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Record W2794794525 · doi:10.1101/289447

Cortical thickness in right frontal and left lingual gyri differentially mediate episodic memory for spatial contextual details across the adult lifespan

2018· preprint· en· W2794794525 on OpenAlexaff
A. Swierkot, M. Natasha Rajah

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsEpisodic memoryContext (archaeology)PsychologyAutobiographical memoryNeuroscienceSemantic memoryCognitionBiology

Abstract

fetched live from OpenAlex

Abstract Healthy aging is associated with declines in episodic memory and with widespread cortical thinning. These parallel declines suggest that age-related changes in cortical thickness may contribute to episodic memory decline with age. The current study uses a cross-sectional study design to examine whether regional cortical thickness mediates the relationship between age and episodic memory, as measured by a context memory task for faces. Mediation and conditional mediation models were tested using bootstrapping in order to determine how age-associated changes in regional cortical thickness mediated age-associated changes in performance on the context memory task. We observed that right superior frontal cortical thickness conditionally mediated spatial context memory only in middle-aged and older adults; and right caudal middle frontal cortical thickness conditionally mediated context memory only in older adults. Left lingual cortical thickness mediated spatial context memory across the adult lifespan, but this effect was most evident at midlife. Right parahippocampal cortical thickness mediated context memory, independent of age. We conclude that our cortical thickness results were generally consistent with the posterior-to-anterior shift in aging hypothesis (Davis et al., 2008) for episodic memory.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.262
Teacher spread0.243 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations3
Published2018
Admission routes1
Has abstractyes

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