Virtual Reality as a new approach for risk taking assessment

[EN] Understanding how people behave when facing hazardous situations, how intrinsic and extrinsic factors influence the risk taking (RT) decision making process and to what extent it is possible to modify their reactions externally, are questions that have long interested academics and society in general. In the spheres, among others, of Occupational Safety and Health (OSH), the military, finance and sociology, this topic has multidisciplinary implications because we all constantly face RT situations. Researchers have hitherto assessed RT profiles by conducting questionnaires prior to and after the presentation of stimuli; however, this can lead to the production of biased, non-realistic, RT profiles. This is due to the reflexive nature of choosing an answer in a questionnaire, which is remote from the reactive, emotional and impulsive decision making processes inherent to real, risky situations. One way to address this question is to exploit VR capabilities to generate immersive environments that recreate realistic seeming but simulated hazardous situations. We propose VR as the next-generation tool to study RT processes, taking advantage of the big four families of metrics which can provide objective assessment methods with high ecological validity: the real-world risks approach (high presence VR environments triggering real-world reactions), embodied interactions (more natural interactions eliciting more natural behaviors), stealth assessment (unnoticed real-time assessments offering efficient behavioral metrics) and physiological real-time measurement (physiological signals avoiding subjective bias). Additionally, VR can provide an invaluable tool, after the assessment phase, to train in skills related to RT due to its transferability to real-world situations.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded projects "Advanced Therapeutic Tools for Mental Health" (DPI2016-77396-R), and "Assessment and Training on Decision Making in Risk Environments" (RTC-2017-6523-6) (MINECO/AEI/FEDER, UE).Juan-Ripoll, CD.; Soler-Domínguez, J.; Guixeres Provinciale, J.; Contero, M.; Álvarez Gutiérrez, N.; Alcañiz Raya, ML. (2018). Virtual Reality as a new approach for risk taking assessment. Frontiers in Psychology. 9:1-8. https://doi.org/10.3389/fpsyg.2018.02532S189Alcañiz, M., Rey, B., Tembl, J., & Parkhutik, V. (2009). A Neuroscience Approach to Virtual Reality Experience Using Transcranial Doppler Monitoring. Presence: Teleoperators and Virtual Environments, 18(2), 97-111. doi:10.1162/pres.18.2.97Baird, I. S., & Thomas, H. (1985). Toward a Contingency Model of Strategic Risk Taking. Academy of Management Review, 10(2), 230-243. doi:10.5465/amr.1985.4278108Barsade, S. G., Ramarajan, L., & Westen, D. (2009). Implicit affect in organizations☆. Research in Organizational Behavior, 29, 135-162. doi:10.1016/j.riob.2009.06.008Becker, W. J., Cropanzano, R., & Sanfey, A. G. (2011). Organizational Neuroscience: Taking Organizational Theory Inside the Neural Black Box. Journal of Management, 37(4), 933-961. doi:10.1177/0149206311398955Bohil, C. J., Alicea, B., & Biocca, F. A. (2011). Virtual reality in neuroscience research and therapy. Nature Reviews Neuroscience, 12(12), 752-762. doi:10.1038/nrn3122Bornovalova, M. A., Cashman-Rolls, A., O’Donnell, J. M., Ettinger, K., Richards, J. B., deWit, H., & Lejuez, C. W. (2009). Risk taking differences on a behavioral task as a function of potential reward/loss magnitude and individual differences in impulsivity and sensation seeking. Pharmacology Biochemistry and Behavior, 93(3), 258-262. doi:10.1016/j.pbb.2008.10.023Bottari, C., Dassa, C., Rainville, C., & Dutil, É. (2009). The factorial validity and internal consistency of the Instrumental Activities of Daily Living Profile in individuals with a traumatic brain injury. Neuropsychological Rehabilitation, 