring, Self, and Spouse Ratings. Journal of Autism and Developmental Disorders, 46(1), 164-175. doi:10.1007/s10803-015-2562-yCHAYTOR, N., SCHMITTEREDGECOMBE, M., & BURR, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21(3), 217-227. doi:10.1016/j.acn.2005.12.002Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. doi:10.1037/h0047470Gillberg, C., & Rasmussen, P. (1994). Brief report: Four case histories and a literature review of williams syndrome and autistic behavior. Journal of Autism and Developmental Disorders, 24(3), 381-393. doi:10.1007/bf02172235Parsons, S. (2016). Authenticity in Virtual Reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138-157. doi:10.1016/j.edurev.2016.08.001Francis, K. (2005). Autism interventions: a critical update. Developmental Medicine & Child Neurology, 47(7), 493-499. doi:10.1017/s0012162205000952Albinali, F., Goodwin, M. S., & Intille, S. S. (2009). Recognizing stereotypical motor movements in the laboratory and classroom. Proceedings of the 11th international conference on Ubiquitous computing. doi:10.1145/1620545.1620555Pyles, D. A. M., Riordan, M. M., & Bailey, J. S. (1997). The stereotypy analysis: An instrument for examining environmental variables associated with differential rates of stereotypic behavior. Research in Developmental Disabilities, 18(1), 11-38. doi:10.1016/s0891-4222(96)00034-0Nosek, B. A., Hawkins, C. B., & Frazier, R. S. (2011). Implicit social cognition: from measures to mechanisms. Trends in Cognitive Sciences, 15(4), 152-159. doi:10.1016/j.tics.2011.01.005Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A. (2019). A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology, 117(3), 522-559. doi:10.1037/pspa0000160LeDoux, J. E., & Pine, D. S. (2016). Using Neuroscience to Help Understand Fear and Anxiety: A Two-System Framework. American Journal of Psychiatry, 173(11), 1083-1093. doi:10.1176/appi.ajp.2016.16030353Fenning, R. M., Baker, J. K., Baucom, B. R., Erath, S. A., Howland, M. A., & Moffitt, J. (2017). Electrodermal Variability and Symptom Severity in Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 47(4), 1062-1072. doi:10.1007/s10803-016-3021-0Nikula, R. (1991). Psychological Correlates of Nonspecific Skin Conductance Responses. Psychophysiology, 28(1), 86-90. doi:10.1111/j.1469-8986.1991.tb03392.xAlcañiz Raya, M., Chicchi Giglioli, I. A., Marín-Morales, J., Higuera-Trujillo, J. L., Olmos, E., Minissi, M. E., … Abad, L. (2020). Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Frontiers in Human Neuroscience, 14. doi:10.3389/fnhum.2020.00090Cunningham, W. A., Raye, C. L., & Johnson, M. K. (2004). Implicit and Explicit Evaluation: fMRI Correlates of Valence, Emotional Intensity, and Control in the Processing of Attitudes. Journal of Cognitive Neuroscience, 16(10), 1717-1729. doi:10.1162/0898929042947919Kopton, I. M., & Kenning, P. (2014). Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00549Nickel, P., & Nachreiner, F. (2003). Sensitivity and Diagnosticity of the 0.1-Hz Component of Heart Rate Variability as an Indicator of Mental Workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(4), 575-590. doi:10.1518/hfes.45.4.575.27094Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. doi:10.1038/mp.2013.78Van Hecke, A. V., Lebow, J., Bal, E., Lamb, D., Harden, E., Kramer, A., … Porges, S. W. (2009). Electroencephalogram and Heart Rate Regulation to Familiar and Unfamiliar People in Children With Autism Spectrum Disorders. Child Development, 80(4), 1118-1133. doi:10.1111/j.1467-8624.2009.01320.xCoronato, A., De Pietro, G., & Paragliola, G. (2014). A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Systems with Applications, 41(17), 7868-7877. doi:10.1016/j.eswa.2014.05.011Goodwin, M. S., Intille, S. S., Albinali, F., & Velicer, W. F. (2010). Automated Detection of Stereotypical Motor Movements. Journal of Autism and Developmental Disorders, 41(6), 770-782. doi:10.1007/s10803-010-1102-zRodrigues, J. L., Gonçalves, N., Costa, S., & Soares, F. (2013). Stereotyped movement recognition in children with ASD. Sensors and Actuators A: Physical, 202, 162-169. doi:10.1016/j.sna.2013.04.019Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., & Castiglioni, I. (2015). Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. Journal of Autism and Developmental Disorders, 45(7), 2146-2156. doi:10.1007/s10803-015-2379-8Wedyan, M., Al-Jumaily, A., & Crippa, A. (2019). Using machine learning to perform early diagnosis of Autism Spectrum Disorder based on simple upper limb movements. International Journal of Hybrid Intelligent Systems, 15(4), 195-206. doi:10.3233/his-190278Parsons, S., Mitchell, P., & Leonard, A. (2004). The Use and Understanding of Virtual Environments by Adolescents with Autistic Spectrum Disorders. Journal of Autism and Developmental Disorders, 34(4), 449-466. doi:10.1023/b:jadd.0000037421.98517.8dParsons, T. D., Rizzo, A. A., Rogers, S., & York, P. (2009). Virtual reality in paediatric rehabilitation: A review. Developmental Neurorehabilitation, 12(4), 224-238. doi:10.1080/17518420902991719Bowman, D. A., Gabbard, J. L., & Hix, D. (2002). A Survey of Usability Evaluation in Virtual Environments: Classification and Comparison of Methods. Presence: Teleoperators and Virtual Environments, 11(4), 404-424. doi:10.1162/105474602760204309Pastorelli, E., & Herrmann, H. (2013). A Small-scale, Low-budget Semi-immersive Virtual Environment for Scientific Visualization and Research. Procedia Computer Science, 25, 14-22. doi:10.1016/j.procs.2013.11.003Cobb, S. V. G., Nichols, S., Ramsey, A., & Wilson, J. R. (1999). Virtual Reality-Induced Symptoms and Effects (VRISE). Presence: Teleoperators and Virtual Environments, 8(2), 169-186. doi:10.1162/105474699566152Wallace, S., Parsons, S., Westbury, A., White, K., White, K., & Bailey, A. (2010). Sense of presence and atypical social judgments in immersive virtual environments. Autism, 14(3), 199-213. doi:10.1177/1362361310363283Lorenzo, G., Lledó, A., Arráez-Vera, G., & Lorenzo-Lledó, A. (2018). The application of immersive virtual reality for students with ASD: A review between 1990–2017. Education and Information Technologies, 24(1), 127-151. doi:10.1007/s10639-018-9766-7Bailenson, J. N., Yee, N., Merget, D., & Schroeder, R. (2006). The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction. Presence: Teleoperators and Virtual Environments, 15(4), 359-372. doi:10.1162/pres.15.4.359Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02086Cummings, J. J., & Bailenson, J. N. (2015). How Immersive Is Enough? A Meta-Analysis of the Effect of Immersive Technology on User Presence. Media Psychology, 19(2), 272-309. doi:10.1080/15213269.2015.1015740Skalski, P., & Tamborini, R. (2007). The Role of Social Presence in Interactive Agent-Based Persuasion. Media Psychology, 10(3), 385-413. doi:10.1080/15213260701533102Slater, M. (2009). Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3549-3557. doi:10.1098/rstb.2009.0138Baños, R. M., Botella, C., Garcia-Palacios, A., Villa, H., Perpiña, C., & Alcañiz, M. (2000). Presence and Reality Judgment in Virtual Environments: A Unitary Construct? CyberPsychology & Behavior, 3(3), 327-335. doi:10.1089/10949310050078760Bente, G., Rüggenberg, S., Krämer, N. C., & Eschenburg, F. (2008). Avatar-Mediated Networking: Increasing Social Presence and Interpersonal Trust in Net-Based Collaborations. Human Communication Research, 34(2), 287-318. doi:10.1111/j.1468-2958.2008.00322.xHeeter, C. (1992). Being There: The Subjective Experience of Presence. Presence: Teleoperators and Virtual Environments, 1(2), 262-271. doi:10.1162/pres.1992.1.2.262Sanchez-Vives, M. V., & Slater, M. (2005). From presence to consciousness through virtual reality. Nature Reviews Neuroscience, 6(4), 332-339. doi:10.1038/nrn1651Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013). Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health. General Hospital Psychiatry, 35(4), 332-338. doi:10.1016/j.genhosppsych.2013.03.008Neguț, A., Matu, S.