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Record W4234866094 · doi:10.31234/osf.io/e2unt

Linking detail to temporal structure in naturalistic event recall

2019· preprint· en· W4234866094 on OpenAlexaff
Nicholas B. Diamond, Brian Levine

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsRecallEpisodic memoryContiguityContext (archaeology)Cognitive psychologyPsychologyContext-dependent memoryAutobiographical memoryRecall testFree recallEvent (particle physics)Encoding (memory)Serial position effectDynamics (music)Computer scienceCognitionHistoryNeuroscience

Abstract

fetched live from OpenAlex

Decades of memory research demonstrate the importance of temporal organization in recall dynamics, using laboratory stimuli (i.e. word lists) at seconds-to-minutes-long delays. Little is known, however, about such organization in recall of richer and more remote real-world experiences, where the focus is usually on memory content without reference to event order. Here, 119 younger and older adults freely recalled extended real-world experiences, for which the encoding sequence was controlled, after two days or one week. We paired analytical tools from the list-learning and autobiographical memory literatures to measure spontaneous contextual dynamics and details in these recall narratives. Recall dynamics were organized by temporal context (contiguity and forward asymmetry), and organization was reduced in older age, despite similar recall initiation and serial position effects across age groups. Across participants, organization was positively associated with episodic detail richness, providing evidence for a link between re-experiencing past events and reinstating their spatiotemporal context.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.048
GPT teacher head0.319
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2019
Admission routes1
Has abstractyes

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