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Record W2124529404 · doi:10.1162/0898929053279478

Overlap in the Functional Neural Systems Involved in Semantic and Episodic Memory Retrieval

2005· article· en· W2124529404 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSemantic memoryEpisodic memoryPsychologyCognitive psychologyCognitive neuroscienceCognitionNeuropsychologyReconstructive memoryFunctional neuroimagingCognitive scienceNeuroscienceExplicit memory

Abstract

fetched live from OpenAlex

Abstract Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems [Cabeza, R., & Nyberg, L. Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12, 1–47, 2000; Gabrieli, J. D. Disorders of memory in humans. Current Opinion in Neurology and Neurosurgery, 6, 93–97, 1993; Gabrieli, J. D. Cognitive neuroscience of human memory. Annual Review of Psychology, 49, 87–115, 1998; Squire, L. R. The organization and neural substrates of human memory. International Journal of Neurology, 21–22, 218–222, 1987; Squire, L. R., & Zola, S. M. Structure and function of declarative and nondeclarative memory systems. Proceedings of the National Academy of Sciences, U.S.A., 93, 13515–13522, 1996; Tulving, E. Multiple memory systems and consciousness. Human Neurobiology, 6, 67–80, 1987]. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks [Duzel, E., Cabeza, R., Picton, T. W., Yonelinas, A. P., Heinze, H. J., Scheich, H., & Tulving, E. Task-related and item related processes in episodic and semantic retrieval: A combined PET and ERP study. Proceedings of the National Academy of Sciences, U.S.A., 96, 1794–1799, 1999]. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may reflect variation along a continuum of processing during task performance within the context of a single memory system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.293
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it