-A., Sava, F. A., & David, D. (2016). Virtual reality measures in neuropsychological assessment: a meta-analytic review. The Clinical Neuropsychologist, 30(2), 165-184. doi:10.1080/13854046.2016.1144793Riva, G. (2005). Virtual Reality in Psychotherapy: Review. CyberPsychology & Behavior, 8(3), 220-230. doi:10.1089/cpb.2005.8.220Valmaggia, L. R., Latif, L., Kempton, M. J., & Rus-Calafell, M. (2016). Virtual reality in the psychological treatment for mental health problems: An systematic review of recent evidence. Psychiatry Research, 236, 189-195. doi:10.1016/j.psychres.2016.01.015Mesa-Gresa, P., Gil-Gómez, H., Lozano-Quilis, J.-A., & Gil-Gómez, J.-A. (2018). Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review. Sensors, 18(8), 2486. doi:10.3390/s18082486Cheng, Y., & Ye, J. (2010). Exploring the social competence of students with autism spectrum conditions in a collaborative virtual learning environment – The pilot study. Computers & Education, 54(4), 1068-1077. doi:10.1016/j.compedu.2009.10.011Jarrold, W., Mundy, P., Gwaltney, M., Bailenson, J., Hatt, N., McIntyre, N., … Swain, L. (2013). Social Attention in a Virtual Public Speaking Task in Higher Functioning Children With Autism. Autism Research, 6(5), 393-410. doi:10.1002/aur.1302Forgeot d’Arc, B., Ramus, F., Lefebvre, A., Brottier, D., Zalla, T., Moukawane, S., … Delorme, R. (2014). Atypical Social Judgment and Sensitivity to Perceptual Cues in Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 46(5), 1574-1581. doi:10.1007/s10803-014-2208-5Maskey, M., Lowry, J., Rodgers, J., McConachie, H., & Parr, J. R. (2014). Reducing Specific Phobia/Fear in Young People with Autism Spectrum Disorders (ASDs) through a Virtual Reality Environment Intervention. PLoS ONE, 9(7), e100374. doi:10.1371/journal.pone.0100374Baron-Cohen, S., Ashwin, E., Ashwin, C., Tavassoli, T., & Chakrabarti, B. (2009). Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1522), 1377-1383. doi:10.1098/rstb.2008.0337Tomchek, S. D., Huebner, R. A., & Dunn, W. (2014). Patterns of sensory processing in children with an autism spectrum disorder. Research in Autism Spectrum Disorders, 8(9), 1214-1224. doi:10.1016/j.rasd.2014.06.006Boyd, B. A., Baranek, G. T., Sideris, J., Poe, M. D., Watson, L. R., Patten, E., & Miller, H. (2010). Sensory features and repetitive behaviors in children with autism and developmental delays. Autism Research, n/a-n/a. doi:10.1002/aur.124Gabriels, R. L., Agnew, J. A., Miller, L. J., Gralla, J., Pan, Z., Goldson, E., … Hooks, E. (2008). Is there a relationship between restricted, repetitive, stereotyped behaviors and interests and abnormal sensory response in children with autism spectrum disorders? Research in Autism Spectrum Disorders, 2(4), 660-670. doi:10.1016/j.rasd.2008.02.002Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186. doi:10.1109/tpami.2019.2929257Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353-363. doi:10.1016/j.snb.2015.02.025Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199O’Neill, M., & Jones, R. S. P. (1997). Journal of Autism and Developmental Disorders, 27(3), 283-293. doi:10.1023/a:1025850431170Foss-Feig, J. H., Kwakye, L. D., Cascio, C. J., Burnette, C. P., Kadivar, H., Stone, W. L., & Wallace, M. T. (2010). An extended multisensory temporal binding window in autism spectrum disorders. Experimental Brain Research, 203(2), 381-389. doi:10.1007/s00221-010-2240-4Courchesne, E., Lincoln, A. J., Kilman, B. A., & Galambos, R. (1985). Event-related brain potential correlates of the processing of novel visual and auditory information in autism. Journal of Autism and Developmental Disorders, 15(1), 55-76. doi:10.1007/bf01837899Russo, N., Foxe, J. J., Brandwein, A. B., Altschuler, T., Gomes, H., & Molholm, S. (2010). Multisensory processing in children with autism: high-density electrical mapping of auditory-somatosensory integration. Autism Research, 3(5), 253-267. doi:10.1002/aur.152Ament, K., Mejia, A., Buhlman, R., Erklin, S., Caffo, B., Mostofsky, S., & Wodka, E. (2014). Evidence for Specificity of Motor Impairments in Catching and Balance in Children with Autism. Journal of Autism and Developmental Disorders, 45(3), 742-751. doi:10.1007/s10803-014-2229-

Applying machine learning to a virtual serious game for neuropsychological assessment.

