The Virtual Reality Stroop Task (VRST) was developed and validated by Dr. Parsons to assess supervisory attentional processing in a virtual environment with varying levels of arousal. The VRST involves a subject being immersed in a virtual Humvee as Stroop stimuli appear on the windshield. The VRST is a measure of executive functioning and was designed by Dr. Parsons to emulate the paper-and-pencil as well as ANAM versions of the Stroop test.

Participants are immersed in the VRST. Psychophysiological responses are recorded as participants ride in a simulated Humvee through alternating zones of low threat (i.e., little activity aside from driving down a desert road) and high threat (i.e., gunfire, explosions, and shouting amongst other stressors). The participants experience 4 low threat and 4 high threat zones designed to manipulate levels of arousal (start section; palm ambush; safe zone; city ambush; safe zone; and bridge ambush). The order of threat levels is counterbalanced across participants.

The VRST is employed to manipulate levels of cognitive workload, and is completed during exposure to the high and low threat zones. The VRST consists of 4 conditions: 1) word-reading, 2) color-naming, 3) simple interference; and 4) complex interference. Each Stroop condition is experienced once in a high threat zone and once in a low threat zone.

The VRST paradigm has been extended to a Virtual Classroom, a Virtual Apartment, and even a VRST administered by a Virtual Human Clinician.

  • ¬∑Parsons, T.D., & Barnett, M. (2019). Virtual Apartment-Based Stroop for Assessing Distractor Inhibition in Healthy Aging. Applied Neuropsychology: Adult, 26, 144-154. [IF = 1.548] (PDF)

  • Parsons, T.D., & Courtney, C.  (2018). Interactions between Threat and Executive Control in a Virtual Reality Stroop Task. IEEE Transactions on Affective Computing, 9, 66-75. [IF = 7.512] (PDF)

  • ¬∑Parsons, T.D., & Barnett, M. (2018). Virtual Apartment Stroop Task: Comparison with Computerized and Traditional Stroop Tasks. Journal of Neuroscience Methods, 309, 35-40 [IF = 2.668] (PDF)

  • Edwards, J., & Parsons, T. D. (2017). Virtual Reality Applications for Neuropsychological Assessment in the Military. In R. Kane & T.D. Parsons (Eds,). The Role of Technology in Clinical Neuropsychology, (pp. 175-236). Oxford University Press. (click here)

  • Dahdah, M., Bennett, M., Prajapati, B., Parsons, T.D., & Sullivan, E., & Driver, S. (2017). Application of Virtual Environments in a Multi-Disciplinary Day Neurorehabilitation Program to Improve Executive Functioning Using the Stroop Task. Neurorehabilitation, 41, 721-734..

  • 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. (PDF)

  • 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. (PDF)

  • *Wu, D., Lance, B., & Parsons, T.D. (2013). Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection. PLOS ONE, 1-18. (PDF)

  • Armstrong, C., Reger, G., Edwards, J., Rizzo, A., Courtney, C., & Parsons, T.D. (2013). Validity of the Virtual Reality Stroop Task (VRST) in Active Duty Military. Journal of Clinical and Experimental Neuropsychology, 35, 113-123. (PDF)

  • Parsons, T.D., & J. Reinebold. (2012). Adaptive Virtual Environments for Neuropsychological Assessment in Serious Games. IEEE Transactions on Consumer Electronics, 58, 197-204. (PDF)

  • *Wu, D., & Parsons, T.D. (2012). Customized Cognitive State Recognition Using Minimal User-Specific Data. Proceedings of the Military Health Systems Research Symposium, Fort Lauderdale, FL, August 2012.(PDF)

  • Parsons, T.D., Courtney, C., Arizmendi, B., & Dawson, M. (2011). Virtual Reality Stroop Task for Neurocognitive Assessment. Studies in Health Technology and Informatics, 143, 433-439. (PDF).

  • *Wu, D., & Parsons, T.D. (2011). Active Learning for Arousal Classification. Lecture Notes in Computer Science, 6975, 132-141. (PDF)

  • *Wu, D., & Parsons, T.D. (2011). Inductive Transfer Learning for Handling Individual Differences in Affective Computing. Lecture Notes in Computer Science, 6975, 142-151 (PDF)

  • *Wu, D., Courtney, C., Lance, B., Narayanan, S.S., Dawson, M., Oie, K., & Parsons, T.D. (2010). Optimal Arousal Identification and Classification for Affective Computing: Virtual Reality Stroop Task. IEEE Transactions on Affective Computing, 1, 109-118.(PDF)