19(2), 177-207. doi:10.1080/09602010802188435BOYER, T. (2006). The development of risk-taking: A multi-perspective review. Developmental Review, 26(3), 291-345. doi:10.1016/j.dr.2006.05.002Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367-383. doi:10.1037/0033-2909.125.3.367Cavalcanti, J., & Soares, M. (2012). Ergonomic analysis of safety signs: a focus of informational and cultural ergonomics. Work, 41, 3427-3432. doi:10.3233/wor-2012-0619-3427Colquitt, J. A., Scott, B. A., & LePine, J. A. (2007). Trust, trustworthiness, and trust propensity: A meta-analytic test of their unique relationships with risk taking and job performance. Journal of Applied Psychology, 92(4), 909-927. doi:10.1037/0021-9010.92.4.909Cooper, M. L., Agocha, V. B., & Sheldon, M. S. (2000). A Motivational Perspective on Risky Behaviors: The Role of Personality and Affect Regulatory Processes. Journal of Personality, 68(6), 1059-1088. doi:10.1111/1467-6494.00126Donohew, R. L., Hoyle, R. H., Clayton, R. R., Skinner, W. F., Colon, S. E., & Rice, R. E. (1999). Sensation seeking and drug use by adolescents and their friends: models for marijuana and alcohol. Journal of Studies on Alcohol, 60(5), 622-631. doi:10.15288/jsa.1999.60.622Durantin, G., Gagnon, J.-F., Tremblay, S., & Dehais, F. (2014). Using near infrared spectroscopy and heart rate variability to detect mental overload. Behavioural Brain Research, 259, 16-23. doi:10.1016/j.bbr.2013.10.042Ernst, L. H., Plichta, M. M., Lutz, E., Zesewitz, A. K., Tupak, S. V., Dresler, T., … Fallgatter, A. J. (2013). Prefrontal activation patterns of automatic and regulated approach–avoidance reactions – A functional near-infrared spectroscopy (fNIRS) study. Cortex, 49(1), 131-142. doi:10.1016/j.cortex.2011.09.013Ford, M. E., Wentzel, K. R., Wood, D., Stevens, E., & Siesfeld, G. A. (1989). Processes Associated with Integrative Social Competence. Journal of Adolescent Research, 4(4), 405-425. doi:10.1177/074355488944002Forgas, J. P. (1982). Reactions to life dilemmas: Risk taking, success and responsibility attribution. Australian Journal of Psychology, 34(1), 25-35. doi:10.1080/00049538208254714Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological Bulletin, 117(1), 39-66. doi:10.1037/0033-2909.117.1.39Gallagher, S. (2005). How the Body Shapes the Mind. doi:10.1093/0199271941.001.0001Gardner, M., & Steinberg, L. (2005). Peer Influence on Risk Taking, Risk Preference, and Risky Decision Making in Adolescence and Adulthood: An Experimental Study. Developmental Psychology, 41(4), 625-635. doi:10.1037/0012-1649.41.4.625George, J. M. (2009). The Illusion of Will in Organizational Behavior Research: Nonconscious Processes and Job Design. Journal of Management, 35(6), 1318-1339. doi:10.1177/0149206309346337Glöckner, A., & Herbold, A.-K. (2010). An eye-tracking study on information processing in risky decisions: Evidence for compensatory strategies based on automatic processes. Journal of Behavioral Decision Making, 24(1), 71-98. doi:10.1002/bdm.684Greene, J. D. (2009). Dual-process morality and the personal/impersonal distinction: A reply to McGuire, Langdon, Coltheart, and Mackenzie. Journal of Experimental Social Psychology, 45(3), 581-584. doi:10.1016/j.jesp.2009.01.003Greene, J. D., Morelli, S. A., Lowenberg, K., Nystrom, L. E., & Cohen, J. D. (2008). Cognitive load selectively interferes with utilitarian moral judgment. Cognition, 107(3), 1144-1154. doi:10.1016/j.cognition.2007.11.004Greene, J. D. (2001). An fMRI Investigation of Emotional Engagement in Moral Judgment. Science, 293(5537), 2105-2108. doi:10.1126/science.1062872Gullone, E., Moore, S., Moss, S., & Boyd, C. (2000). The Adolescent Risk-Taking Questionnaire. Journal of Adolescent Research, 15(2), 231-250. doi:10.1177/0743558400152003Hedberg, J., & Alexander, S. (1994). Virtual Reality in Education: Defining Researchable Issues. Educational Media International, 31(4), 