[Otros] Neuropsychological assessment has been traditionally made through paper-and-pencil batteries which usually are time-consuming, decontextualized, and nonecological. These abilities play a critical role in education since they are very related to learning capacity, academic achievement, social functioning, as well as the inhibition of maladaptive behaviors. Meanwhile, serious games are being used in education and psychology to achieve assessments without these limitations, including neuropsychological assessments. While traditional tests can be analyzed with classical statistics, a large number of variables can be extracted from serious games, the analysis of which can be more complex. Machine learning can handle this large amount of information and find patterns that allow us to recognize behaviors. This study aimed to investigate whether machine learning could be used to improve predictive validity in applying a serious game for neuropsychological assessment. Results were based on 60 subjects, including 42 cognitive activities. The validation process showed best results on attention, memory, planning, and cognitive flexibility, achieving accuracies higher or equal to 0.8 and Cohen¿s Kappas higher than 0.55, which implies that the Virtual Serious Game could be a valid tool to perform a neuropsychological evaluation along with traditional tests.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded project Advanced Therapeutically Tools for Mental Health (DPI2016-77396-R) and by the European Union through the Operational Program of the European Regional Development Fund (ERDF) on the Valencian Community 2010-2020 (IDIFEDER/2018/029).Marín-Morales, J.; Carrasco-Ribelles, LA.; Alcañiz Raya, ML.; Chicchi-Giglioli, IA. (2021). Applying machine learning to a virtual serious game for neuropsychological assessment. IEEE. 951-954. https://doi.org/10.1109/EDUCON46332.2021.9454138S95195

Pilot study on effectiveness of a virtual game training on executive functions

[Otros] Attention, control inhibition, and visual-spatial working memory represent the three basic sets of cognitive processes involving on executive functions (EF). Basic EF are relevant abilities in daily life that allow to control and monitor adapted behaviors in order to achieve specific goals. In the educational field, EF are related to academic achievement, social functioning, as well as the inhibition of maladaptive behaviors. Their impairment often leads to an incapacity to perform multiple and simultaneous mental activities, as well as to plan and monitor learning. The main aim of cognitive neuropsychology intervention is to identify effective methods that allow transferring trained strategies and abilities to daily life. Accordingly, virtual reality games (VRG) are showing ecological validity effectiveness in EF training. In this framework, the aim of this study was to examine the effectiveness of a VRG cooking-based for improving basic EF processing. 31 healthy subjects (M=24.3; SD=2.51) participated to 3 training sessions of 25 minutes each. Each session involved 6 VRG characterized by different levels of difficulties. Three traditional measures were administered to participants pre- and post- VRG: The Corsi test for assessing visual-spatial working memory, the Dual-task, and the Flanker task for attention and inhibition control respectively. The results reported a significant improvement of the three EF abilities after training, showing the potential effectiveness of a VRG along with the traditional measures. Future studies on students with learning disabilities are needed to compare performance and effectiveness.Chicchi-Giglioli, IA.; Mussoni, S.; Cipresso, P.; Marín-Morales, J.; Riva, G.; Alcañiz Raya, ML. (2021). Pilot study on effectiveness of a virtual game training on executive functions. IEEE. 956-960. https://doi.org/10.1109/EDUCON46332.2021.9453899S95696

Evaluación ecológica mediante Realidad Virtual de las necesidades psicológicas básicas

Pese a que las técnicas de evaluación psicológica comúnmente utilizadas en lápiz y papel son una estrategia elegida por su adecuada validez, se presentan algunas limitaciones importantes que pueden superarse por los avances recientes en Realidad Virtual (RV), al permitir la evaluación de constructos psicológicos en entornos inmersivos, como una forma de evaluación ecológica. Es así que el propósito de la presente investigación fue determinar la eficacia de una herramienta de realidad virtual en la evaluación de cuatro necesidades psicológicas básicas: apego, autoestima, autoeficacia, maximización del placer/minimización del dolor. La muestra la conformaron 61 participantes, quienes fueron expuestos a entornos virtuales centrados en la evaluación conductual de cada uno de estos constructos. Los resultados mostraron una adecuada precisión de los entornos de RV en cuanto al reconocimiento de las necesidades evaluadas. En conclusión, los hallazgos permitieron contar con mayor evidencia en cuanto al uso de la RV como una alternativa válida para la medición de los constructos, se reconocen limitaciones importantes referentes al número limitado de participantes y a la ausencia de población clínica.

