How priming with body odors affects decision speeds in consumer behavior

To date, odor research has primarily focused on the behavioral efects of common odors on consumer perception and choices. We report a study that examines, for the frst time, the efects of human body odor cues on consumer purchase behaviors. The infuence of human chemosignals produced in three conditions, namely happiness, fear, a relaxed condition (rest), and a control condition (no odor), were examined on willingness to pay (WTP) judgments across various products. We focused on the speed with which participants reached such decisions. The central fnding revealed that participants exposed to human odors reached decisions signifcantly faster than the no odor control group. The main driving force is that human body odors activate the presence of others during decision-making. This, in turn, afects response speed. The broader implications of this fnding for consumer behavior are discussed.Comunidade Europeia e Generalitat Valencianainfo:eu-repo/semantics/publishedVersio

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-

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

Assessment of the autism spectrum disorder based on machine learning and social visual attention: a systematic review

[EN] The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children¿s social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.The authors have no relevant financial or non-financial interests to disclose. 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). This work was also supported by the Spanish Ministry of Science and Innovation funded project "T-EYE: Monitoring system for children with ASD based on artificial intelligence and physiological measures" (IDI-20201146)Minissi, ME.; Chicchi-Giglioli, IA.; Mantovani, F.; Alcañiz Raya, ML. (2021). Assessment of the autism spectrum disorder based on machine learning and social visual attention: a systematic review. Journal of Autism and Developmental Disorders. 1-16. https://doi.org/10.1007/s10803-021-05106-5S116Alcañiz Raya, M., Chicchi Giglioli, I. A., Marín-Morales, J., Higuera-Trujillo, J. L., Olmos, E., Minissi, M. E., Teruel Garcia, G., Sirera, M., & 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. https://doi.org/10.3389/fnhum.2020.00090Alcañiz Raya, M., Giglioli, I. A. C., Sirera, M., Minissi, E., & Abad, L. (2020). Biomarcadores del trastorno del especto autista basados en bioseñales, realidad virtual e inteligencia artificial. Medicina (Buenos Aires), 80(supl II), 31–36.Alcañiz Raya, M., Marín-Morales, J., Minissi, M. E., Teruel Garcia, G., Abad, L., & Chicchi Giglioli, I. A. (2020). Machine learning and virtual reality on body movements’ behaviors to classify children with autism spectrum disorder. Journal of Clinical Medicine, 9(5), 1260.American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). . American Psychiatric Association.Bölte, S., Bartl-Pokorny, K. D., Jonsson, U., Berggren, S., Zhang, D., Kostrzewa, E., Falck-Ytter, T., Einspieler, C., Pokorny, F. B., Jones, E. J., Roeyers, H., Charman, T., & Marschik, P. B. (2016). How can clinicians detect and treat autism early? Methodological trends of technology use in research. Acta paediatrica, 105(2), 137–144.Carette, R., Cilia, F., Dequen, G., Bosche, J., Guerin, J. L., & Vandromme, L. (2017). Automatic autism spectrum disorder detection thanks to eye-tracking and neural network-based approach. International conference on IoT technologies for healthcare (pp. 75–81). Cham: Springer.Carette, R., Elbattah, M., Dequen, G., Guérin, J., Cilia, F., & Bosche, J. (2019). Learning to predict autism spectrum disorder based on the visual patterns of eye-tracking scanpaths. In HEALTHINF (pp. 103–112). Chaytor, N., Schmitter-Edgecombe, M., & Burr, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21(3), 217–227.Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social motivation theory of autism. Trends in Cognitive Sciences, 16(4), 231–239.Chita-Tegmark, M. (2016). Social attention in ASD: A review and meta-analysis of eye-tracking studies. Research in Developmental Disabilities, 48, 79–93.Choueiri, R. N., & Zimmerman, A. W. (2017). New assessments and treatments in ASD. Current Treatment Options in Neurology, 19(2), 6.Chuba, H., Paul, R., Klin, A., & Volkmar, F. (2003, November). Assessing pragmatic skills in individuals with autism spectrum disorders. In Presentation at the National Convention of the American Speech-Language-Hearing Association, Chicago, IL.Chumerin, N., & Van Hulle, M. M. (2006). Comparison of two feature extraction methods based on maximization of mutual information. 