214-220. doi:10.1080/0952398940310402Horvath, P., & Zuckerman, M. (1993). Sensation seeking, risk appraisal, and risky behavior. Personality and Individual Differences, 14(1), 41-52. doi:10.1016/0191-8869(93)90173-zJorgensen, P. F. (1996). Affect, persuasion, and communication processes. Handbook of Communication and Emotion, 403-422. doi:10.1016/b978-012057770-5/50017-5Kilteni, K., Groten, R., & Slater, M. (2012). The Sense of Embodiment in Virtual Reality. Presence: Teleoperators and Virtual Environments, 21(4), 373-387. doi:10.1162/pres_a_00124Kivikangas, J. M., Chanel, G., Cowley, B., Ekman, I., Salminen, M., Järvelä, S., & Ravaja, N. (2011). A review of the use of psychophysiological methods in game research. Journal of Gaming & Virtual Worlds, 3(3), 181-199. doi:10.1386/jgvw.3.3.181_1Thevenot, C., & Oakhill, J. (2008). A generalization of the representational change theory from insight to non-insight problems: The case of arithmetic word problems. Acta Psychologica, 129(3), 315-324. doi:10.1016/j.actpsy.2008.08.008Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., … Brown, R. A. (2002). Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied, 8(2), 75-84. doi:10.1037/1076-898x.8.2.75Lindgren, R., & Johnson-Glenberg, M. (2013). Emboldened by Embodiment. Educational Researcher, 42(8), 445-452. doi:10.3102/0013189x13511661MacCrimmon, K. R., & Wehrung, D. A. (1990). Characteristics of Risk Taking Executives. Management Science, 36(4), 422-435. doi:10.1287/mnsc.36.4.422Manchester, D., Priestley, N., & Jackson, H. (2004). The assessment of executive functions: coming out of the office. Brain Injury, 18(11), 1067-1081. doi:10.1080/02699050410001672387Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., … Valenza, G. (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports, 8(1). doi:10.1038/s41598-018-32063-4Mohamed, S., Ali, T. H., & Tam, W. Y. V. (2009). National culture and safe work behaviour of construction workers in Pakistan. Safety Science, 47(1), 29-35. doi:10.1016/j.ssci.2008.01.003Motowildo, S. J., Borman, W. C., & Schmit, M. J. (1997). A Theory of Individual Differences in Task and Contextual Performance. Human Performance, 10(2), 71-83. doi:10.1207/s15327043hup1002_1Nicholson, N., Soane, E., Fenton‐O’Creevy, M., & Willman, P. (2005). Personality and domain‐specific risk taking. Journal of Risk Research, 8(2), 157-176. doi:10.1080/1366987032000123856Parker, S. K., Axtell, C. M., & Turner, N. (2001). Designing a safer workplace: Importance of job autonomy, communication quality, and supportive supervisors. Journal of Occupational Health Psychology, 6(3), 211-228. doi:10.1037/1076-8998.6.3.211Parsey, C. M., & Schmitter-Edgecombe, M. (2013). Applications of Technology in Neuropsychological Assessment. The Clinical Neuropsychologist, 27(8), 1328-1361. doi:10.1080/13854046.2013.834971Parsons, T. D. (2015). Virtual Reality for Enhanced Ecological Validity and Experimental Control in the Clinical, Affective and Social Neurosciences. Frontiers in Human Neuroscience, 9. doi:10.3389/fnhum.2015.00660Paulhus, 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-xPaulus, M. P., Rogalsky, C., Simmons, A., Feinstein, J. S., & Stein, M. B. (2003). Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. NeuroImage, 19(4), 1439-1448. doi:10.1016/s1053-8119(03)00251-9Piaget, J. (1952). The origins of intelligence in children. doi:10.1037/11494-000Robson, L. S., Stephenson, C. M., Schulte, P. A., Amick, B. C. I., Irvin, E. L., Eggerth, D. E., … Grubb, P. L. (2011). A systematic review of the effectiveness of occupational health and safety training. Scandinavian Journal of Work, Environment & Health, 38(3), 193-208. doi:10.5271/sjweh.3259Rundmo, T. (1996). Associations between risk perception and safety. Safety Science, 24(3), 197-209. doi:10.1016/s0925-7535(97)00038-6Schmitt, N. (1994). Method bias: The importance of theory and measurement. Journal of Organizational Behavior, 15(5), 393-398. doi:10.1002/job.4030150504Schoemaker, P. J. H. (1993). Determinants of risk-taking: Behavioral and economic views. Journal of Risk and Uncertainty, 6(1), 49-73. doi:10.1007/bf01065350Schroeter, R., Oxtoby, J., & Johnson, D. (2014). AR and Gamification Concepts to Reduce Driver Boredom and Risk Taking Behaviours. Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI ’14. doi:10.1145/2667317.2667415Seo, H.-C., Lee, Y.-S., Kim, J.-J., & Jee, N.-Y. (2015). Analyzing safety behaviors of temporary construction workers using structural equation modeling. Safety Science, 77, 160-168. doi:10.1016/j.ssci.2015.03.010Sequeira, H., Hot, P., Silvert, L., & Delplanque, S. (2009). Electrical autonomic correlates of emotion. International Journal of Psychophysiology, 71(1), 50-56. doi:10.1016/j.ijpsycho.2008.07.009SITKIN, S. B., & WEINGART, L. R. (1995). DETERMINANTS OF RISKY DECISION-MAKING BEHAVIOR: A TEST OF THE MEDIATING ROLE OF RISK PERCEPTIONS AND PROPENSITY. Academy of Management Journal, 38(6), 1573-1592. doi:10.2307/256844Skeel, R. L., Neudecker, J., Pilarski, C., & Pytlak, K. (2007). The utility of personality variables and behaviorally-based measures in the prediction of risk-taking behavior. Personality and Individual Differences, 43(1), 203-214. doi:10.1016/j.paid.2006.11.025Slater, 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.2009.0138Sneddon, A., Mearns, K., & Flin, R. (2013). Stress, fatigue, situation awareness and safety in offshore drilling crews. Safety Science, 56, 80-88. doi:10.1016/j.ssci.2012.05.027Soler, J. L., Contero, M., & Alcañiz, M. (2017). VR Serious Game Design Based on Embodied Cognition Theory. Lecture Notes in Computer Science, 12-21. doi:10.1007/978-3-319-70111-0_2Stanford, M. S., Greve, K. W., Boudreaux, J. K., Mathias, C. W., & L. Brumbelow, J. (1996). Impulsiveness and risk-taking behavior: comparison of high-school and college students using the Barratt Impulsiveness Scale. Personality and Individual Differences, 21(6), 1073-1075. doi:10.1016/s0191-8869(96)00151-1STEINBERG, L. (2004). Risk Taking in Adolescence: What Changes, and Why? Annals of the New York Academy of Sciences, 1021(1), 51-58. doi:10.1196/annals.1308.005Tsujii, T., & Watanabe, S. (2010). Neural correlates of belief-bias reasoning under time pressure: A near-infrared spectroscopy study. NeuroImage, 50(3), 1320-1326. doi:10.1016/j.neuroimage.2010.01.026Wang, L., Shute, V., & Moore, G. R. (2015). Lessons Learned and Best Practices of Stealth Assessment. International Journal of Gaming and Computer-Mediated Simulations, 7(4), 66-87. doi:10.4018/ijgcms.2015100104White, T. L., Lejuez, C. W., & de Wit, H. (2008). Test-retest characteristics of the Balloon Analogue Risk Task (BART). Experimental and Clinical Psychopharmacology, 16(6), 565-570. doi:10.1037/a0014083Wills, T. A., Walker, C., Mendoza, D., & Ainette, M. G. (2006). Behavioral and emotional self-control: Relations to substance use in samples of middle and high school students. Psychology of Addictive Behaviors, 20(3), 265-278. doi:10.1037/0893-164x.20.3.265Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625-636. doi:10.3758/bf03196322Yuen, K. S. ., & Lee, T. M. . (2003). Could mood state affect risk-taking decisions? Journal of Affective Disorders, 75(1), 11-18. doi:10.1016/s0165-0327(02)00022-

Studien zur romantischen Psychologie der Musik, besonders mit Rücksicht auf die Schriften E. T. A. Hoffmanns

Thesis (doctoral)--Rheinische Friedrich-Wilhelms-Universitat, Bonn.Mode of access: Internet

Needs for an Integration of Specific Data Sources and Items - First Insights of a National Survey Within the German Center for Infection Research.

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Stakeholder dynamics in residential solar energy adoption: findings from focus group discussions in Germany

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