[논문]N-backer: An auditory n-back task with automatic scoring of spoken responses

Monk, Andrew F.; Jackson, Dan; Nielsen, Dea; Jefferies, Elizabeth; Olivier, Patrick

doi:10.3758/s13428-011-0074-z

N-backer: An auditory n-back task with automatic scoring of spoken responses 원문보기

Behavior research methods : BRM, v.43 no.3, 2011년, pp.888 - 896

Monk, Andrew F. , Jackson, Dan , Nielsen, Dea , Jefferies, Elizabeth , Olivier, Patrick

Abstract ▼ AI-Helper

The n-back task is commonly used to load working memory (WM) in dual-task and neuroimaging experiments. However, it typically involves visual presentation and buttonpress responses, making it unsuitable for combination with primary tasks that involve vision and action, such as sequential object use and other tasks of daily living. The N-backer software presented here will automatically present and score auditory-verbal n-back sequences utilising the standard speech synthesis and recognition facilities that come with Microsoft Windows. Data are presented from an experiment in which 12 student participants carried out three tasks from the Naturalistic Action Test (NAT) while their attention was divided between the primary task and a continuous auditory-verbal 2-back secondary task. The participants' 2-back performance was scored in two ways: by hand, from video recordings, and automatically, using the software, allowing us to evaluate the accuracy of N-backer. There was an extremely high correlation between these scores (.933). The videos were also used to obtain a comprehensive error score for the NAT. Significantly more errors were made in the more complex NAT tasks when participants were 2-backing, as compared with when they were not, showing that the auditory-verbal n-back task can be used to disrupt sequential object use. This dual-task method may simulate the attentional deficits of patients with brain injury, providing insights into the difficulties they face in tasks of daily living.

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N-backer: An auditory n-back task with automatic scoring of spoken responses 원문보기

Abstract ▼ AI-Helper

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