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8 | Bulletin vol. 34 no. 1 References Bauer, R. M., Iverson, G. L., Cernich, A. N., Binder, L. M., Ruff, R. M., & Naugle, R. I. (2012). Computerized neuropsychological assessment devices: joint position paper of the American Academy of Clinical Neuropsychology and the National Academy of Neuropsychology. The Clinical Neuropsychologist, 26(2), 177-196. Bilder, R. M. (2011). Neuropsychology 3.0: Evidence-based science and practice. Journal of the International Neuropsychological Society, 17(01), 7–13. Bohil, C. J., Alicea, B., & Biocca, F. A. (2011). Virtual reality in neuroscience research and therapy. Nature Reviews Neuroscience, 12, 752–762. Burgess, P. W., Alderman, N., Forbes, C., Costello, A., Laure, M. C., Dawson, D. R., … & Channon, S. (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. Cernich, A. N., Brennana, D. M., Barker, L. M., & Bleiberg, J. (2007). Sources of error in computerized neuropsychological assessment. Archives of Clinical Neuropsychology, 22, 39–48. 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. Feenstra, H. E., Vermeulen, I. E., Murre, J. M., & Schagen, S. B. (2017). Online cognition: Factors facilitating reliable online neuropsychological test results. The Clinical Neuropsychologist, 31(1), 59-84. Fragopanagos, N., & Taylor, J. G. (2005). Emotion recognition in human– computer interaction. Neural Networks, 18(4), 389-405. Gibbons, R. D., Weiss, D. J., Kupfer, D. J., Frank, E., Fagiolini, A., Grochocinski, V. J., … Immekus, J. C. (2008). Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatric Services, 59, 361–368. Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266. Jagaroo, V. (2009). Neuroinformatics for neuropsychology. In Neuroinformatics for Neuropsychology (pp. 25-84). Springer, New York, NY. Mitchell, R. L., & Xu, Y. (2015). What is the Value of Embedding Artificial Emotional Prosody in Human–Computer Interactions? Implications for Theory and Design in Psychological Science. Frontiers in psychology, 6, 1750. Overton, M., Pihlsgård, M., & Elmståhl, S. (2016). Test administrator effects on cognitive performance in a longitudinal study of ageing. Cogent Psychology, 3(1), 1260237. Parsey, C. M., & Schmitter-Edgecombe, M. (2013). Applications of technology in neuropsychological assessment. The Clinical Neuropsychologist, 27(8), 1328-1361. Parsons, T.D. (2015). Virtual Reality for Enhanced Ecological Validity and Experimental Control in the Clinical, Affective, and Social Neurosciences. Frontiers in Human Neuroscience, 1-19. Parsons, T.D. (2016). Clinical Neuropsychology and Technology: What's New and How We Can Use It. New York: Springer Press. 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. 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. Parsons, T.D., & Duffield, T. (2019). National Institutes of Health initiatives for advancing scientific developments in Clinical Neuropsychology. The Clinical Neuropsychologist, 33, 246-270. Rabin, L. A., Spadaccini, A. T., Brodale, D. L., Grant, K. S., Elbulok-Charcape, M. M., & Barr, W. B. (2014). Utilization rates of computerized tests and test batteries among clinical neuropsychologists in the United States and Canada. Professional Psychology: Research and Practice, 45, 368–377. Roark, B., Mitchell, M., Hosom, J. P., Hollingshead, K., & Kaye, J. (2011). Spoken language derived measures for detecting mild cognitive impairment. IEEE transactions on audio, speech, and language processing, 19(7), 2081-2090. automated methods. Furthermore, it is likely that by implementing machine learning approaches, technologically enhanced neuropsychological assessments could pick up additional data informative to the assessment process (Mitchell and Xu 2015). While there are limitations to current affective technologies, the gap between humans and computers in assessing emotion, while wide, continues to narrow through development and implementation of artificial neural network architectures constructed to handle the fusion of different modalities (e.g., facial features, prosody and lexical content in speech; Fragopanagos and Taylor 2005). While early computational approaches to speech recognition and language processing focused on automated analyses of linguistic structures and the development of lab- based technologies (machine translation, speech recognition, and speech synthesis), current approaches can be implemented in real-world speech-to-speech translation engines that can identify sentiment and emotion (Hirschberg and Manning 2015). Further, the temporal characteristics of spontaneous speech (e.g., speech tempo, number and length of pauses in speech) are markers of cognitive disorders (Roark, Mitchell et al. 2011) and may help with early diagnosis, and thus early intervention and planning for cognitive decline. With continual improvements in feature detection and quantification of body movements, eye- gaze, expressive language parameters, etc. that can be captured during testing, the range of clinically relevant aspects of patient presentations will continue to expand in ways that will both enhance and challenge current approaches to neuropsychological assessment. Conclusions Technologically advanced neuropsychological assessments are best considered as additional tools that may be used to provide the clinical neuropsychologist enhanced stimulus presentation and data logging for subsequent interpretation. It is important to note that while some clinical neuropsychologists may continue to cling to paper as a preferred technology, a recent survey revealed an increased likelihood of computerized test utilization among newer clinical neuropsychologists (Rabin et al., 2014). This new generation of clinical neuropsychologists and their patients will be more familiar with twenty-first century technologies for health care.

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