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Record W4294125883 · doi:10.1037/emo0001147

Episodic memory through a social and emotional lens.

2022· article· en· W4294125883 on OpenAlexafffund
Charlotte I Stewardson, Michelle C. Hunsche, Victoria Wardell, Daniela J. Palombo, Connor M. Kerns

Bibliographic record

VenueEmotion · 2022
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsPsychologyPsycINFOCognitive psychologyContent-addressable memoryMemory errorsAssociative propertyEncoding (memory)Episodic memoryAffect (linguistics)Social cognitionRecognition memoryCognitionRecallCommunicationNeuroscienceComputer science

Abstract

fetched live from OpenAlex

= 706) were recruited. Stimuli included (1) images with varying social and emotional content categorized into four conditions: negative social, negative nonsocial, neutral social, neutral nonsocial and (2) neutral images of objects paired with target stimuli to assess associative memory. Participants viewed the image pairings (Encoding). Twenty-four hours later, item and associative recognition memory were tested. Item recognition memory was better for negative vs. neutral and social vs. nonsocial images. By contrast, associative recognition memory was worse for negative vs. neutral, but better for social vs. nonsocial images. Finally, women demonstrated similar item memory performance compared to men but better associative recognition memory performance overall compared to men. Emotional and social cues impart distinct effects on how we form holistic episodic memories, highlighting the importance of considering these critical factors when striving to understand how and what we remember. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0310.005

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 head0.049
GPT teacher head0.323
Teacher spread0.274 · 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 designTheoretical or conceptual
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

Citations15
Published2022
Admission routes2
Has abstractyes

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