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Distributed Representations in Memory: Insights from Functional Brain Imaging
Open accessAbstract
Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly utilized powerful analytical tools (e.g., multivoxel pattern analysis) to decode the information represented within distributed functional magnetic resonance imaging activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses.
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“So-called subsequent memory paradigms [141] have yielded several markers that predicted the later voluntary recollection of information [142], or the involuntary retrieval of experimental trauma [143–145].”
From Multiple memory systems, multiple time points: How science can inform treatment to control the expression of unwanted emotional memories (2018) · cites this paper as [142]
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Attention to Distinct Goal-relevant Features Differentially Guides Semantic Knowledge Retrieval
Hanson, G. K., & Chrysikou, E. G. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01121 · Cited by 5
“…or the context in which it is typically used) that match task-relevant attentional priorities, and the representation of which is supported by the selective activation of particular neuronal ensembles within a distributed semantic memory system (Rissman & Wagner, 2012; Patterson et al., 2007).”
BackgroundPetrican, R., & Levine, B. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.057 · Cited by 5
“The processes underlying the ability to create, maintain and retrieve a mental record of specific past events, commonly referred to as episodic memory (EM), have been the topic of substantial empirical inquiry (Bird, 2017; Nadel et al., 2012; Rissman and Wagner, 2012).”
Price, M. H., & Johnson, J. D. (2018) · Brain Research · doi:10.1016/j.brainres.2018.06.031 · Cited by 5
Synesthetic Binding and the Reactivation Model of Memory
Brogaard, B. “. (2017) · Oxford University Press eBooks · doi:10.1093/oso/9780199688289.003.0007 · Cited by 5
Emotional influences on remembering and forgetting explained by frontal and parietal dynamics
Chen, Z., Wang, L., Ying, S., Yuan, J., Ren, J., Ye, Y., Qin, Y. F., Liu, T., & Yao, D. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00484.2024 · Cited by 4
Decentralized Spatially Constrained Source-Based Morphometry
Saha, D. K., Silva, R. F., Baker, B. T., & Calhoun, V. D. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761419 · Cited by 4
“In the past few years, there has been extensive research to leverage data across multiple sites [11, 12, 13, 14, 15].”
BackgroundMethodSaha, D. K., Silva, R. F., Baker, B. T., Saha, R., & Calhoun, V. D. (2023) · Human Brain Mapping · doi:10.1002/hbm.26483 · Cited by 4
“In recent years, considerable work has been focused on leveraging decentralized computation for neuroimaging analysis (Bostami et al., 2021; Plis et al., 2016; Rissman & Wagner, 2012; D. K. Saha et al., 2017; D. K. Saha, Calhoun, et al., 2022).”
BackgroundDecoding context memories for threat in large-scale neural networks
Crombie, K. M., Azar, A., Botsford, C., Heilicher, M. O., Jaeb, M. A., Gruichich, T. S., Schomaker, C. M., Williams, R., Stowe, Z. N., Dunsmoor, J. E., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae018 · Cited by 4
Critical Concepts in the Study of Learning and Memory
Roediger, H. L., III, & Üner, Ö. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190917982.013.1 · Cited by 4
Woroch, B. S., Konkel, A., & Gonsalves, B. D. (2019) · AIMS neuroscience · doi:10.3934/neuroscience.2019.4.250 · Cited by 4
Thiele, J. E., Haaf, J. M., & Rouder, J. N. (2017) · doi:10.31234/osf.io/auf9n · Cited by 4
Speech and music recruit frequency-specific distributed and overlapping cortical networksPreprint
Rietmolen, N. T., Mercier, M. R., Trébuchon, A., Morillon, B., & Schön, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.08.511398 · Cited by 4
“Abbreviations according to the Brainnetome Atlas (Fan et al., 2016).”
