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Virtual School Environments for Neuropsychological Assessment and Training

  • Thomas D. ParsonsEmail author
  • Tyler Duffield
  • Timothy McMahan
  • Unai Diaz-Orueta
Chapter
Part of the Educational Communications and Technology: Issues and Innovations book series (ECTII)

Abstract

The virtual school environment has been developed and validated by the Computational Neuropsychology and Simulation (CNS) Laboratory of Dr. Thomas Parsons. The overarching goal of the virtual school project is to provide neuropsychological, affective, and social cognitive assessments that are more meaningful for the lives of children. These previously developed and validated virtual reality (VR) simulations of various contexts within the school environment (e.g., classroom, hallway, playground) can be combined and harnessed to gain ecologically valid assessments of children in real-world situations. The virtual school environment generates synthetic surroundings, including a virtual classroom, hallway, and playground via a 360-degree immersive experience. Furthermore, the computational design and administration of the virtual school environment platform allows for simultaneous recording of the child’s behavioral and physiological responses. Virtual environments can be used to offer traditional psychometric testing and can collect additional real-time data (e.g., head movements, limb movements that reflect distraction). As such, they have potential to provide greater diagnostic specificity and more useful targets for intervention.

Keywords

Virtual reality Ecological validity Neurodevelopmental disorders Autism Attention-deficit/hyperactivity disorder Neuropsychological assessment 

References

  1. Adams, R., Finn, P., Moes, E., Flannery, K., & Rizzo, A. S. (2009). Distractibility in attention/deficit/hyperactivity disorder (ADHD): The virtual reality classroom. Child Neuropsychology, 15(2), 120–135.CrossRefGoogle Scholar
  2. Andari, E., Duhamel, J. R., Zalla, T., Herbrecht, E., Leboyer, M., & Sirigu, A. (2010). Promoting social behavior with oxytocin in high-functioning autism spectrum disorders. Proceedings of the National Academy of Sciences of the United States of America, 107, 4389–4394.CrossRefGoogle Scholar
  3. Andari, E., Richard, N., Leboyer, M., & Sirigu, A. (2016). Adaptive coding of the value of social cues with oxytocin, an fMRI study in autism spectrum disorder. Cortex, 76, 79–88.CrossRefGoogle Scholar
  4. Areces, D., Rodríguez, C., García, T., Cueli, M., & González-Castro, P. (2018). Efficacy of a continuous performance test based on virtual reality in the diagnosis of ADHD and its clinical presentations. Journal of Attention Disorders, 22(11), 1081–1091.  https://doi.org/10.1177/1087054716629711 CrossRefGoogle Scholar
  5. Barkley, R. A. (1994). Can neuropsychological tests help diagnose ADD/ADHD? The ADHD Report, 2, 1–3.Google Scholar
  6. Biederman, J., Faraone, S. V., Milberger, S., & Doyle, A. (1993). Diagnoses of attention-deficit hyperactivity disorder from parent reports predict diagnoses based on teacher reports. Journal of the American Academy of Child & Adolescent Psychiatry, 32, 315–317.CrossRefGoogle Scholar
  7. Bioulac, S., Lallemand, S., Rizzo, A., Philip, P., Fabrigoule, C., & Bouvard, M. P. (2012). Impact of time on task on ADHD patient’s performances in a virtual classroom. European Journal of Paediatric Neurology, 16(5), 514–521.CrossRefGoogle Scholar
  8. Bohil, C. J., Alicea, B., & Biocca, F. A. (2011). Virtual reality in neuroscience research and therapy. Nature Reviews Neuroscience, 12, 752–762.CrossRefGoogle Scholar