2006 16th IEEE signal processing society workshop on machine learning for signal processing (pp. 343–348). IEEE.Cilia, F., Aubry, A., Bourdin, B., & Vandromme, L. (2019). Comment déterminer les zones d’intérêt visuelles sans a priori? Analyse des fixations d’enfants autistes en oculométrie. Revue De Neuropsychologie, 11(2), 144–150.Cilia, F., Aubry, A., Le Driant, B., Bourdin, B., & Vandromme, L. (2019). Visual exploration of dynamic or static joint attention bids in children with autism syndrome disorder. Frontiers in psychology. https://doi.org/10.3389/fpsyg.2019.02187Constantino, J. N., & Gruber, C. P. (2005). Social responsiveness scale (SRS). Los Angeles: Western Psychological Services.Crippa, 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.Currenti, S. A. (2010). Understanding and determining the etiology of autism. Cellular and Molecular Neurobiology, 30(2), 161–171.Dawson, G., Hill, D., Spencer, A., Galpert, L., & Watson, L. (1990). Affective exchanges between young autistic children and their mothers. Journal of Abnormal Child Psychology, 18, 335–345.Dawson, G., Toth, K., Abbott, R., Osterling, J., Munson, J., Estes, A., & Liaw, J. (2004). Early social attention impairments in autism: Social orienting, joint attention, and attention to distress. Developmental Psychology, 40(2), 271.Dawson, G., Webb, S. J., & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Developmental Neuropsychology, 27(3), 403–424.Deng, Y., Manjunath, B. S., Kenney, C., Moore, M. S., & Shin, H. (2001). An efficient color representation for image retrieval. IEEE Transactions on Image Processing, 10(1), 140–147.De Bildt, A., Sytema, S., Ketelaars, C., Kraijer, D., Mulder, E., Volkmar, F., & Minderaa, R. (2004). Interrelationship between autism diagnostic observation schedule-generic (ADOS-G), autism diagnostic interview-revised (ADI-R), and the diagnostic and statistical manual of mental disorders (DSM-IV-TR) classification in children and adolescents with mental retardation. Journal of Autism and Developmental Disorders, 34(2), 129–137.Duan, H., Zhai, G., Min, X., Che, Z., Fang, Y., Yang, X., Gutiérrez, J., & Callet, P. L. (2019, June). A dataset of eye movements for the children with autism spectrum disorder. In Proceedings of the 10th ACM Multimedia Systems Conference (pp. 255–260).Elbattah, M., Carette, R., Dequen, G., Guérin, J. L., & Cilia, F. (2019). Learning clusters in autism spectrum disorder: image-based clustering of eye-tracking scanpaths with deep autoencoder. 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 1417–1420). IEEE.Forscher, 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. https://doi.org/10.1037/pspa0000160.Franzen, M. D., & Wilhelm, K. L. (1996). Conceptual foundations of ecological validity in neuropsychological assessment. In R. J. Sbordone & C. J. Long (Eds.), Ecological validity of neuropsychological testing (pp. 91–112). Gr Press/St Lucie Press Inc.Frazier, T. W., Strauss, M., Klingemier, E. W., Zetzer, E. E., Hardan, A. Y., Eng, C., & Youngstrom, E. A. (2017). A meta-analysis of gaze differences to social and nonsocial information between individuals with and without autism. Journal of the American Academy of Child & Adolescent Psychiatry, 56(7), 546–555.Ghaziuddin, M., & Gerstein, L. (1996). Pedantic speaking style differentiates asperger syndrome from high-functioning autism. Journal of Autism and Developmental Disorders, 26(6), 585–595.Goldberg, J. H., & Helfman, J. I. (2010). Visual scanpath representation. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications (pp. 203–210).Goldstein, S., & Ozonoff, S. (Eds.). (2018). Assessment of autism spectrum disorder. Guilford Publications.Ismail, M. M., Keynton, R. S., Mostapha, M. M., ElTanboly, A. H., Casanova, M. F., Gimel’farb, G. L., & El-Baz, A. (2016). Studying autism spectrum disorder with structural and diffusion magnetic resonance imaging: A survey. Frontiers in Human Neuroscience, 10, 211.He, Y., Su, Q., Wang, L., He, W., Tan, C., Zhang, H., Ng, M. L., Yan, N., & Chen, Y. (2019). The characteristics of intelligence profile and eye gaze in facial emotion recognition in mild and moderate preschoolers with autism spectrum disorder. Frontiers in psychiatry, 10, 402.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.Hyde, 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.Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448–456). PMLR.    Jiang, M., & Zhao, Q. (2017). Learning visual attention to identify people with autism spectrum disorder. