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Record W4238698139 · doi:10.32920/ryerson.14649834

An examination of Emotion Regulation strategy use on recognition memory

2021· preprint· en· W4238698139 on OpenAlexaff
Beverley K. Fredborg

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsToronto Metropolitan UniversityUniversity of WinnipegSystems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsSurprisePsychologyEncoding (memory)Task (project management)Cognitive psychologyTest (biology)Emotional memorySocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Emotion regulation (ER) refers to the use of various strategies to modify an emotional response and has important implications for memory of emotional events. During this study, participants were instructed to first enhance, maintain, or suppress their emotional responses while viewing unpleasant and neutral images and then report the ER strategies they used during the task. A surprise memory test for these images known as the Remember/Know procedure was then conducted immediately after encoding and following a one-week delay. Overall, negative images were better remembered than neutral images. Moreover, images paired with the instruction to enhance one’s emotional responses were better remembered than images paired with the instruction to maintain or suppress, on the first test day only. Specific types of ER strategies used were not reliably associated with memory for emotional images. This research is the first to inform of the impact of spontaneous ER strategy use on 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 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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.150
GPT teacher head0.321
Teacher spread0.171 · 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 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
Published2021
Admission routes1
Has abstractyes

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