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Methodological advice for the young at heart investigator: Triangulation to build better foundations
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Sarıcaoğlu, M., Yücel, M. A., Budak, M., Omurtag, A., & Hanoğlu, L. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121062 · Cited by 5
“…the hippocampus, Parahippocampal, Temporal, Ventral Pa-rietal, DLPFC, and Ventrolateral Prefrontal cortex (VLPFC) regions are involved in answering general knowledge questions (Rissman and Wagner, 2012; Woodward et al., 2006; Kuhl and Chun, 2014; Jahani et al., 2017; Berglund-Barraza et al., 2019).”
Encoding and the Medial Temporal Lobe
Fernandez, C., Madore, K. P., & Wagner, A. D. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190917982.013.23 · Cited by 3
Self-citationAn in-depth Exploration of the Interplay between fMRI Methods and Theory in Cognitive Neuroscience
Huffman, D. J. (2024) · Journal of Undergraduate Neuroscience Education · doi:10.59390/zabm1739 · Cited by 3
A Cognitive Neuroscience Perspective on Language Comprehension in Context
Berkum, J. J. A. V., & Nieuwland, M. S. (2019) · The MIT Press eBooks · doi:10.7551/mitpress/10841.003.0037 · Cited by 3
St‐Laurent, M., Rosenbaum, R. S., Olsen, R. K., & Buchsbaum, B. R. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107436 · Cited by 3
Smith, M. S., & Truscott, J. (1920) · Cambridge University Press eBooks · doi:10.1017/cbo9781139644044 · Cited by 3
Pallares, V., Insabato, A., Sanjuán, A., Kühn, S., Mantini, D., Deco, G., & Gilson, M. (2018) · Max Planck Digital Library · Cited by 3
Neuroscience-inspired continuous learning: a sustainable approach to AI energy challengePreprint
Xiao, D. (2023) · doi:10.31219/osf.io/twn9q · Cited by 3
Graph-like organization of non-spatial knowledge about social closeness in movie narratives
Santacroce, F., Committeri, G., Matteo, R. D., Censo, D. D., Sestieri, C., & Tosoni, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-17427-x · Cited by 2
“Furthermore, repeated episodic exposure can reduce potential ambiguities arising from previous poor coding and promote the integration of new information into existing schemas, a process likely mediated, at a neural level, by hippocampal-neocortical interactions 61,62 .”
Lu, H., Chan, S. S. M., Fung, A. W. T., & LAM, L. C. W. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161122 · Cited by 2
“Just asourprecedingresultsimplicated,thecomponents ofIPS,asacognitivecapacity[50],mightcontribute totheattention[51–53]anddepthofprocessing[54] duringepisodicencoding(i.e.,delayedrecall).”
Hierarchical Reasoning with Distributed Vector Representations
Kommers, C., Ustun, V., Demski, A., & Rosenbloom, P. S. (2015) · eScholarship (California Digital Library) · Cited by 2
Semantic Vector Spaces for Broadening Consideration of Consequences
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Metadata
- Authors
- Rissman, J., & Wagner, A. D.
- Affiliations
- University of California, Los Angeles (US)
- Stanford University (US)
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2012-01-10
- Volume
- 63
- Issue
- 1
- Pages
- 101-128
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- PMC
- PMC4533899
- OpenAlex
- W2141815738
- Cited by
- 299
- References
- 179
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 19 · 2023: 14 · 2022: 17 · 2021: 29 · 2020: 11 · 2019: 20 · 2018: 23 · 2017: 27 · 2016: 17 · 2015: 28 · 2014: 17 · 2013: 19 · 2012: 13
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Psychology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5r01mh080309-04)
- National Institute of Mental Health (R01-MH080309, 5R01MH080309, R01MH076932)
- National Institute of Neurological Disorders and Stroke (F32NS059195)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
- Keywords
- distributed representations, memory, neuronal ensembles, multi-voxel pattern analysis, memory-guided behavior, memory encoding, memory retrieval, perceptual content, semantic content, memory theory, functional MRI
- MeSH terms
- Brain, Brain Mapping, Humans, Memory, Short-Term, Functional Neuroimaging, Memory, Episodic
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