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

[EN] 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.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness-funded project Immersive Virtual Environment for the Evaluation and Training of Children with Autism Spectrum Disorder: T Room (IDI-20170912) and by the Generalitat Valenciana-funded project REBRAND (PROMETEU/2019/105).Alcañiz Raya, ML.; Chicchi-Giglioli, IA.; Marín-Morales, J.; Higuera-Trujillo, JL.; Olmos-Raya, E.; Minissi, ME.; Teruel García, G.... (2020). Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Frontiers in Human Neuroscience. 14:1-16. https://doi.org/10.3389/fnhum.2020.00090S11614Allen, R., Davis, R., & Hill, E. (2012). The Effects of Autism and Alexithymia on Physiological and Verbal Responsiveness to Music. Journal of Autism and Developmental Disorders, 43(2), 432-444. doi:10.1007/s10803-012-1587-8Anagnostou, E., Zwaigenbaum, L., Szatmari, P., Fombonne, E., Fernandez, B. A., Woodbury-Smith, M., … Scherer, S. W. (2014). Autism spectrum disorder: advances in evidence-based practice. Canadian Medical Association Journal, 186(7), 509-519. doi:10.1503/cmaj.121756Ashwin, C., Chapman, E., Howells, J., Rhydderch, D., Walker, I., & Baron-Cohen, S. (2014). Enhanced olfactory sensitivity in autism spectrum conditions. Molecular Autism, 5(1), 53. doi:10.1186/2040-2392-5-53Baron-Cohen, S. (1990). Autism: A Specific Cognitive Disorder of & lsquo;Mind-Blindness’. International Review of Psychiatry, 2(1), 81-90. doi:10.3109/09540269009028274Baron-Cohen, S., Ashwin, E., Ashwin, C., Tavassoli, T., & Chakrabarti, B. (2009). Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1522), 1377-1383. doi:10.1098/rstb.2008.0337Barry, R. J., & James, A. L. (1988). Coding of stimulus parameters in autistic, retarded, and normal children: evidence for a two-factor theory of autism. International Journal of Psychophysiology, 6(2), 139-149. doi:10.1016/0167-8760(88)90045-1Bekele, E., Crittendon, J., Zheng, Z., Swanson, A., Weitlauf, A., Warren, Z., & Sarkar, N. (2014). Assessing the Utility of a Virtual Environment for Enhancing Facial Affect Recognition in Adolescents with Autism. Journal of Autism and Developmental Disorders, 44(7), 1641-1650. doi:10.1007/s10803-014-2035-8Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80-91. doi:10.1016/j.jneumeth.2010.04.028Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002). TARGET ARTICLE: Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology. Psychological Inquiry, 13(2), 103-124. doi:10.1207/s15327965pli1302_01Boucsein, W. (2012). Electrodermal Activity. doi:10.1007/978-1-4614-1126-0Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. doi:10.1037/h0047470BUJNAKOVA, I., ONDREJKA, I., MESTANIK, M., VISNOVCOVA, Z., MESTANIKOVA, A., HRTANEK, I., … TONHAJZEROVA, I. (2016). Autism Spectrum Disorder Is Associated With Autonomic Underarousal. Physiological Research, S673-S682. doi:10.33549/physiolres.933528Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Chang, M. C., Parham, L. D., Blanche, E. I., Schell, A., Chou, C.-P., Dawson, M., & Clark, F. (2012). Autonomic and Behavioral Responses of Children With Autism to Auditory Stimuli. American Journal of Occupational Therapy, 66(5), 567-576. doi:10.5014/ajot.2012.004242CHAYTOR, N., SCHMITTEREDGECOMBE, M., & BURR, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21(3), 217-227. doi:10.1016/j.acn.2005.12.002Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clinical, 8, 238-245. doi:10.1016/j.nicl.2015.04.002Chita-Tegmark, M. (2016). Attention Allocation in ASD: a Review and Meta-analysis of Eye-Tracking Studies. Review Journal of Autism and Developmental Disorders, 3(3), 209-223. doi:10.1007/s40489-016-0077-xFenwick, T. (2014). Social Media and Medical Professionalism. Academic Medicine, 89(10), 1331-1334. doi:10.1097/acm.0000000000000436Delobel-Ayoub, M., Ehlinger, V., Klapouszczak, D., Maffre, T., Raynaud, J.