Abstract
Neurocognitive self-enhancement can be defined as a voluntary attempt to improve one’s own cognitive skills and performance by means of neuroscience techniques able to influence the activity of neural structures and neural networks subserving such skills and performance. In the last years, the strive to improve personal potential and efficiency of cognitive functioning lead to the revival of mental training activities. Recently, it has been suggested that such practices may benefit from the support of mobile computing applications and wearable body-sensing devices. Besides discussing such topics, we report preliminary results of a project aimed at investigating the potential for cognitive-affective enhancement of a technology-mediated mental training intervention supported by a novel brain-sensing wearable device. Modulation of motivational and affective measures, neuropsychological and cognitive performances, and both electrophysiological and autonomic reactivity have been tested by dividing participants into an experimental and an active control group and by comparing the outcome of their psychometric, neuropsychological, and instrumental assessment before, halfway through, and after the end of the intervention period. The technology-mediated intervention seemed to help optimizing attention regulation, control and focusing skills, as marked by a reduction of response times at challenging computerized cognitive tasks and by the enhancement of event-related electrophysiological deflections marking early attention orientation and cognitive control. Available evidences, together with the first set of findings here reported, are starting to consistently show the potential of available methods and technologies for enhancing human cognitive abilities and improving efficiency of cognitive processes.
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Balconi, M., Crivelli, D. (2019). Wearable Devices for Self-enhancement and Improvement of Plasticity: Effects on Neurocognitive Efficiency. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_2
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