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Record W4407964469 · doi:10.1080/02699931.2024.2421395

Emotional time travel: the role of emotion in temporal memory

2025· editorial· en· W4407964469 on OpenAlexaff
Deborah Talmi, Daniela J. Palombo

Bibliographic record

VenueCognition & Emotion · 2025
Typeeditorial
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyCognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

emotional experiences occurred can be adaptive, yet there is no consensus on how emotion influences temporal aspects of memory. Temporal memory, a type of associative memory, refers to the capacity to encode, store, and retrieve information about the sequence and timing of events. This Special Issue presents evidence on how emotion affects three aspects of temporal memory: temporal-order, temporal source, and event segmentation. The contributions suggest that emotion often increases temporal-order memory, a result that is harder to reconcile with some dominant emotional memory theories, including the Object-Based Framework, the Dual Representation Account or other trade-off models, but may fit with Arousal-Biased Competition theory. The contributions also suggest that emotion can act as a boundary between events, although only under some experimental set-ups. Findings regarding its effect on temporal source memory were less clear. We discuss the diversity of findings in light of theories of emotional associative memory and methodological factors, such as the direction of the shift in emotional experience and discrepancies between temporal-order and temporal distance measures as indices of event boundaries. We provide a roadmap for future studies aimed at understanding how emotion shapes the fate of our memories as they unfold in time.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.288
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations9
Published2025
Admission routes1
Has abstractyes

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