-P., Delpierre, C., & Arnaud, C. (2015). Socioeconomic Disparities and Prevalence of Autism Spectrum Disorders and Intellectual Disability. PLOS ONE, 10(11), e0141964. doi:10.1371/journal.pone.0141964Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. doi:10.1038/mp.2013.78Dudova, I., Vodicka, J., Havlovicova, M., Sedlacek, Z., Urbanek, T., & Hrdlicka, M. (2011). Odor detection threshold, but not odor identification, is impaired in children with autism. European Child & Adolescent Psychiatry, 20(7), 333-340. doi:10.1007/s00787-011-0177-1Fagius, J., & Wallin, B. G. (1980). Sympathetic reflex latencies and conduction velocities in normal man. Journal of the Neurological Sciences, 47(3), 433-448. doi:10.1016/0022-510x(80)90098-2Fenning, R. M., Baker, J. K., Baucom, B. R., Erath, S. A., Howland, M. A., & Moffitt, J. (2017). Electrodermal Variability and Symptom Severity in Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 47(4), 1062-1072. doi:10.1007/s10803-016-3021-0Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A. (2019). A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology, 117(3), 522-559. doi:10.1037/pspa0000160Francis, K. (2007). Autism interventions: a critical update. Developmental Medicine & Child Neurology, 47(7), 493-499. doi:10.1111/j.1469-8749.2005.tb01178.xFriston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158. doi:10.1016/s2215-0366(14)70275-5Gillberg, C., & Rasmussen, P. (1994). Brief report: Four case histories and a literature review of williams syndrome and autistic behavior. Journal of Autism and Developmental Disorders, 24(3), 381-393. doi:10.1007/bf02172235Hirstein, W., Iversen, P., & Ramachandran, V. S. (2001). Autonomic responses of autistic children to people and objects. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1479), 1883-1888. doi:10.1098/rspb.2001.1724Hubert, B. E., Wicker, B., Monfardini, E., & Deruelle, C. (2009). Electrodermal reactivity to emotion processing in adults with autistic spectrum disorders. Autism, 13(1), 9-19. doi:10.1177/1362361308091649Hyde, K. K., Novack, M. N., LaHaye, N., Parlett-Pelleriti, C., Anden, R., Dixon, D. R., & Linstead, E. (2019). Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review. Review Journal of Autism and Developmental Disorders, 6(2), 128-146. doi:10.1007/s40489-019-00158-xJOSEPH, R. M., EHRMAN, K., MCNALLY, R., & KEEHN, B. (2008). Affective response to eye contact and face recognition ability in children with ASD. Journal of the International Neuropsychological Society, 14(6), 947-955. doi:10.1017/s1355617708081344Kandalaft, M. R., Didehbani, N., Krawczyk, D. C., Allen, T. T., & Chapman, S. B. (2012). Virtual Reality Social Cognition Training for Young Adults with High-Functioning Autism. Journal of Autism and Developmental Disorders, 43(1), 34-44. doi:10.1007/s10803-012-1544-6Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological Psychology, 84(3), 394-421. doi:10.1016/j.biopsycho.2010.03.010Kylliäinen, A., & Hietanen, J. K. (2006). Skin Conductance Responses to Another Person’s Gaze in Children with Autism. Journal of Autism and Developmental Disorders, 36(4), 517-525. doi:10.1007/s10803-006-0091-4Kylliäinen, A., Wallace, S., Coutanche, M. N., Leppänen, J. M., Cusack, J., Bailey, A. J., & Hietanen, J. K. (2012). Affective-motivational brain responses to direct gaze in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 53(7), 790-797. doi:10.1111/j.1469-7610.2011.02522.xLedoux, K., Coderre, E., Bosley, L., Buz, E., Gangopadhyay, I., & Gordon, B. (2015). The concurrent use of three implicit measures (eye movements, pupillometry, and event-related potentials) to assess receptive vocabulary knowledge in normal adults. Behavior Research Methods, 48(1), 285-305. doi:10.3758/s13428-015-0571-6Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2006). Describing the Sensory Abnormalities of Children and Adults with Autism. Journal of Autism and Developmental Disorders, 37(5), 894-910. doi:10.1007/s10803-006-0218-7Levy, A., & Perry, A. (2011). Outcomes in adolescents and adults with autism: A review of the literature. Research in Autism Spectrum Disorders, 5(4), 1271-1282. doi:10.1016/j.rasd.2011.01.023Li, B., Sharma, A., Meng, J., Purushwalkam, S., & Gowen, E. (2017). Applying machine learning to identify autistic adults using imitation: An exploratory study. PLOS ONE, 12(8), e0182652. doi:10.1371/journal.pone.0182652Liu, W., Li, M., & Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8), 888-898. doi:10.1002/aur.1615Lord, C., Risi, S., DiLavore, P. S., Shulman, C., Thurm, A., & Pickles, A. (2006). Autism From 2 to 9 Years of Age. Archives of General Psychiatry, 63(6), 694. doi:10.1001/archpsyc.63.6.694Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659-685. doi:10.1007/bf02172145Loth, E., Spooren, W., Ham, L. M., Isaac, M. B., Auriche-Benichou, C., Banaschewski, T., … Murphy, D. G. M. (2015). Identification and validation of biomarkers for autism spectrum disorders. Nature Reviews Drug Discovery, 15(1), 70-70. doi:10.1038/nrd.2015.7Louwerse, A., van der Geest, J. N., Tulen, J. H. M., van der Ende, J., Van Gool, A. R., Verhulst, F. C., & Greaves-Lord, K. (2013). Effects of eye gaze directions of facial images on looking behaviour and autonomic responses in adolescents with autism spectrum disorders. Research in Autism Spectrum Disorders, 7(9), 1043-1053. doi:10.1016/j.rasd.2013.04.013Lydon, S., Healy, O., Reed, P., Mulhern, T., Hughes, B. M., & Goodwin, M. S. (2014). A systematic review of physiological reactivity to stimuli in autism. Developmental Neurorehabilitation, 19(6), 335-355. doi:10.3109/17518423.2014.971975McCarthy, C., Pradhan, N., Redpath, C., & Adler, A. (2016). Validation of the Empatica E4 wristband. 