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dc.contributor.authorTa-Johnson, Vivian
dc.contributor.authorSuss, Joel M.
dc.contributor.authorLande, Brian
dc.identifier.citationTa-Johnson, V., Suss, J. and Lande, B. (2022), "Using natural language processing to measure cognitive load during use-of-force decision-making training", Policing: An International Journal, Vol. ahead-of-print No. ahead-of-print.
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dc.description.abstractPurpose Few studies have tested the efficacy of instruction based on cognitive load theory in police use-of-force (UoF) training due to limitations of existing cognitive load measures. Although linguistic measures of cognitive load address these limitations, they have yet to be applied to police UoF training. This study aims to discuss the aforementioned issue. Design/methodology/approach Officers’ verbal behavioral data from two UoF de-escalation projects were used to calculate cognitive load and assess how it varied with officer experience level (less-experienced, experienced). The verbal data were further analyzed to examine specific thinking patterns that contributed to heightened cognitive load across officer experience levels. Findings Across both studies, responses from less-experienced officers contained greater usage of cognitive language than responses from experienced officers. Specific cognitive processes that contribute to cognitive load in specific situations were also identified. Originality/value This paper enables police trainers to facilitate the development of adaptive training strategies to improve police UoF training via the reduction of cognitive load, and also contributes to the collective understanding of how less-experienced and experienced officers differ in their UoF decision-making.
dc.publisherEmerald Publishing Limited
dc.relation.ispartofseriesPolicing: An International Journal
dc.subjectDecision making
dc.subjectUse of force
dc.subjectPolice training
dc.titleUsing natural language processing to measure cognitive load during use-of-force decision-making training
dc.rights.holder© 2022 Emerald Publishing Limited

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