Implicit Responses for Assessment of Autism Based on Natural Behaviors Obtained Inside Immersive Virtual Environment
The late detection and subjectivity of the assessment of Autism Spectrum Disorder (ASD) imposed a difficulty for the children’s clinical and familiar environment. The results showed in this paper, are part of a research project about the assessment and training of social skills in children with ASD, whose overall goal is the use of virtual environments together with physiological measures in order to find a new model of objective ASD assessment based on implicit brain processes measures. In particular, this work tries to contribute by studying the differences and changes in the Skin Conductance Response (SCR) and Eye Tracking (ET) between a typical development group (TD group) and an ASD group (ASD group) after several combined stimuli using a low cost Immersive Virtual Environment (IVE). Subjects were exposed to a virtual environment that showed natural scenes that stimulated visual, auditory and olfactory perceptual system. By exposing them to the IVE, subjects showed natural behaviors while measuring SCR and ET. This study compared measures of subjects diagnosed with ASD (N = 18) with a control group of subjects with typical development (N=10) when exposed to three different conditions: only visual (V), visual and auditory (VA) and visual, auditory and olfactory (VAO) stimulation. Correlations between SCR and ET measures were also correlated with the Autism Diagnostic Observation Schedule (ADOS) test. SCR measures showed significant differences among the experimental condition between groups. The ASD group presented higher level of SCR while we did not find significant differences between groups regarding DF. We found high significant correlations among all the experimental conditions in SCR measures and the subscale of ADOS test of imagination and symbolic thinking. Regarding the correlation between ET measures and ADOS test, the results showed significant relationship between VA condition and communication scores.
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 C. Hutt, C., Hutt, S. J., Lee, D., & Ounsted, “Arousal and Childhood Autism,” Nature, vol. 204, no. 4961, pp. 908–909, 1964.
 S. Baron-Cohen, “Autism: a specific cognitive disorder of ‘mind-blindness,’” Int. Rev. Psychiatry, vol. 2, no. October, pp. 81–90, 1990.
 L. Wing, “The autistic spectrum,” vol. 350, pp. 1761–1766, 1997.
 American Psychiatric Association., The principles of medical ethics: With annotations especially applicable to psychiatry. 2001.
 M. Elsabbagh et al., “Global Prevalence of Autism and Other Pervasive Developmental Disorders,” no. April, pp. 160–179, 2012.
 M. Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C. & Rutter, “The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism.,” J. Autism Dev. Disord., vol. 30, no. 3, p. 205, 2000.
 R. S. Wyer, “The automaticity of everyday life: Advances in social cognition,” Psychol. Press, vol. 10, 2014.
 Lieberman, M. D, “Social cognitive neuroscience.,” in Handbook of social psychology, 2010.
 A. P. Brief, “Attitudes in and around organizations.” 1998.
 W. J. Becker, R. Cropanzano, and A. G. Sanfey, “Organizational Neuroscience: Taking Organizational Theory Inside the Neural Black Box,” J. Manage., vol. 37, no. 4, pp. 933–961, 2011.
 K. Ledoux, E. Coderre, L. Bosley, E. Buz, I. Gangopadhyay, and B. Gordon, “The concurrent use of three implicit measures (eye movements, pupillometry, and event-related potentials) to assess receptive vocabulary knowledge in normal adults,” Behav. Res. Methods, vol. 48, no. 1, pp. 285–305, 2016.
 B. Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Deen, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.,” Mol. Psychiatry, vol. 19, no. 6, pp. 659–667, 2014.
 K. Yanagisawa, N. Nakamura, H. Tsunashima, and N. Narita, “Proposal of auxiliary diagnosis index for autism spectrum disorder using near-infrared spectroscopy,” Neurophotonics, vol. 3, no. 3, p. 31413, 2016.
 R. M. Fenning, J. K. Baker, B. R. Baucom, S. A. Erath, M. A. Howland, and J. Moffitt, “Electrodermal Variability and Symptom Severity in Children with Autism Spectrum Disorder,” J. Autism Dev. Disord., vol. 47, no. 4, pp. 1062–1072, 2017.
 A. V. Van Hecke et al., “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,” J. Autism Dev. Disord., vol. 45, no. 2, pp. 316–335, 2013.
 Q. Guillon, N. Hadjikhani, S. Baduel, and B. Rogé, “Visual social attention in autism spectrum disorder: Insights from eye tracking studies,” Neurosci. Biobehav. Rev., vol. 42, pp. 279–297, 2014.
 Y. Wang, M. K. Hensley, A. Tasman, L. Sears, M. F. Casanova, and E. M. Sokhadze, “Heart Rate Variability and Skin Conductance During Repetitive TMS Course in Children with Autism,” Appl. Psychophysiol. Biofeedback, vol. 41, no. 1, pp. 47–60, 2016.
 I. A. C. Giglioli, G. Pravettoni, D. L. S. Martín, E. Parra, and M. A. Raya, “A novel integrating virtual reality approach for the assessment of the attachment behavioral system,” Front. Psychol., vol. 8, no. JUN, pp. 1–7, 2017.