  9. Bolling, D. Z., Pitskel, N. B., Deen, B., Crowley, M. J., Mayes, L. C., & Pelphrey, K. A. (2011). Development of neural systems for processing social exclusion from childhood to adolescence. Developmental Science, 14(6), 1431–1444.CrossRefGoogle Scholar
  10. Bottari, C., Dassa, C., Rainville, C., & Dutil, E. (2009). The criterion-related validity of the IADL: Profile with measures of executive functions, indices of trauma severity and sociodemographic characteristics. Brain Injury, 23, 322–335.CrossRefGoogle Scholar
  11. Brock, L. L., Rimm-Kaufman, S. E., Nathanson, L., & Grimm, K. J. (2009). The contributions of ‘hot’ and ‘cool’ executive function to children’s academic achievement, learning-related behaviors, and engagement in kindergarten. Early Childhood Research Quarterly, 24(3), 337–349.CrossRefGoogle Scholar
  12. Burgess, P. W., Alderman, N., Forbes, C., Costello, A., Laure, M. C., Dawson, D. R., et al. (2006). The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. Journal of the International Neuropsychological Society, 12(2), 194–209.CrossRefGoogle Scholar
  13. Chan, R. C., Shum, D., Toulopoulou, T., & Chen, E. Y. (2008). Assessment of executive functions: Review of instruments and identification of critical issues. Archives of Clinical Neuropsychology, 23(2), 201–216.CrossRefGoogle Scholar
  14. Chaytor, N., & Schmitter-Edgecombe, M. (2003). The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychology Review, 13, 181–197.CrossRefGoogle Scholar
  15. Chen, F. S., Kumsta, R., von Dawans, B., Monakhov, M., Ebstein, R. P., & Heinrichs, M. (2011). Common oxytocin receptor gene (OXTR) polymorphism and social support interact to reduce stress in humans. Proceedings of the National Academy of Sciences, 108(50), 19937–19942.CrossRefGoogle Scholar
  16. Conners, C. K. (2004). Conners’ CPT II: Continuous performance test II. New York: MHS.Google Scholar
  17. Davidson, D. J., Zacks, R. T., & Williams, C. (2003). Stroop interference, practice, and aging. Aging, Neuropsychology and Cognition, 10, 85–98.CrossRefGoogle Scholar
  18. de Nijs, P. F., Ferdinand, R. F., de Bruin, E. I., Dekker, M. C. J., van Duijn, C. M., & Verhulst, D. C. (2004). Attention-deficit/hyperactivity disorder (ADHD): Parents’ judgment about school, teachers’ judgment about home. European Child & Adolescent Psychiatry, 13, 315–320.CrossRefGoogle Scholar
  19. Díaz-Orueta, U., Alonso-Sánchez, B., & Climent, G. (2014). AULA versus d2 test of attention: Convergent validity and applicability of virtual reality in the study of reading disorders. Preliminary results. Poster presented at the 42th Annual Meeting of the International Neuropsychological Society, Seattle, USA, 12–15 February.Google Scholar
  20. Díaz-Orueta, U., Cueto, E., Alonso-Sánchez, B., Crespo-Eguílaz, N., Fernández, M., Otaduy, C., et al. (2014). AULA VR based attention test: Factorial validity and convergent validity with commonly used ADHD diagnostic tools. Poster presented at the 9th Conference of the International Test Commission, San Sebastian, Spain, 2–5 July.Google Scholar
  21. Díaz-Orueta, U., Fernández-Fernández, M. A., Morillo-Rojas, M. D., & Climent, G. (2016). Efficacy of lisdexamphetamine to improve the behavioural and cognitive symptoms of attention deficit hyperactivity disorder: Treatment monitored by means of the AULA Nesplora virtual reality test. Revista de Neurologia, 63(1), 19–27.Google Scholar
  22. Díaz-Orueta, U., Garcia-López, C., Crespo-Eguílaz, N., Sánchez-Carpintero, R., Climent, G., & Narbona, J. (2014). AULA virtual reality test as an attention measure: Convergent validity with Conners’ continuous performance test. Child Neuropsychology: A Journal on Normal and Abnormal Development in Childhood and Adolescence, 20(3), 328–342.CrossRefGoogle Scholar
