Using Serious Games to Train Adaptive Emotional Regulation Strategies

[EN] Emotional Regulation (ER) strategies allow people to influence the emotions they feel, when they feel them, how they experience them, and how they express them in any situation. Deficiencies or deficits in ER strategies during the adolescence may become mental health problems in the future. The aim of this paper is to describe a virtual multiplatform system based on serious games that allows adolescents to train and evaluate their ER strategies. The system includes an ecological momentary assessment (EMA) tool, which allows the therapist to monitor the emotional status of teenagers every day in real time. Results obtained from a usability and effectiveness study about the EMA tool showed that adolescents preferred using the EMA tool than other classical instruments.This study was funded by Vicerrectorado de Investigación de la Universitat Politècnica de València, Spain, PAID-06-2011, R.N. 1984; by Ministerio de Educación y Ciencia, Spain, Project Game Teen (TIN2010-20187) and partially by projects Consolider-C (SEJ2006-14301/PSIC), “CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII” and Excellence Research Program PROMETEO (Generalitat Valenciana. Consellería de Educación, 2008-157). The work of Alejandro Rodríguez was supported by the Spanish MEC under an FPI Grant BES-2011-043316.Alcañiz Raya, ML.; Rodríguez Ortega, A.; Rey, B.; Parra Vargas, E. (2014). Using Serious Games to Train Adaptive Emotional Regulation Strategies. Lecture Notes in Computer Science. 8531:541-549. https://doi.org/10.1007/978-3-319-07632-4_51S5415498531Mennin, D., Farach, F.: Emotion and evolving treatments for adult psychopathology. Clinical Psychology: Science and Practice 14, 329–352 (2007)Serrano, A., Iborra, I.: Informe violencia entre compañeros en la escuela. Spanish Version (2005), http://www.centroreinasofia.esInforme Cisneros X.: Acoso y Violencia Escolar en España, por Iñaki Piñuel y Araceli Oñate. Editorial IIEDDI, Spanish Version (2007)Werner, K., Gross, J.J.: Emotion Regulation and Psychopathology. In: Emotion Regulation and Psychopathology: A Transdiagnostic Approach to Etiology and Treatment. Guildford Press (2010)Berking, M., Wupperman, P., Reichardt, A., Pejic, T., Dippel, A., Znoj, H.: Emotion-regulation skills as a treatment target in psychotherapy. Behaviour Research and Therapy 46, 1230–1237 (2008)Shields, A., Cicchetti, D.: Emotion regulation among school-age children: The development and validation of a new criterion Q-sort scale. Developmental Psychology 33(6), 906–916 (1997)Gross, J.J., John, O.P.: Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology 85(2), 348–362 (2003)Gross, J.J., Levenson, R.W.: Hiding feelings: The acute effects of inhibiting negative and positive emotion. Journal of Abnormal Psychology 106, 95–103 (1997)Winn, et al.: The Effect of Student Construction of Virtual Environments on the Performance of High- and Low-Ability Students. Annual Meeting of the American Educational Research Association (2003)Pantelidis, V.: Reasons to use virtual reality in education. VR in the Schools 1(1) (1995)Playmancer, http://www.playmancer.euBen Moussa, M., Magnenat-Thalmann, N.: Applying affect recognition in serious games: The playMancer project. In: Egges, A., Geraerts, R., Overmars, M. (eds.) MIG 2009. LNCS, vol. 5884, pp. 53–62. Springer, Heidelberg (2009)Replay, http://www.replayproject.euFeldman, L.B., Gross, J.J., Conner, T., Benvenuto, M.: Knowing what you’re feeling and knowing what to do about it: mapping the relation between emotion differentiation and emotion regulation. Cognition and Emotion 15, 713–724 (2001

Analysis of PBPK models for risk characterization

International audienceAdoption of a Bayesian framework for risk characterization permits the seamless integration of different kinds of information available in order to choose and parameterize risk models. It also becomes easy to disentangle uncertainty from variability, through hierarchical statistical modeling. Appropriate numerical techniques can be found, for example, in the recently developed arsenal of Markov chain, Monte Carlo simulations. The developments in this area can actually be viewed as extensions of the traditional or standard Monte Carlo methods for uncertainty analysis. Following a brief review of the techniques, examples of Bayesian analyses of physiologically-based pharmacokinetic models are presented for tetrachloroethylene and dichloromethane. The discussion touches on some open problems and perspectives for the proposed methods