 O. I. Lovaas, R. L. Koegel, and L. Schreibman, “Stimulus overselectivity in autism: A review of research,” Psychol. Bull., vol. 86, no. 6, pp. 1236–1254, 1979.
 K. Francis, “Autism interventions: A critical update,” Dev. Med. Child Neurol., vol. 47, no. 7, pp. 493–499, 2005.
 C. Gillberg and P. Rasmussen, “Brief report: four case histories and a literature review of Williams syndrome and autistic behavior,” J Autism Dev Disord, vol. 24, no. 3, pp. 381–393, 1994.
 J. M. Loomis, J. J. Blascovich, and A. C. Beall, “Immersive virtual environment technology as a basic research tool in psychology,” Behav. Res. Methods, Instruments, Comput., vol. 31, no. 4, pp. 557–564, 1999.
 M. Alcañiz, R. Baños, C. Botella, and B. Rey, “The emma project: Emotions as a determinant of presence,” PsychNology J., vol. 1, no. 2, pp. 141–150, 2003.
 M. Slater, B. Lotto, M. M. Arnold, and M. V. Sanchez-Vives, “How we experience immersive virtual environments: The concept of presence and its measurement,” Anu. Psicol., vol. 40, no. 2, pp. 193–210, 2009.
 S. Parsons, “Learning to work together: Designing a multi-user virtual reality game for social collaboration and perspective-taking for children with autism,” Int. J. Child-Computer Interact., 2015.
 C. J. Bohil, B. Alicea, and F. A. Biocca, “Virtual reality in neuroscience research and therapy,” Nat. Rev. Neurosci., vol. 12, no. 12, 2011.
 S. Parsons and L. Beardon, “Development of social skills amongst adults with Asperger’s Syndrome using virtual environments: the ‘AS Interactive’project,” … Disabil. Virtual …, pp. 163–170, 2000.
 D. Strickland, “Virtual reality for the treatment of autism,” Stud. Health Technol. Inform., vol. 44, pp. 81–86, 1997.
 A. Parsons, S., Mitchell, P., & Leonard, “The Use and Understanding of Virtual Environments by Adolescents with Autistic Spectrum Disorders,” Journal Autism Dev. Disord., vol. 34, no. 4, pp. 449–466, 2004.
 M. R. Kandalaft, N. Didehbani, D. C. Krawczyk, T. T. Allen, and S. B. Chapman, “Virtual Reality Social Cognition Training for Young Adults with High-Functioning Autism,” J. Autism Dev. Disord., vol. 43, no. 1, pp. 34–44, 2013.
 P. Fomby and A. J. Cherlin, “Assessing the Utility of a Virtual Environment for Enhancing Facial Affect Recognition in Adolescents with Autism,” J Autism Dev Disord, vol. 72, no. 2, pp. 181–204, 2014.
 S. Sharples, S. Cobb, A. Moody, and J. R. Wilson, “Virtual reality induced symptoms and effects (VRISE): Comparison of head mounted display (HMD), desktop and projection display systems,” Displays, vol. 29, no. 2, pp. 58–69, 2008.
 M. Liss, M., Saulnier, C., Fein, D., & Kinsbourne, “Sensory and attention abnormalities in autism spectrum disorders,” Autism, vol. 10, no. 2, pp. 155–172, 2006.
 Y. Cai, N. K. H. Chia, D. Thalmann, N. K. N. Kee, J. Zheng, and N. M. Thalmann, “Design and development of a Virtual Dolphinarium for children with autism.,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 2, pp. 208–17, 2013.
 G. Perhakaran et al., “SnoezelenCAVE: Virtual Reality CAVE Snoezelen Framework for Autism Spectrum Disorders,” in Advances in Visual Informatics, vol. 9429, 2015, pp. 443–453.
 W. Boucsein, “Principles of Electrodermal Phenomena,” in Springer Science & Business Media., 2012.
 D. S. Annis, D. F. Mosher, and D. D. Roberts, “Atypical Modulation of Cognitive Control by Arousal in Autism,” Psychiatry Res. Neuroimaging, vol. 164, no. 3, pp. 185–197, 2008.
 E. Hedman, O. Wilder-Smith, M. S. Goodwin, M. Z. Poh, R. Fletcher, and R. Picard, “iCalm: Measuring electrodermal activity in almost any setting,” Proc. - 2009 3rd Int. Conf. Affect. Comput. Intell. Interact. Work. ACII 2009, pp. 2–3, 2009.
 M. J. Christie, “Electrodermal activity in the 1980s: a review.,” J. R. Soc. Med., vol. 74, no. April, pp. 616–622, 1981.
 H. Sequeira, P. Hot, L. Silvert, and S. Delplanque, “Electrical autonomic correlates of emotion,” Int. J. Psychophysiol., vol. 71, no. 1, pp. 50–56, 2009.