  23. Díaz-Orueta, U., Iriarte, Y., Climent, G., & Banterla, F. (2012). AULA: An ecological virtual reality test with distractors for evaluating attention in children and adolescents. Journal of Virtual Reality, 5, 1–20.Google Scholar
  24. Duffield, T. C., Parsons, T. D., Landry, A., Karam, S., Otero, T., Mastel, S., & Hall, T. A. (2017). Virtual environments as an assessment modality with pediatric ASD populations: a brief report. Child Neuropsychology, 1–8.Google Scholar
  25. Ellaway, R. (2006). eMedical teacher. Medical Teacher, 28(8), 751–752.CrossRefGoogle Scholar
  26. Epstein, J. N., Erkanli, A., Conners, C. K., Kleric, J., Castello, J. E., & Angold, A. (2003). Relations between continuous performance test performance measures and ADHD behaviors. Journal of Abnormal Child Psychology, 31(5), 543–554.CrossRefGoogle Scholar
  27. Farra, S., Miller, E., Timm, N., & Schafer, J. (2013). Improved training for disasters using 3-D virtual reality simulation. Western Journal of Nursing Research, 35(5), 655–671.CrossRefGoogle Scholar
  28. Fernandez, M. (2017). Augmented virtual reality: How to improve education systems. Higher Learning Research Communications, 7, 1–15.CrossRefGoogle Scholar
  29. Fernández-Fernández, M., & Morillo-Rojas, M. (2012). Test—Retest validation of AULA Nesplora (virtual reality continuous performance test) for ADHD. Poster presented at the 2nd International ADHD Conference, Barcelona, Spain, 23–25 May.Google Scholar
  30. Forbes, G. B. (1998). Clinical utility of the test of variables of attention (TOVA) in the diagnosis of attention-deficit/hyperactivity disorder. Journal of Clinical Psychology, 54, 461–476.CrossRefGoogle Scholar
  31. Gilboa, Y., Kerrouche, B., Longaud-Vales, A., Kieffer, V., Tiberghien, A., Aligon, D., et al. (2015). Describing the attention profile of children and adolescents with acquired brain injury using the Virtual Classroom. Brain Injury, 29(13–14), 1691–1700.CrossRefGoogle Scholar
  32. Gilboa, Y., Rosenblum, S., Fattal-Valevski, A., Toledano-Alhadef, H., Rizzo, A. S., & Josman, N. (2011). Using a Virtual Classroom environment to describe the attention deficits profile of children with Neurofibromatosis type 1. Research in Developmental Disabilities, 32(6), 2608–2613.CrossRefGoogle Scholar
  33. Gordon, M. (1983). The Gordon Diagnostic System (GDS) The Standard in Computerized Assessment of Attention and Self Control (online). Retrieved September 25, 2014, from http://www.devdis.com/gds.html (online).
  34. Greenberg, L. M., & Waldman, I. D. (1993). Developmental normative data on the Test of Variables of Attention (TOVA). Journal of Child Psychology and Psychiatry, 34, 1019–1030.CrossRefGoogle Scholar
  35. Gutiérrez Maldonado, J., Letosa Porta, A., Rus Calafell, M., & Peñaloza Salazar, C. (2009). The assessment of Attention Deficit Hyperactivity Disorder in children using continuous performance tasks in virtual environments. Anuario de Psicología, 40(2), 211–222.Google Scholar
  36. Halvorson, W., Crittenden, V. L., & Pitt, L. (2011). Teaching cases in a virtual environment: When the traditional case classroom is problematic. Decision Sciences Journal of Innovative Education, 9(3), 485–492.CrossRefGoogle Scholar
  37. Iriarte, Y., Díaz-Orueta, U., Cueto, E., Irazustabarrena, P., Banterla, F., & Climent, G. (2016). AULA—Advanced virtual reality tool for the assessment of attention: Normative study in Spain. Journal of Attention Disorders, 20(6), 542–568.CrossRefGoogle Scholar
  38. Kaplan, R. M., Howard, V. J., Safford, M. M., & Howard, G. (2015). Educational attainment and longevity: results from the REGARDS US national cohort study of blacks and whites. Annals of epidemiology, 25(5), 323–328.CrossRefGoogle Scholar
