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There is growing opportunity for technologies to augment human memory and other cognitive processes, but systems to date typically either address known cognitive impairments such as autism and Alzheimers disease or look to enhance ones general capacity for a specific task. In contrast to these approaches, we argue that recognition and quantification of human error is key to the design of future computing systems for augmenting the human mind. By focusing on cognitive errors, we can first identify frequent, persistent, or severe failures as targets for such systems and then go on to measure the success of any interventions.
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Clinch et al. (2018) studied this question.
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