Age-related changes in amplitude, latency and specialization of ERP responses to faces and watches. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Boutet, I.; Shah, D.K.; Collin, C.A.; Berti, S.; Persike, M.; Meinhardt-Injac, B.
• Is Part Of: Aging, neuropsychology, and cognition. Volume 28:Issue 1(2021)
• Page Start: 37
• Page End: 64
• Publication Date: 2021-01-02
• Subjects: Aging -- faces -- event-related potentials -- holistic processing -- dedifferentiation -- P100 -- N170 -- P200; Aging -- Periodicals; Cognition in old age -- Periodicals; Cognition disorders in old age -- Periodicals; Neuropsychology -- Periodicals; Aging -- periodicals; Neuropsychology -- periodicals; Cognition -- periodicals; Mental Disorders -- periodicals; Gerontology -- periodicals; Mental Health -- periodicals; 155.67
• DOI: 10.1080/13825585.2019.1708253
• Languages: English
• ISSNs: 1382-5585

Abstract

ABSTRACT: Healthy aging is associated with impairments in face recognition. While earlier research suggests that these impairments arise during memory retrieval, more recent findings suggest that earlier mechanisms, at the perceptual stage, may also be at play. However, results are often inconsistent and very few studies have included a non-face control stimulus to facilitate interpretation of results with respect to the implication of specialized face mechanisms vs. general cognitive factors. To address these issues, P100, N170 and P200 event-related potentials (ERPs) were measured during processing of faces and watches. For faces, age-related differences were found for P100, N170 and P200 ERPs. For watches, age-related differences were found for N170 and P200 ERPs. Older adults showed less selective and less lateralized N170 responses to faces, suggesting that ERPs can detect age-related de-differentiation of specialized face networks. We conclude that age-related impairments in face recognition arise in part from difficulties in the earliest perceptual stages of visual information processing. A working model is presented based on coarse-to-fine analysis of visually similar exemplars.