  39. Kassner, M. P., Wesselmann, E. D., Law, A. T., & Williams, K. D. (2012). Virtually ostracized: Studying ostracism in immersive virtual environments. Cyberpsychology, Behavior, and Social Networking, 15(8), 399–403.CrossRefGoogle Scholar
  40. Kirsch, P., Esslinger, C., Chen, Q., Mier, D., Lis, S., Siddhanti, S., et al. (2005). Oxytocin modulates neural circuitry for social cognition and fear in humans. Journal of Neuroscience, 25(49), 11489–11493.CrossRefGoogle Scholar
  41. Krach, S., Kamp-Becker, I., Einhäuser, W., Sommer, J., Frässle, S., Jansen, A., et al. (2015). Evidence from pupillometry and fMRI indicates reduced neural response during vicarious social pain but not physical pain in autism. Human Brain Mapping, 14, 4730–4744.CrossRefGoogle Scholar
  42. Lalonde, G., Henry, M., Drouin-Germain, A., Nolin, P., & Beauchamp, M. H. (2013). Assessment of executive function in adolescence: A comparison of traditional and virtual reality tools. Journal of Neuroscience Methods, 219(1), 76–82.CrossRefGoogle Scholar
  43. Lemay, S., Bedard, M. A., Roulea, I., & Tremblay, P. L. G. (2004). Practice effect and test–retest reliability of attentional and executive tests in middle-aged to elderly subjects. The Clinical Neuropsychologist, 18, 284–302.CrossRefGoogle Scholar
  44. Limniou, M., Roberts, D., & Papadopoulos, N. (2008). Full immersive virtual environment CAVETM in chemistry education. Computers & Education, 51(2), 584–593.CrossRefGoogle Scholar
  45. Llorente, A. M., Voigt, R., Jensen, C. L., Fraley, J. K., Heird, W. C., & Rennie, K. M. (2007). The test of variables of attention (TOVA): Internal consistency (Q(1) vs Q(2) and Q(3) vs Q(4)) in children with attention deficit/hyperactivity disorder [ADHD]. Child Neuropsychology, 3, 1–9.Google Scholar
  46. Losier, B. J., McGrath, P. J., & Klein, R. M. (1996). Error patterns on the continuous performance test in non-medicated and medicated samples of children with and without ADHD: A meta-analytic review. Journal of Child Psychology and Psychiatry, 37, 971–987.CrossRefGoogle Scholar
  47. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: an integrative review. Psychological Bulletin, 109(2), 163– 203.CrossRefGoogle Scholar
  48. MacLeod, C. M. (1992). The Stroop task: The “gold standard” of attentional measures. Journal of Experimental Psychology: General, 121(1), 12–14.CrossRefGoogle Scholar
  49. Manchester, D., Priestley, N., & Jackson, H. (2004). The assessment of executive functions: Coming out of the office. Brain Injury, 18(11), 1067–1081.CrossRefGoogle Scholar
  50. Masten, C. L., Colich, N. L., Rudie, J. D., Bookheimer, S. Y., Eisenberger, N. I., & Dapretto, M. (2011). An fMRI investigation of responses to peer rejection in adolescents with autism spectrum disorders. Developmental Cognitive Neuroscience, 1(3), 260–270.CrossRefGoogle Scholar
  51. Mavromihelaki, E., Eccles, J., Harrison, N., Grice-Jackson, T., Ward, J., Critchley, H., et al. (2014). Cyberball3D+: A 3D serious game for fMRI investigating social exclusion and empathy. 6th International Conference on Games and Virtual Worlds for Serious Applications: VS-GAMES 2014.Google Scholar
  52. McPartland, J. C., Crowley, M. J., Perszyk, D. R., Naples, A., Mukerji, C. E., Wu, J., et al. (2011). Temporal dynamics reveal atypical brain response to social exclusion in autism. Developmental Cognitive Neuroscience, 1(3), 271–279.CrossRefGoogle Scholar
  53. Mejías, M., Redondo, M., Fernández, M., & Diaz-Orueta, U. (2016). Eficacia del metilfenidato de liberación prolongada en la mejora sintomática cognitiva y conductual del TDAH monitorizado a través de AULA Nesplora. XX Congreso Anual de la Academia Iberoamericana de Neurología Pediátrica (AINP), Madrid, Spain, 8th–10th September.Google Scholar
