Cyberpsychology and the Brain. The Interaction of Neuroscience and Affective Computing

by Thomas D. Parsons

Cyberpsychology is a relatively new discipline that is growing at an alarming rate. While a number of cyberpsychology-related journals and books have emerged, none directly address the neuroscience behind it. This book proposes a framework for integrating neuroscience and cyberpsychology for the study of social, cognitive, and affective processes, and the neural systems that support them. A brain-based cyberpsychology can be understood as a branch of psychology that studies the neurocognitive, affective, and social aspects of humans interacting with technology, as well as the affective computing aspects of humans interacting with computational devices or systems. As such, a cyberpsychologist working from a brain-based cyberpsychological framework studies both the ways in which persons make use of devices and the neurocognitive processes, motivations, intentions, behavioural outcomes, and effects of online and offline uses of technology. Cyberpsychology and the Brain brings researchers into the vanguard of cyberpsychology and brain research.

460 pages, Paperback

Published April 7, 2017

About the author

Thomas D. Parsons, PhD is a Clinical Neuropsychologist and Professor of Learning Technologies at the University of North Texas. He directs the Computational Neuropsychology and Simulation (CNS) lab at UNT. Prior to joining the faculty at UNT, he was a Professor and Research Scientist at the University of Southern California's Institute for Creative Technologies. His work integrates neuropsychology and simulation technologies for novel assessment, modeling, and training of neurocognitive and affective processes. He is a leading scientist in this area and he has directed 17 funded projects during his career and he has been an investigator on an additional 13 funded projects. In addition to his patents for the eHarmony.com Matching System, he has invented and validated virtual reality-based assessments of attention,spatial abilities, memory, and executive functions. He uses neural networks and machine learning to model mechanisms underlying reinforcement learning, decision making, working memory, and inhibitory control. He has over 200 publications in peer reviewed journals and book chapters. His contributions to neuropsychology were recognized when he received the 2013 National Academy of Neuropsychology Early Career Achievement award. In 2014, he was awarded Fellow status in the National Academy of Neuropsychology.