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Record W4407089377 · doi:10.31219/osf.io/rw49j_v1

Similarity is Associated With Where Repeated Events Fall on the Semantic-Episodic Continuum

2023· preprint· en· W4407089377 on OpenAlexaff
Oliver Bontkes, Daniela J. Palombo, Eva Rubínová

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSimilarity (geometry)Semantic similarityPsychologyNatural language processingCognitive psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Memories of repeated events are one form of memory thought to be intermediate on a proposed semantic-episodic continuum. However, it is not yet understood where repeated event memories fall on this continuum, and which factors may be associated with greater (or lesser) reliance on episodic and semantic memory during recall. We investigated similarity amongst instances of repeated events as one factor which may be associated with where repeated events fall on the semantic-episodic continuum. In two preregistered studies we asked participants to recall three repeated event memories from their own lives (N1 = 97 participants, 291 memories; N2 = 419 participants, 1257 memories) and report on the similarity amongst instances as well as the degree to which they relied on semantic memory, a single episode, and a mix of episodes in their recall of each event. In line with our predictions, similarity was positively correlated with reliance on semantic memory in both studies. In Study 2, similarity was negatively correlated with reliance on a single episode. We also conducted exploratory latent profile analyses using our three memory reliance variables, revealing three types of repeated event memories. In both studies, similarity of place and emotional arousal were each associated with different memory profiles. Our findings highlight similarity as a factor associated with diversity amongst repeated event memories, which has key theoretical implications and practical significance in legal and clinical contexts.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.057
GPT teacher head0.271
Teacher spread0.214 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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