  54. Mitsis, E. M., McKay, K. E., Schulz, K. P., Newcorn, J. H., & Halperin, J. M. (2000). Parent-teacher concordance for DSM–IV attention-deficit/hyperactivity disorder in a clinic-referred sample. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 308–313.CrossRefGoogle Scholar
  55. Mühlberger, A., Jekel, K., Probst, T., Schecklmann, M., Conzelmann, A., Andreatta, M., et al. (2016). The influence of methylphenidate on hyperactivity and attention deficits in children with ADHD: A virtual classroom test. Journal of Attention Disorders, 1087054716647480.Google Scholar
  56. Neguț, A., Jurma, A. M., & David, D. (2017). Virtual-reality-based attention assessment of ADHD: ClinicaVR: Classroom-CPT versus a traditional continuous performance test. Child Neuropsychology, 23(6), 692–712.CrossRefGoogle Scholar
  57. Nolin, P., Martin, C., & Bouchard, S. (2009). Assessment of inhibition deficits with the virtual classroom in children with traumatic brain injury: A pilot-study. Studies in Health Technology and Informatics, 144, 240–242.Google Scholar
  58. Nolin, P., Stipanicic, A., Henry, M., Joyal, C. C., & Allain, P. (2012). Virtual reality as a screening tool for sports concussion in adolescents. Brain Injury, 26(13–14), 1564–1573.CrossRefGoogle Scholar
  59. Nolin, P., Stipanicic, A., Henry, M., Lachapelle, Y., Lussier-Desrochers, D., Rizzo, A., et al. (2016). ClinicaVR: Classroom-CPT: A virtual reality tool for assessing attention and inhibition in children and adolescents. Computers in Human Behavior, 59, 327–333.CrossRefGoogle Scholar
  60. Parsons, T. D. (2014). Virtual teacher and classroom for assessment of neurodevelopmental disorders. In S. Brahnam & L. C. Jain (Eds.), Serious games, alternative realities, and play therapy (pp. 121–137). Heidelberg: Springer.Google Scholar
  61. Parsons, T. D. (2015). Virtual reality for enhanced ecological validity and experimental control in the clinical, affective and social neurosciences. Frontiers in Human Neuroscience, 9, 660.CrossRefGoogle Scholar
  62. Parsons, S. (2016). Authenticity in virtual reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138–157.CrossRefGoogle Scholar
  63. Parsons, T. D., Bowerly, T., Buckwalter, J. G., & Rizzo, A. A. (2007). A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods. Child Neuropsychology, 13, 363–381.CrossRefGoogle Scholar
  64. Parsons, T. D., & Carlew, A. R. (2016). Bimodal virtual reality Stroop for assessing distractor inhibition in autism Spectrum disorders. Journal of Autism and Developmental Disorders, 46(4), 1255–1267.CrossRefGoogle Scholar
  65. Parsons, T. D., Carlew, A. R., Magtoto, J., & Stonecipher, K. (2017). The potential of function-led virtual environments for ecologically valid measures of executive function in experimental and clinical neuropsychology. Neuropsychological Rehabilitation, 37(5), 777–807.CrossRefGoogle Scholar
  66. Parsons, T. D., Courtney, C., & Dawson, M. (2013). Virtual reality Stroop task for assessment of supervisory attentional processing. Journal of Clinical and Experimental Neuropsychology, 35, 812–826.CrossRefGoogle Scholar
  67. Parsons, T. D., Gaggioli, A., & Riva, G. (2017). Virtual environments in social neuroscience. Brain Sciences, 7(42), 1–21.Google Scholar
  68. Parsons, T. D., & Phillips, A. (2016). Virtual reality for psychological assessment in clinical practice. Practice Innovations, 1, 197–217.CrossRefGoogle Scholar
  69. Parsons, T. D., Riva, G., Parsons, S., Mantovani, F., Newbutt, N., Lin, L., et al. (2017). Virtual reality in pediatric psychology: Benefits, challenges, and future directions. Pediatrics, 140, 86–91.CrossRefGoogle Scholar
  70. Parsons, T.D., McMahan, T., & Kane, R. (2018). Practice Parameters Facilitating Adoption of Advanced Technologies for Enhancing Neuropsychological Assessment Paradigms. The Clinical Neuropsychologist, 32, 16–41.CrossRefGoogle Scholar
