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Neural Dynamics Underlying Repeated Learning of Visual Image Sequences.

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Title: Neural Dynamics Underlying Repeated Learning of Visual Image Sequences.
Authors: Zhao, Chong1 (AUTHOR) chongzhao@uchicago.edu, Kim, Audrey1 (AUTHOR), Campos, Leyla1 (AUTHOR), Vogel, Edward K.1 (AUTHOR)
Source: Journal of Cognitive Neuroscience. Jun2026, Vol. 38 Issue 6, p1185-1195. 11p.
Subjects: Repetition (Learning process), Evoked potentials (Electrophysiology), Memory, Short-term memory, Recognition (Psychology), Neurosciences, Visual perception
Abstract: Humans possess a remarkable ability to recognize visual objects with high fidelity, supported by complex neural mechanisms underlying memory retrieval. ERP studies have identified two key neural signatures of recognition memory: the parietal old/new effect and the frontal old/new effect. Despite extensive research on these ERP components, the extent to which these components reflect distinct memory processes remains debated. In the present study, we investigated how repetitive learning modulates these ERP components. Participants repeatedly studied a fixed list of 32 real-world images across up to five study-test repetitions while EEG was recorded. In addition, a separate set size 1 condition served as a proxy for working memory. Our results showed that with increased repetitions, the parietal old/new effect exhibited enhanced amplitude and earlier peak latency, reflecting more efficient retrieval of well-learned memories. In contrast, the frontal old/new effect remained unchanged in both amplitude and timing. These findings suggest that the parietal old/new effect is a sensitive neural marker of learning-related changes in long-term memory representations, whereas the frontal effect is less influenced by repetition. In addition, despite similarly high accuracy between the well-practiced set size 32 condition and the set size 1 working memory condition, both parietal and frontal old/new effects peaked significantly earlier for set size 1, suggesting that access to working memory is substantially faster than even well-practiced long-term memory. Together, our results highlight the unique role of the parietal old/new effect, but not the frontal old/new effect, in repetitive learning, despite both components being important for successful recognition of learned visual stimuli. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 194131766
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Neural Dynamics Underlying Repeated Learning of Visual Image Sequences.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Cognitive+Neuroscience%22">Journal of Cognitive Neuroscience</searchLink>. Jun2026, Vol. 38 Issue 6, p1185-1195. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Repetition+%28Learning+process%29%22">Repetition (Learning process)</searchLink><br /><searchLink fieldCode="DE" term="%22Evoked+potentials+%28Electrophysiology%29%22">Evoked potentials (Electrophysiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Memory%22">Memory</searchLink><br /><searchLink fieldCode="DE" term="%22Short-term+memory%22">Short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Recognition+%28Psychology%29%22">Recognition (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink>
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  Data: Humans possess a remarkable ability to recognize visual objects with high fidelity, supported by complex neural mechanisms underlying memory retrieval. ERP studies have identified two key neural signatures of recognition memory: the parietal old/new effect and the frontal old/new effect. Despite extensive research on these ERP components, the extent to which these components reflect distinct memory processes remains debated. In the present study, we investigated how repetitive learning modulates these ERP components. Participants repeatedly studied a fixed list of 32 real-world images across up to five study-test repetitions while EEG was recorded. In addition, a separate set size 1 condition served as a proxy for working memory. Our results showed that with increased repetitions, the parietal old/new effect exhibited enhanced amplitude and earlier peak latency, reflecting more efficient retrieval of well-learned memories. In contrast, the frontal old/new effect remained unchanged in both amplitude and timing. These findings suggest that the parietal old/new effect is a sensitive neural marker of learning-related changes in long-term memory representations, whereas the frontal effect is less influenced by repetition. In addition, despite similarly high accuracy between the well-practiced set size 32 condition and the set size 1 working memory condition, both parietal and frontal old/new effects peaked significantly earlier for set size 1, suggesting that access to working memory is substantially faster than even well-practiced long-term memory. Together, our results highlight the unique role of the parietal old/new effect, but not the frontal old/new effect, in repetitive learning, despite both components being important for successful recognition of learned visual stimuli. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1162/JOCN.a.2426
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        Text: English
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      – SubjectFull: Evoked potentials (Electrophysiology)
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      – SubjectFull: Memory
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      – SubjectFull: Short-term memory
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      – SubjectFull: Recognition (Psychology)
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      – SubjectFull: Neurosciences
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      – SubjectFull: Visual perception
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      – TitleFull: Neural Dynamics Underlying Repeated Learning of Visual Image Sequences.
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            NameFull: Zhao, Chong
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            NameFull: Kim, Audrey
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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