2016 IEEE EMBS International Student Conference (ISC). doi:10.1109/embsisc.2016.7508621McCormick, C., Hessl, D., Macari, S. L., Ozonoff, S., Green, C., & Rogers, S. J. (2014). Electrodermal and Behavioral Responses of Children With Autism Spectrum Disorders to Sensory and Repetitive Stimuli. Autism Research, 7(4), 468-480. doi:10.1002/aur.1382Miller, L. J., Anzalone, M. E., Lane, S. J., Cermak, S. A., & Osten, E. T. (2007). Concept Evolution in Sensory Integration: A Proposed Nosology for Diagnosis. American Journal of Occupational Therapy, 61(2), 135-140. doi:10.5014/ajot.61.2.135Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1), 72-80. doi:10.1016/j.tics.2011.11.018Möricke, E., Buitelaar, J. K., & Rommelse, N. N. J. (2015). Do We Need Multiple Informants When Assessing Autistic Traits? The Degree of Report Bias on Offspring, Self, and Spouse Ratings. Journal of Autism and Developmental Disorders, 46(1), 164-175. doi:10.1007/s10803-015-2562-yMurphy, D., & Spooren, W. (2012). EU-AIMS: a boost to autism research. Nature Reviews Drug Discovery, 11(11), 815-816. doi:10.1038/nrd3881Nakai, Y., Takiguchi, T., Matsui, G., Yamaoka, N., & Takada, S. (2017). Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders. Perceptual and Motor Skills, 124(5), 961-973. doi:10.1177/0031512517716855Nikula, R. (1991). Psychological Correlates of Nonspecific Skin Conductance Responses. Psychophysiology, 28(1), 86-90. doi:10.1111/j.1469-8986.1991.tb03392.xNosek, B. A., Hawkins, C. B., & Frazier, R. S. (2011). Implicit social cognition: from measures to mechanisms. Trends in Cognitive Sciences, 15(4), 152-159. doi:10.1016/j.tics.2011.01.005Palkovitz, R. J., & Wiesenfeld, A. R. (1980). Differential autonomic responses of autistic and normal children. Journal of Autism and Developmental Disorders, 10(3), 347-360. doi:10.1007/bf02408294Parsons, S. (2016). Authenticity in Virtual Reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138-157. doi:10.1016/j.edurev.2016.08.001Parsons, T. D. (2016). Telemedicine, Mobile, and Internet-Based Neurocognitive Assessment. Clinical Neuropsychology and Technology, 99-111. doi:10.1007/978-3-319-31075-6_6Paulhus, D. L. (1991). Measurement and Control of Response Bias. Measures of Personality and Social Psychological Attitudes, 17-59. doi:10.1016/b978-0-12-590241-0.50006-xPicard, R. W., Fedor, S., & Ayzenberg, Y. (2015). Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry. Emotion Review, 8(1), 62-75. doi:10.1177/1754073914565517Reaven, J. A., Hepburn, S. L., & Ross, R. G. (2008). Use of the ADOS and ADI-R in Children with Psychosis: Importance of Clinical Judgment. Clinical Child Psychology and Psychiatry, 13(1), 81-94. doi:10.1177/1359104507086343Redish, A. D., & Gordon, J. A. (Eds.). (2016). Computational Psychiatry. doi:10.7551/mitpress/9780262035422.001.0001Riby, D. M., Whittle, L., & Doherty-Sneddon, G. (2012). Physiological reactivity to faces via live and video-mediated communication in typical and atypical development. Journal of Clinical and Experimental Neuropsychology, 34(4), 385-395. doi:10.1080/13803395.2011.645019Rogers, S. J., & Ozonoff, S. (2005). Annotation: What do we know about sensory dysfunction in autism? A critical review of the empirical evidence. Journal of Child Psychology and Psychiatry, 46(12), 1255-1268. doi:10.1111/j.1469-7610.2005.01431.xSchmidt, L., Kirchner, J., Strunz, S., Broźus, J., Ritter, K., Roepke, S., & Dziobek, I. (2015). Psychosocial Functioning and Life Satisfaction in Adults With Autism Spectrum Disorder Without Intellectual Impairment. Journal of Clinical Psychology, 71(12), 1259-1268. doi:10.1002/jclp.22225Schoen, S. A. (2009). Physiological and behavioral differences in sensory processing: a comparison of children with Autism Spectrum Disorder and Sensory Processing Disorder. Frontiers in Integrative Neuroscience, 3. doi:10.3389/neuro.07.029.2009Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Slater, M. (2009). Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3549-3557. doi:10.1098/rstb.2009.0138Slater, M., & Wilbur, S. (1997). A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 6(6), 603-616. doi:10.1162/pres.1997.6.6.603Stevens, S., & Gruzelier, J. (1984). Electrodermal activity to auditory stimuli in autistic, retarded, and normal children. Journal of Autism and Developmental Disorders, 14(3), 245-260. doi:10.1007/bf02409577Tomchek, S. D., & Dunn, W. (2007). Sensory Processing in Children With and Without Autism: A Comparative Study Using the Short Sensory Profile. American Journal of Occupational Therapy, 61(2), 190-200. doi:10.5014/ajot.61.2.190Tomchek, S. D., Huebner, R. A., & Dunn, W. (2014). Patterns of sensory processing in children with an autism spectrum disorder. Research in Autism Spectrum Disorders, 8(9), 1214-1224. doi:10.1016/j.rasd.2014.06.006Van Engeland, H., Roelofs, J. W., Verbaten, M. N., & Slangen, J. L. (1991). Abnormal electrodermal reactivity to novel visual stimuli in autistic children. Psychiatry Research, 38(1), 27-38. doi:10.1016/0165-1781(91)90050-yVan Hecke, A. V., Stevens, S., Carson, A. M., Karst, J. S., Dolan, B., Schohl, K., … Brockman, S. (2013). Measuring the Plasticity of Social Approach: A Randomized Controlled Trial of the Effects of the PEERS Intervention on EEG Asymmetry in Adolescents with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 45(2), 316-335. doi:10.1007/s10803-013-1883-yVolkmar, F. R., State, M., & Klin, A. (2009). Autism and autism spectrum disorders: diagnostic issues for the coming decade. Journal of Child Psychology and Psychiatry, 50(1-2), 108-115. doi:10.1111/j.1469-7610.2008.02010.xWang, X.-J., & Krystal, J. H. (2014). Computational Psychiatry. Neuron, 84(3), 638-654. doi:10.1016/j.neuron.2014.10.018Wang, Y., Hensley, M. K., Tasman, A., Sears, L., Casanova, M. F., & Sokhadze, E. M. (2015). Heart Rate Variability and Skin Conductance During Repetitive TMS Course in Children with Autism. Applied Psychophysiology and Biofeedback, 41(1), 47-60. doi:10.1007/s10484-015-9311-zWhite, M. P., Yeo, N., Vassiljev, P., Lundstedt, R., Wallergård, M., Albin, M., & Lõhmus, M. (2018). A prescription for “nature” – the potential of using virtual nature in therapeutics. Neuropsychiatric Disease and Treatment, Volume 14, 3001-3013. doi:10.2147/ndt.s179038White, S. W., Mazefsky, C. A., Dichter, G. S., Chiu, P. H., Richey, J. A., & Ollendick, T. H. (2014). Social‐cognitive, physiological, and neural mechanisms underlying emotion regulation impairments: understanding anxiety in autism spectrum disorder. International Journal of Developmental Neuroscience, 39(1), 22-36. doi:10.1016/j.ijdevneu.2014.05.012Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: Better or worse than the DSM-IV? Research in Developmental Disabilities, 32(2), 768-773. doi:10.1016/j.ridd.2010.11.003Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353-363. doi:10.1016/j.snb.2015.02.025Zahn, T. P., Rumsey, J. M., & Van Kammen, D. P. (1987). Autonomic nervous system activity in autistic, schizophrenic, and normal men: Effects of stimulus significance. Journal of Abnormal Psychology, 96(2), 135-144. doi:10.1037/0021-843x.96.2.13

Challenge for the Assessment and Treatment of Psychological Disorders

Augmented Reality is a new technological system that allows introducing virtual contents in the real world in order to run in the same representation and, in real time, enhancing the user's sensory perception of reality. From another point of view, Augmented Reality can be defined as a set of techniques and tools that add information to the physical reality. To date, Augmented Reality has been used in many fields, such as medicine, entertainment, maintenance, architecture, education, and cognitive and motor rehabilitation but very few studies and applications of AR exist in clinical psychology. In the treatment of psychological disorders, Augmented Reality has given preliminary evidence to be a useful tool due to its adaptability to the patient needs and therapeutic purposes and interactivity. Another relevant factor is the quality of the user's experience in the Augmented Reality system determined from emotional engagement and sense of presence. This experience could increase the AR ecological validity in the treatment of psychological disorders. This paper reviews the recent studies on the use of Augmented Reality in the evaluation and treatment of psychological disorders, focusing on current uses of this technology and on the specific features that delineate Augmented Reality a new technique useful for psychology