  71. Parsons, T. D., & Rizzo, A. A. (in press). A virtual classroom for ecologically valid assessment of attention-deficit/hyperactivity disorder. In P. Sharkey (Ed.), Virtual reality technologies for health and clinical applications: Psychological and neurocognitive interventions. Heidelberg: Springer.Google Scholar
  72. Pelphrey, K. A., & Morris, J. P. (2006). Brain mechanisms for interpreting the actions of others from biological-motion cues. Current Directions in Psychological Science, 15(3), 136–140.CrossRefGoogle Scholar
  73. Pelphrey, K. A., Morris, J. P., & McCarthy, G. (2005). Neural basis of eye gaze processing deficits in autism. Brain, 128(5), 1038–1048.CrossRefGoogle Scholar
  74. Pollak, Y., Shomaly, H. B., Weiss, P. L., Rizzo, A. A., & Gross-Tsur, V. (2010). Methylphenidate effect in children with ADHD can be measured by an ecologically valid continuous performance test embedded in virtual reality. CNS Spectrums, 15(2), 125–130.CrossRefGoogle Scholar
  75. Pollak, Y., Weiss, P. L., Rizzo, A. A., Weizer, M., Shriki, L., Shalev, R. S., et al. (2009). The utility of a continuous performance test embedded in virtual reality in measuring ADHD-related deficits. Journal of Developmental & Behavioral Pediatrics, 30(1), 2–6.CrossRefGoogle Scholar
  76. Reisoğlu, I., Topu, B., Yılmaz, R., Karakuş Yılmaz, T., & Göktaş, Y. (2017). 3D virtual learning environments in education: A meta-review. Asia Pacific Education Review, 18(1), 81–100.CrossRefGoogle Scholar
  77. Riccio, C. A., Garland, B. H., & Cohen, M. J. (2007). Relations between the test of variables of attention (TOVA) and the Children’s Memory Scale (CMS). Journal of Attention Disorders, 11, 167–171.CrossRefGoogle Scholar
  78. Riccio, C. A., Reynolds, C. R., & Lowe, P. (2001). Clinical applications of continuous performance tests. New York: Wiley.Google Scholar
  79. Rizzo, A. A., Bowerly, T., Buckwalter, J. G., Limchuk, D., Mitura, R., & Parsons, T. D. (2006). A virtual reality scenario for all seasons: The virtual classroom. CNS Spectrums, 11, 35–44.CrossRefGoogle Scholar
  80. Rufo-Campos, M., Cueto, E., Iriarte, Y., & Rufo-Muñoz, M. (2012). Sensitivity study of a new diagnostic method for ADHD: Aula Nesplora. Paper presented at the XXXVI Annual Meeting of the Spanish Society of Pediatric Neurology, Santander, Spain, 31st May–2nd June.Google Scholar
  81. Sandford, J. A., & Turner, A. (1995). Manual for the integrated visual and auditory (IVA) continuous performance test. Richmond, VA: BrainTrain.Google Scholar
  82. Sbordone, R. J. (2008). Ecological validity of neuropsychological testing: Critical issues. In A. M. Horton Jr. & D. Wedding (Eds.), The neuropsychology handbook (pp. 367–394). New York, NY: Springer.Google Scholar
  83. Séguin, J. R., Arseneault, L., & Tremblay, R. E. (2007). The contribution of “cool” and “hot” components of decision-making in adolescence: Implications for developmental psychopathology. Cognitive Development, 22(4), 530–543.CrossRefGoogle Scholar
  84. Servera, M., & Llabrés, J. (2004). CSAT: Children sustained attention task [Book in Spanish]. Madrid: TEA.Google Scholar
  85. Thorsteinsson, G., & Page, T. (2008). Innovative technology education using a virtual reality learning environment. Pedagogy Studies (Pedagogika), 90, 26–35.Google Scholar
  86. U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS). “Public School Data File,” 2007–2008.Google Scholar
  87. Uttl, B., & Graf, P. (1997). Color–Word Stroop test performance across the adult life span. Journal of Clinical and Experimental Neuropsychology, 19, 405–420.CrossRefGoogle Scholar
  88. Van Der Meulen, M., Van IJzendoorn, M. H., & Crone, E. A. (2016). Neural correlates of prosocial behavior: Compensating social exclusion in a four-player cyberball game. PLoS One, 11(7), 1–13.Google Scholar
