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Updated: Feb 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Selective impairments in components of affective prosody in neurologically impaired individuals
Amy Wright1, Sadhvi Saxena1, Shannon M Sheppard1
1Johns Hopkins University School of Medicine, Department of Neurology, USA.
Abstract:
The intent and feelings of the speaker are often conveyed less by what they say than by how they say it, in terms of the affective prosody - modulations in pitch, loudness, rate, and rhythm of the speech to convey emotion. Here we propose a cognitive architecture of the perceptual, cognitive, and motor processes underlying recognition and generation of affective prosody. We developed the architecture on the basis of the computational demands of the task, and obtained evidence for various components by identifying neurologically impaired patients with relatively specific deficits in one component. We report analysis of performance across tasks of recognizing and producing affective prosody by four patients (three with right hemisphere stroke and one with frontotemporal dementia). Their distinct patterns of performance across tasks and quality of their abnormal performance provides preliminary evidence that some of the components of the proposed architecture can be selectively impaired by focal brain damage.
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