Abstract
Decision-making involves numerous associated cognitive processes (memory, attention, learning, motor system) and is responsible for the final behavior of employees. Decision-making can be effective or lead to errors with significant consequences for organizations (economic or human). For this reason, decision-making is currently being studied extensively from different fields and with different approaches. From Psychology, human decision-making has classically been subject to manual or computerized methods from which general conclusions were drawn. However, decision-making is a highly complex process involving numerous sub-processes that increase the mental workload. In this regard, in recent years, numerous algorithms have been developed from computational models that allow different parameters of decision-making to be extracted and that are making it possible to scrutinize the processes underlying decision-making. Thus, based on Bayesian statistics, computational decision-making models can provide more specificity in studying human decision-making through complex and more robust algorithms to explain and predict this process. Therefore, this chapter aims to review the paradigms of human decision-making assessment (from a classical to a computational perspective) that allow the reader to have a clear and updated view of evaluating human decision-making. Considering that the tasks shown come from laboratory contexts or basic science, practical implications, and guidelines for their use by ergonomists and mental workload experts in industrial settings will be shown.
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Serrano, M.Á., Molins, F., Alacreu-Crespo, A. (2022). Human Decision-Making Evaluation: From Classical Methods to Neurocomputational Models. In: García Alcaraz, J.L., Realyvásquez Vargas, A. (eds) Algorithms and Computational Techniques Applied to Industry. Studies in Systems, Decision and Control, vol 435. Springer, Cham. https://doi.org/10.1007/978-3-031-00856-6_9
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