Repetition priming and repetition suppression: a case for enhanced efficiency through neural synchronization
Open accessAbstract
Stimulus repetition in identification tasks leads to improved behavioral performance ("repetition priming") but attenuated neural responses ("repetition suppression") throughout task-engaged cortical regions. While it's clear that this pervasive brain-behavior relationship reflects some form of improved processing efficiency, the exact form that it takes remains elusive. In this Discussion Paper, we review four different theoretical proposals that have the potential to link repetition suppression and priming, with a particular focus on a proposal that stimulus repetition affects improved efficiency through enhanced neural synchronization. We argue that despite exciting recent work on the role of neural synchronization in cognitive processes such as attention and perception, similar studies in the domain of learning and memory - and priming, in particular - have been lacking. We emphasize the need for new studies with adequate spatiotemporal resolution, formulate several novel predictions, and discuss our ongoing efforts to disentangle the current proposals.
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The neurocomputational mechanisms to realize predictive coding theory are still in debate.
“However, the neurocomputational mechanisms to realize predictive coding theory are still in debate (den Ouden et al., 2012; Eriksson et al., 2012; Gotts et al., 2012; Kok et al., 2012a; Clark, 2013a,b; Rauss and Pourtois, 2013; Summerfield and de Lange, 2014).”
From Is predictive coding theory articulated enough to be testable? (2015) · cites this paper as Gotts et al., 2012
In “Bayesian Networks and ‘Explaining Away’” (page unavailable), the paper describes a prediction-error account and says future experiments must distinguish it from the Synchrony model. That supports uncertainty about the mechanisms of repetition suppression, but the cited discussion does not extend the debate to how predictive coding theory is realized generally. (LLM, conf=0.82)
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Repetition suppression and its contextual determinants in predictive coding
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- Authors
- Gotts, S. J., Chow, C. C., & Martin, A.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Gotts, Stephen J.
- Venue
- Cognitive Neuroscience
- Publisher
- Informa UK Limited
- Published
- 2012-03-26
- Volume
- 3
- Issue
- 3-4
- Pages
- 227-237
- ISSN
- 1758-8928 (ISSN-L)
- 1758-8936
- PMC
- PMC3491809
- OpenAlex
- W2014863934
- Cited by
- 213
- References
- 198
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 7 · 2023: 8 · 2022: 8 · 2021: 12 · 2020: 14 · 2019: 40 · 2018: 17 · 2017: 29 · 2016: 16 · 2015: 21 · 2014: 18 · 2013: 7 · 2012: 6
- FWCI
- 8.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 MH999999)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- repetition priming, repetition suppression, neuronal synchronization, stimulus repetition, processing efficiency, neural oscillations, learning and memory, brain-behavior relationship, spatiotemporal resolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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