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      The effect of feedback timing on category learning and feedback processing in younger and older adults

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          Abstract

          Introduction

          Corrective feedback can be received immediately after an action or with a temporal delay. Neuroimaging studies suggest that immediate and delayed feedback are processed by the striatum and medial temporal lobes (MTL), respectively. Age-related changes in the striatum and MTL may influence the efficiency of feedback-based learning in older adults. The current study leverages event-related potentials (ERPs) to evaluate age-related differences in immediate and delayed feedback processing and consequences for learning. The feedback-related negativity (FRN) captures activity in the frontostriatal circuit while the N170 is hypothesized to reflect MTL activation.

          Methods

          18 younger ( M years  = 24.4) and 20 older ( M years  = 65.5) adults completed learning tasks with immediate and delayed feedback. For each group, learning outcomes and ERP magnitudes were evaluated across timing conditions.

          Results

          Younger adults learned better than older adults in the immediate timing condition. This performance difference was associated with a typical FRN signature in younger but not older adults. For older adults, impaired processing of immediate feedback in the striatum may have negatively impacted learning. Conversely, learning was comparable across groups when feedback was delayed. For both groups, delayed feedback was associated with a larger magnitude N170 relative to immediate feedback, suggesting greater MTL activation.

          Discussion and conclusion

          Delaying feedback may increase MTL involvement and, for older adults, improve category learning. Age-related neural changes may differentially affect MTL- and striatal-dependent learning. Future research can evaluate the locus of age-related learning differences and how feedback can be manipulated to optimize learning across the lifespan.

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          Most cited references75

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          "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

          M F Folstein, S Folstein, P R McHugh (1975)
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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            Arnaud Delorme, Scott Makeig (2004)
            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
            Article 0 comments Cited 3337 times – based on 0 reviews     Review now
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              R: a language and environment for statistical computing

              (Team) R-pRC, Core Team R Development, Core R … (2021)
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                Author and article information

                Contributors
                Kristen Nunn: URI : https://loop.frontiersin.org/people/2691916/overviewRole: Role: Role: Role: Role:
                Robert Creighton: Role: Role: Role: Role:
                Victoria Tilton-Bolowsky: URI : https://loop.frontiersin.org/people/1239951/overviewRole: Role:
                Yael Arbel: URI : https://loop.frontiersin.org/people/969497/overviewRole: Role: Role: Role: Role: Role: Role: Role:
                Sofia Vallila-Rohter: URI : https://loop.frontiersin.org/people/435212/overviewRole: Role: Role: Role: Role: Role: Role:
                Journal
                Journal ID (nlm-ta): Front Aging Neurosci
                Journal ID (iso-abbrev): Front Aging Neurosci
                Journal ID (publisher-id): Front. Aging Neurosci.
                Title: Frontiers in Aging Neuroscience
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1663-4365
                Publication date (Electronic): 03 June 2024
                Publication date Collection: 2024
                Volume: 16
                Electronic Location Identifier: 1404128
                Affiliations
                [1] 1MGH Institute of Health Professions , Boston, MA, United States
                [2] 2Geriatric Research Education and Clinical Center, VA Pittsburgh Healthcare System , Pittsburgh, PA, United States
                [3] 3Johns Hopkins University School of Medicine , Baltimore, MD, United States
                Author notes

                Edited by: Adrian W. Gilmore, National Institute of Mental Health (NIH), United States

                Reviewed by: Nicole Long, University of Virginia, United States

                Irene van de Vijver, Utrecht University, Netherlands

                *Correspondence: Kristen Nunn, kristen.nunn@ 123456va.gov
                Article
                DOI: 10.3389/fnagi.2024.1404128
                PMC ID: 11182045
                PubMed ID: 38887611
                SO-VID: 83431452-36ce-4dbc-8900-8871ffe1f23f
                Copyright © Copyright © 2024 Nunn, Creighton, Tilton-Bolowsky, Arbel and Vallila-Rohter.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 20 March 2024
                Date accepted : 21 May 2024
                Page count
                Figures: 9, Tables: 5, Equations: 0, References: 76, Pages: 15, Words: 10329
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Institute on Deafness and Other Communication Disorders of the National Institute of Health awarded to Sofia Vallila-Rohter [R21 DC019203] and Yael Arbel [R15 DC016438]. A portion of Kristen Nunn’s time during manuscript preparation and revision was supported by the Department of Veterans Affairs Office of Academic Affiliations, Advanced Fellowship in Geriatrics, of the Veterans Affairs Pittsburgh Health Care System, and the Department of Veterans Affairs Pittsburgh Geriatric Research, Education, and Clinical Center (GRECC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health or the VA.
                Categories
                Subject: Aging Neuroscience
                Subject: Original Research
                Custom metadata
                section-at-acceptance Neurocognitive Aging and Behavior

                ScienceOpen disciplines: Neurosciences
                Keywords: feedback,timing,event-related potentials,aging,category learning
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: feedback, timing, event-related potentials, aging, category learning

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