  89. Venturini, E., Riva, P., Serpetti, F., Romero, L., Pallavincini, F., Mantovani, F., et al. (2016). A comparison of 3D versus 2D virtual environments on the feelings of social exclusion, inclusion and over-inclusion. Annual Review of CyberTherapy and Telemedicine, 14, 89–94.Google Scholar
  90. Virvou, M., & Katsionis, G. (2008). On the usability and likeability of virtual reality games for education: The case of VR-ENGAGE. Computers and Education, 50(1), 154–178.CrossRefGoogle Scholar
  91. Wada, N., Yamashita, Y., Matsuishi, T., Ohtani, Y., & Kato, H. (2000). The test of variables of attention (TOVA) is useful in the diagnosis of Japanese male children with attention deficit hyperactivity disorder. Brain Development, 22, 378–382.CrossRefGoogle Scholar
  92. Washburn, D. A. (2016). The Stroop effect at 80: The competition between stimulus control and cognitive control. Journal of the Experimental Analysis of Behavior, 105(1), 3–13.CrossRefGoogle Scholar
  93. Weyandt, L. L., Mitzlaff, L., & Thomas, L. (2002). The relationship between intelligence and performance on the test of variables of attention (TOVA). Journal of Learning Disabilities, 35, 114–120.CrossRefGoogle Scholar
  94. White, S. W., Richey, J. A., Gracanin, D., Bell, M. A., LaConte, S., Coffman, M., et al. (2014). The promise of neurotechnology in clinical translational science. Clinical Psychological Science, 3(5), 797–815.CrossRefGoogle Scholar
  95. Williams, K. D., Cheung, C. K. T., & Choi, W. (2000). CyberOstracism: Effects of being ignored over the Internet. Journal of Personality and Social Psychology, 79, 748–762.CrossRefGoogle Scholar
  96. Wilson, B. A. (1998). Cognitive rehabilitation: How it is and how it should be. Journal of the International Neuropsychological Society, 3, 487–496.Google Scholar
  97. Wirth, J., Feldberg, F., Schouten, A. P., Hooff, B., & Williams, K. D. (2011). Using virtual game environments to study group behavior. In A. Hollingshead & M. Scott Poole (Eds.), Research methods for studying groups and teams: A guide to approaches, tools and technologies (pp. 1–24). New York, NY: Routledge.Google Scholar
  98. Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6(4), 354–360.Google Scholar
  99. Zelnik, N., Bennett-Back, O., Miari, W., Goez, H. R., & Fattal-Valevski, A. (2012). Is the test of variables of attention reliable for the diagnosis of attention-deficit hyperactivity disorder (ADHD). Journal of Child Neurology, 27, 703.  https://doi.org/10.1177/0883073811423821. Published online, 28th February 2012.CrossRefGoogle Scholar
  100. Zimmerman, D. L., Ownsworth, T., O’Donovan, A., Roberts, J., & Gullo, M. J. (2016). Independence of hot and cold executive function deficits in high-functioning adults with autism spectrum disorder. Frontiers in Human Neuroscience, 10, 24.Google Scholar
  101. Zulueta, A., Iriarte, Y., Díaz-Orueta, U., & Climent, G. (2013). Aula Nesplora: Progress in assessing attention processes—A convergent validity study with the Faces—Perception of Differences Test (extended version). ISEP Science, 4, 3–10.Google Scholar

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© Association for Educational Communications and Technology 2019

Authors and Affiliations

  • Thomas D. Parsons
    • 1
    Email author
  • Tyler Duffield
    • 2
  • Timothy McMahan
    • 3
  • Unai Diaz-Orueta
    • 4
  1. 1.College of Information, University of North TexasComputational Neuropsychology and SimulationDentonUSA
  2. 2.Oregon Health & Science UniversityPortlandUSA
  3. 3.University of Texas at DallasRichardsonUSA
  4. 4.Maynooth UniversityMaynoothIreland

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