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Record W2041535583 · doi:10.3389/fnagi.2012.00025

Remote spatial memory in aging: all is not lost

2012· article· en· W2041535583 on OpenAlexafffund
R. Shayna Rosenbaum, Gordon Winocur, Malcolm A. Binns, Morris Moscovitch

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2012
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsTrent UniversityBaycrest HospitalPublic Health OntarioUniversity of TorontoYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsEpisodic memoryPsychologyCognitive psychologySpatial memorySpatial learningTest (biology)Cognitive agingSemantic memoryDevelopmental psychologyCognitionNeuroscienceWorking memory

Abstract

fetched live from OpenAlex

The ability to acquire and retain spatial memories in order to navigate in new environments is known to decline with age, but little is known about the effect of aging on representations of environments learned long ago, in the remote past. To investigate the status of remote spatial memory in old age, we tested healthy young and older adults on a variety of mental navigation tests based on a large-scale city environment that was very familiar to participants but rarely visited by the older adults in recent years. We show that whereas performance on a route learning test of new spatial learning was significantly worse in older than younger adults, performance was comparable or better in the older adults on mental navigation tests based on a well-known environment learned long ago. An exception was in the older adults' ability to vividly re-experience the well-known environment, and recognize and represent the visual details contained within it. The results are seen as analogous to the pattern of better semantic than episodic memory that has been found to accompany healthy aging.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.252
Teacher spread0.228 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations69
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Aging NeuroscienceSame topicSpatial Cognition and NavigationFrench-language works237,207