Abstract
Efforts have always been deployed to surpass limitations in human cognitive abilities to enhance aspects such as task accuracy, work effectiveness, and error management. Cognitive enhancement is a field aiming at improving human cognition to overcome those limitations. It bears important interest from the human factors community given its potential for reducing errors in complex operational environments, but also for occupational psychology to improve work performance, mitigate risks, and improve job stress/well-being. Yet, cognitive enhancement strategies are still marginally used in practice. The current narrative review presents a brief summary of the literature on human cognitive enhancement and discusses key implications as well as operational applications of the main methods and technologies reported in this field. Using a human factors perspective, the paper also outlines how such techniques could be integrated into intelligent support systems to help operators facing cognitive challenges in complex operational domains, including those experiencing functional limitations preventing them to contribute to the workforce. We also discuss the implications of integrating such techniques into the workplace and the consequences this might incur for workers and stakeholders. Then, we briefly present a five-step guideline to discuss ways of optimally integrating cognitive enhancement methods into the workplace.
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Part of this work was presented in a non-archived proceeding volume of the Cognitive Science Society [Marois, A., & Lafond, D. (2022). Towards augmenting humans in the field: A review of cognitive enhancement methods and applications. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society (Vol. 44, pp. 1891–1902). Retrieved from https://escholarship.org/uc/item/5mx7h3m0].
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Marois, A., Lafond, D. Augmenting cognitive work: a review of cognitive enhancement methods and applications for operational domains. Cogn Tech Work 24, 589–608 (2022). https://doi.org/10.1007/s10111-022-00715-1
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DOI: https://doi.org/10.1007/s10111-022-00715-1