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3267–3276).Kamp-Becker, I., Albertowski, K., Becker, J., Ghahreman, M., Langmann, A., Mingebach, T., Poustka, L., Weber, L., Schmidt, H., Smidt, J., Stehr, T., Roessner, V., Kucharczyk, K., Wolff, N., & Stroth, S. (2018). Diagnostic accuracy of the ADOS and ADOS-2 in clinical practice. European Child & Adolescent Psychiatry, 27(9), 1193–1207.Kang, J., Han, X., Song, J., Niu, Z., & Li, X. (2020). The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data. Computers in Biology and Medicine, 120, 103722. https://doi.org/10.1016/j.compbiomed.2020.103722Kasari, C., Sigman, M., & Yirmiya, N. (1993). Focused and social attention of autistic children in interactions with familiar and unfamiliar adults: A comparison of autistic, mentally retarded, and normal children. Development and Psychopathology, 5, 403–414.Klin, A. (2018). Biomarkers in autism spectrum disorder: challenges, advances, and the need for biomarkers of relevance to public health. Focus, 16(2), 135–142.Klin, A., & Mercadante, M. T. (2006). Autism and the pervasive developmental disorders. Revista Brasileira De Psiquiatria, 28(Suppl. 1), s1–s2. https://doi.org/10.1590/S1516-44462006000500001Koirala, A., Yu, Z., Schiltz, H., Van Hecke, A., Koth, K. A., & Zheng, Z. (2019, June). An exploration of using virtual reality to assess the sensory abnormalities in children with autism spectrum disorder. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (pp. 293–300).Le Couteur, A., Haden, G., Hammal, D., & McConachie, H. (2008). Diagnosing autism spectrum disorders in pre-school children using two standardised assessment instruments: the ADI-R and the ADOS. Journal of Autism and Developmental Disorders, 38(2), 362–372.Li, J., Zhong, Y., Han, J., Ouyang, G., Li, X., & Liu, H. (2020). Classifying ASD children with LSTM based on raw videos. Neurocomputing, 390, 226–238.Li, J., Zhong, Y., & Ouyang, G. (2018). Identification of ASD children based on video data. 2018 24th International conference on pattern recognition (ICPR) (pp. 367–372). IEEE.Lieberman, M. D. (2010). Social cognitive neuroscience.Liu, 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.Liu, W., Yu, X., Raj, B., Yi, L., Zou, X., & Li, M. (2015). Efficient autism spectrum disorder prediction with eye movement: A machine learning framework. 2015 International conference on affective computing and intelligent interaction (ACII) (pp. 649–655). IEEE.Lord, 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), 694–701.Lord, 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, 659–685. https://doi.org/10.1007/bf02172145Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. A. (1999). Diagnostic observation schedule-WPS (ADOS-WPS). Los Angeles: Western Psychological Services.Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (2001). Autism diagnostic observation schedule. Los Angeles: Western Psychological Services.Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2579–2605.Matlis, S., Boric, K., Chu, C. J., & Kramer, M. A. (2015). Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism. BMC Neurology, 15(1), 97.Mello, R. F., & Ponti, M. A. (2018). Machine learning: A practical approach on the statistical learning theory. Springer.Mitchell, T. M. (1997). Machine learning.Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Linee guida per il reporting di revisioni sistematiche e meta-analisi: il PRISMA Statement. PLoS Med, 6(7), e1000097.Mundy, P., Sigman, M., Ungerer, J., & Sherman, T. (1986). Defining the social deficits of autism: The contribution of non-verbal communication measures. Journal of Child Psychology and Psychiatry, 27(5), 657–669.Naber, F. B., Bakermans-Kranenburg, M. J., van Ijzendoorn, M. H., Dietz, C., van Daalen, E., Swinkels, S. H., Buitelaar, J. K., & van Engeland, H. (2008). Joint attention development in toddlers with autism. European Child & Adolescent Psychiatry, 17(3), 143–152.Nguyen, G. H., Bouzerdoum, A., & Phung, S. L. (2009). Learning pattern classification tasks with imbalanced data sets. Pattern recognition, 193–208.Nosek, B. A., Hawkins, C. B., & Frazier, R. S. (2011). Implicit social cognition: From measures to mechanisms. Trends in cognitive sciences, 15(4), 152–159.Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine learning in psychometrics and psychological research. Frontiers in Psychology, 10, 2970.Pan, J., Ferrer, C. C., McGuinness, K., O’Connor, N. E., Torres, J., Sayrol, E., & Giro-i-Nieto, X. (2017). Salgan: Visual saliency prediction with generative adversarial networks. ArXiv preprint arXiv1701.01081.Parsons, S. (2016). Authenticity in Virtual Reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138–157.Parsons, T. D. (2016). Clinical neuropsychology and technology. Cham: Springer International Publishing.Paulhus, D. L. (1991). Measurement and control of response bias. Elsevier.Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.Reaven, 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.Rutter, M., Bailey, A., & Lord, C. (2003). SCQ. The Social Communication Questionnaire. Western Psychological Services.Shmueli, G. (2010). To explain or to predict? Statistical Science, 25, 289–310.Schopler, E., Reichler, R. J., DeVellis, R. F., & Daly, K. (1980). Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). Journal of Autism and Developmental Disorders. https://doi.org/10.1007/BF02408436Sterling, L., Dawson, G., Webb, S., Murias, M., Munson, J., Panagiotides, H., & Aylward, E. (2008). The role of face familiarity in eye tracking of faces by individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(9), 1666–1675.Strimbu, K., & Tavel, J. A. (2010). What are biomarkers? Current Opinion in HIV and AIDS, 5(6), 463.Swettenham, J., Baron-Cohen, S., Charman, T., Cox, A., Baird, G., Drew, A., et al. (1998). The frequency and distribution of spontaneous attention shifts between social and nonsocial stimuli in autistic, typically developing, and nonautistic developmentally delayed infants. Journal of Child Psychology and Psychiatry, 39, 747–753.Tager-Flusberg, H., Paul, R., & Lord, C. (2005). Language and communication in autism. Handbook of Autism and Pervasive Developmental Disorders, 1, 335–364.Tanaka, J. W., & Sung, A. (2016). The “eye avoidance” hypothesis of autism face processing. Journal of Autism and Developmental Disorders, 46(5), 1538–1552.Tao, Y., & Shyu, M. L. (2019). SP-ASDNet: CNN-LSTM based ASD classification model using observer scanpaths. 2019 IEEE International conference on multimedia & expo workshops (ICMEW) (pp. 641–646). IEEE.Thabtah, F. (2019). Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Informatics for Health and Social Care, 44(3), 278–297.Torii, I., Ohtani, K., & Ishii, N. (2016). Measurement of ocular movement abnormality in pursuit eye movement (PEM) of autism spectrum children with disability. 2016 4th Intl conf on applied computing and information technology/3rd intl conf on computational science/intelligence and applied informatics/1st intl conf on big data, cloud computing, data science & engineering (ACIT-CSII-BCD) (pp. 235–240). IEEE.Vu, T., Tran, H., Cho, K. W., Song, C., Lin, F., Chen, C. W., Hartley-McAndrew, M., Doody, K. R., & Xu, W. (2017). Effective and efficient visual stimuli design for quantitative autism screening: An exploratory study. 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 297–300). IEEE.Wallace, S., Parsons, S., & Bailey, A. (2017). Self-reported sense of presence and responses to social stimuli by adolescents with ASD in a collaborative virtual reality environment. Journal of Intellectual & Developmental Disability, 42(2), 131–141.Wallace, S., Parsons, S., Westbury, A., White, K., White, K., & Bailey, A. (2010). Sense of presence and atypical social judgments in immersive virtual environments: Responses of adolescents with Autism Spectrum Disorders. Autism, 14(3), 199–213.Walsh, P., Elsabbagh, M., Bolton, P., & Singh, I. (2011). In search of biomarkers for autism: Scientific, social and ethical challenges. Nature Reviews Neuroscience, 12(10), 603–612.Wan, G., Kong, X., Sun, B., Yu, S., Tu, Y., Park, J., Lang, C., Koh, M., Wei, Z., Feng, Z., Lin, Y., & Kong, J. (2019). Applying eye tracking to identify autism spectrum disorder in children. Journal of Autism and Developmental Disorders, 49(1), 209–215.Wilkinson, K. M. (1998). Profiles of language and communication skills in autism. Mental Retardation and Developmental Disabilities Research Reviews, 4(2), 73–79.Wolfers, T., Floris, D. L., Dinga, R., van Rooij, D., Isakoglou, C., Kia, S. M., Zabihi, M., Llera, A., Chowdanayaka, R., Kumar, V. J., Peng, H., Laidi, C., Batalle, D., Dimitrova, R., Charman, T., Loth, E., Lai, M. C., Jones, E., Baumeister, S., … Beckmann, C. F. (2019). From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neuroscience & Biobehavioral Reviews, 104, 240–254.World Health Organization [WHO]. (2019). Autism spectrum disorders. Available at: https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders (Visited on April 1, 2021).Wu, D., José, J. V., Nurnberger, J. I., & Torres, E. B. (2018). A biomarker characterizing neurodevelopment with applications in autism. Scientific Reports, 8(1), 1–14.Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.Yi, L., Feng, C., Quinn, P. C., Ding, H., Li, J., Liu, Y., & Lee, K. (2014). Do individuals with and without autism spectrum disorder scan faces differently? A new multi-method look at an existing controversy. Autism Research, 7(1), 72–83

