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Record W2069707236 · doi:10.1371/journal.pone.0062750

Effects of Regulating Positive Emotions through Reappraisal and Suppression on Verbal and Non-Verbal Recognition Memory

2013· article· en· W2069707236 on OpenAlexaff
Catherine N. M. Ortner, Monica de Koning

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsPsychologyCognitive reappraisalCognitionRecognition memoryVerbal memoryCognitive psychologyExpressive SuppressionNonverbal communicationDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

Previous research has suggested that regulating emotions through reappraisal does not incur cognitive costs. However, in those experiments, cognitive costs were often assessed by recognition memory for information that was contextually related to the emotionally evocative stimuli and may have been incorporated into the reappraisal script, facilitating memory. Furthermore, there is little research on the cognitive correlates of regulating positive emotions. In the current experiment, we tested memory for information that was contextually unrelated to the emotional stimuli and could not easily be related to the reappraisal. Participants viewed neutral and mildly positive slides and either reappraised, suppressed their emotions, or viewed the images with no emotion regulation instruction. At the same time, they heard abstract words that were unrelated to the picture stimuli. Subsequent verbal recognition memory was lower after reappraising than viewing, whereas non-verbal recognition memory (of the slides) was higher after reappraising, but only for positive pictures and when participants viewed the positive pictures first. Suppression had no significant effect on either verbal or non-verbal recognition scores, although there was a trend towards poorer recognition of verbal information. The findings support the notion that reappraisal is effortful and draws on limited cognitive resources, causing decrements in performance in a concurrent memory task.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.384

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.258
Teacher spread0.217 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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