 L. Rozenkrantz et al., “A Mechanistic Link between Olfaction and Autism Spectrum Disorder,” Curr. Biol., vol. 25, no. 14, pp. 1904–1910, 2015.
 M. Larsson, C. Tirado, and S. Wiens, “A Meta-Analysis of Odor Thresholds and Odor Identification in Autism Spectrum Disorders,” Front. Psychol., vol. 8, no. May, pp. 1–9, 2017.
 B. Wicker, E. Monfardini, and J.-P. Royet, “Olfactory processing in adults with autism spectrum disorders,” Mol. Autism, vol. 7, no. 1, p. 4, 2016.
 A. Klin, W. Jones, R. Schultz, F. Volkmar, and D. Cohen, “Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism.,” Arch. Gen. Psychiatry, vol. 59, no. 9, pp. 809–816, 2002.
 M. Chita-Tegmark, “Attention Allocation in ASD: A Review and Meta-analysis of Eye-Tracking Studies,” Rev. J. Autism Dev. Disord., vol. 3, no. 3, pp. 209–223, 2016.
 E. Bal, E. Harden, D. Lamb, A. V. Van Hecke, J. W. Denver, and S. W. Porges, “Emotion recognition in children with autism spectrum disorders: Relations to eye gaze and autonomic state,” J. Autism Dev. Disord., vol. 40, no. 3, pp. 358–370, 2010.
 J. R. Irwin and L. Brancazio, “Seeing to hear? Patterns of gaze to speaking faces in children with autism spectrum disorders,” Front. Psychol., vol. 5, no. MAY, pp. 1–10, 2014.
 M. A. Just and P. A. Carpenter, “Eye fixations and cognitive processes,” Cogn. Psychol., vol. 8, no. 4, pp. 441–480, 1976.
 J. A. Osterling, G. Dawson, and J. A. Munson, “Early recognition of 1-year-old infants with autism spectrum disorder versus mental retardation,” Dev. Psychopathol., vol. 14, no. 2, pp. 239–251, 2002.
 R. J. Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S., Gernsbacher, M. A., Goldsmith, H. & Davidson, “Gaze fixation and the neural circuitry of face processing in autism.,” vol. 8, no. 4, pp. 519–526, 2005.
 D. Al-Omar, A. Al-Wabil, and M. Fawzi, “Using pupil size variation during visual emotional stimulation in measuring affective states of non communicative individuals,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8010 LNCS, no. PART 2, pp. 253–258, 2013.
 N. Bekele, E., Zheng, Z., Swanson, A., Crittendon, J., Warren, Z., & Sarkar, “Understanding How Adolescents with Autism Respond to Facial Expressions in Virtual Reality Environments,” IEEE Trans. Vis. Comput. Graph., vol. 19, no. 4, pp. 711–720, 2013.
 Olorama Technology. (2018, August 22). Retrieved from http://www.olorama.com.
 Empatica. (2018, August 22). Retrieved from http://www.empatica.com.
 Ledalab. (2018, August 22). Retrieved from http://www.ledalab.de.
 Matlab-MathWorks. (2018, August 22). Retrieved from https://www.mathworks.com/products/matlab.html?s_tid=hp_products_matlab
 E. P. Valenza, G., & Scilingo, Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition. Significant Advances in Data Acquisition, Signal Processing and Classification. New York, USA, 2014.
 M. Benedek and C. Kaernbach, “A continuous measure of phasic electrodermal activity,” J. Neurosci. Methods, vol. 190, no. 1, pp. 80–91, 2010.
 M. J. Venables, P.H., Christie, “Electrodermal activity,” in Techniques in Psychophysiology, Wiley & So., P. H. Martin, I., Venables, Ed. New York, USA, 1980, pp. 3–67.
 Tobii Pro Glasses 2. (2018, August 22). Retrieved from https://www.tobiipro.com/product-listing/tobii-pro-glasses-2/.
 IMOTIONS. (2018, August 22). Retrieved from https://imotions.com.
 W. Hirstein, P. Iversen, and V. S. Ramachandran, “Autonomic responses of autistic children to people and objects,” Proc. R. Soc. B Biol. Sci., vol. 268, no. 1479, pp. 1883–1888, 2001.
 M. C. Chang et al., “Autonomic and behavioral responses of children with autism to auditory stimuli.,” Am. J. Occup. Ther., vol. 66, no. 5, pp. 567–76, 2010.
 S. Stevens and J. Gruzelier, “Electrodermal activity to auditory stimuli in autistic, retarded, and normal children.,” J. Autism Dev. Disord., vol. 14, no. 3, pp. 245–260, 1984.
 A. Swanson, J. Crittendon, and Z. Warren, “Understanding How Adolescents with Autism Respond to Facial Expressions in Virtual Reality Environments,” vol. 19, no. 4, 2013.
 J. L. Barnes, “Fiction, imagination, and social cognition: Insights from autism,” Poetics, vol. 40, no. 4, pp. 299–316, 2012.