Biomarcadores del trastorno del espectro autista basados en bioseñales, realidad virtual e inteligencia artificial

[ES] Se ha observado que la estratificación de trastornos del espectro autista (TEA) generada por las escalas actuales no es efectiva para la personalización de tratamientos tempranos. La evaluación clínica de TEA requiere su consideración como un continuo de déficits, y existe la necesidad de identificar parámetros biológicamente significativos (biomarcadores) que tengan el poder de caracterizar automáticamente a cada individuo en diferentes etapas del desarrollo neurológico. El incipiente campo de la psiquiatría computacional (CP) intenta satisfacer las necesidades de diagnóstico de precisión mediante el desarrollo de potentes técnicas computacionales y matemáticas. Una creciente actividad científica propone el uso de medidas implícitas basadas en bioseñales para la clasificación de ASD. Las tecnologías de realidad virtual (VR) han demostrado potencial para las intervenciones de TEA, pero la mayoría de los trabajos han utilizado la realidad virtual para el aprendizaje / objetivo de las intervenciones. Muy pocos estudios han utilizado señales biológicas para el registro y el análisis detallado de las respuestas conductuales que se pueden utilizar para monitorear o producir cambios a lo largo del tiempo. En el presente trabajo se introduce el concepto de biomarcadores conductuales basados en VR o VRBB. Los VRBB van a permitir la clasificación de TEA utilizando un paradigma de psiquiatría computacional basado en procesos cerebrales implícitos medidos a través de señales psicofisiológicas y el comportamiento de sujetos expuestos a complejas réplicas de condiciones sociales utilizando interfaces de realidad virtual.[EN] It has been observed that the stratification of Autism Spectrum Disorders (ASD) generated by the current scales is not effective for the personalization of early treatments. The clinical evaluation of ASD requires its consideration as a continuum of deficits, and there is a need to identify biologically significant parameters (biomarkers) that have the power to automatically characterize each individual at different stages of neurological development. The emerging field of computational psychiatry (CP) attempts to meet the needs of precision diagnosis by developing powerful computational and mathematical techniques. A growing scientific activity proposes the use of implicit measures based on biosignals for the classification of ASD. Virtual reality (VR) technologies have demonstrated potential for ASD interventions, but most of the work has used virtual reality for the learning / objective of interventions. Very few studies have used biological signals for recording and detailed analysis of behavioral responses that can be used to monitor or produce changes over time. In this paper the concept of behavioral biomarkers based on VR or VRBB is introduced. VRBB will allow the classification of ASD using a paradigm of computational psychiatry based on implicit brain processes measured through psychophysiological signals and the behavior of subjects exposed to complex replicas of social conditions using virtual reality interfaces.Alcañiz Raya, ML.; Chicchi-Giglioli, IA.; Sirera, M.; Minissi, ME.; Abad, L. (2020). Biomarcadores del trastorno del espectro autista basados en bioseñales, realidad virtual e inteligencia artificial. Medicina (Buenos Aires) (Online). 80(supl II):10-17. http://hdl.handle.net/10251/171695S101780supl I

How priming with body odors affects decision speeds in consumer behavior

Abstract To date, odor research has primarily focused on the behavioral effects of common odors on consumer perception and choices. We report a study that examines, for the first time, the effects of human body odor cues on consumer purchase behaviors. The influence of human chemosignals produced in three conditions, namely happiness, fear, a relaxed condition (rest), and a control condition (no odor), were examined on willingness to pay (WTP) judgments across various products. We focused on the speed with which participants reached such decisions. The central finding revealed that participants exposed to human odors reached decisions significantly faster than the no odor control group. The main driving force is that human body odors activate the presence of others during decision-making. This, in turn, affects response speed. The broader implications of this finding for consumer behavior are discussed

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis

[EN] The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations,advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults¿ rather than children¿s faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition.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, Grant/Award Number: IDI20170912Alcañiz Raya, ML.; Chicchi-Giglioli, IA.; Carrasco-Ribelles, LA.; Marín-Morales, J.; Minissi, ME.; Teruel-Garcia, G.; Sirera, M.... (2022). Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis. Autism Research. 15(1):131-145. https://doi.org/10.1002/aur.2636S13114515