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Record W2793568094 · doi:10.1002/wcs.1462

Spatial scaffold effects in event memory and imagination

2018· review· en· W2793568094 on OpenAlexaff
Jessica Robin

Bibliographic record

VenueWiley Interdisciplinary Reviews Cognitive Science · 2018
Typereview
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsBaycrest Hospital
Fundersnot available
KeywordsEpisodic memoryRetrosplenial cortexPrecuneusNeuroscienceSpatial memoryPosterior cingulateSpatial contextual awarenessContext (archaeology)PsychologyParahippocampal gyrusHippocampusCognitive psychologySemantic memorySpatial cognitionCognitionTemporal lobeWorking memoryComputer scienceBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Spatial context is a defining feature of episodic memories, which are often characterized as being events occurring in specific spatiotemporal contexts. In this review, I summarize research suggesting a common neural basis for episodic and spatial memory and relate this to the role of spatial context in episodic memory. I review evidence that spatial context serves as a scaffold for episodic memory and imagination, in terms of both behavioral and neural effects demonstrating a dependence of episodic memory on spatial representations. These effects are mediated by a posterior-medial set of neocortical regions, including the parahippocampal cortex, retrosplenial cortex, posterior cingulate cortex, precuneus, and angular gyrus, which interact with the hippocampus to represent spatial context in remembered and imagined events. I highlight questions and areas that require further research, including differentiation of hippocampal function along its long axis and subfields, and how these areas interact with the posterior-medial network. This article is categorized under: Psychology > Memory Neuroscience > Cognition.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.103
GPT teacher head0.421
Teacher spread0.318 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations100
Published2018
Admission routes1
Has abstractyes

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