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Record W4244282506 · doi:10.31234/osf.io/jrtcg

Positive emotion enhances association-memory

2018· preprint· en· W4244282506 on OpenAlexaff
Christopher R. Madan, Sarah Scott, Elizabeth A. Kensinger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAssociation (psychology)ArousalPsychologyRecallValence (chemistry)Cued recallCognitive psychologyCued speechImplicit memoryCognitionDevelopmental psychologyFree recallNeuroscience

Abstract

fetched live from OpenAlex

The influence of emotion on association-memory is often attributed to arousal, but negative stimuli are typically used to test for these effects. While prior studies of negative emotion on association-memory have found impairments, theories suggest that positive emotion may have a distinct effect on memory, and may lead to enhanced association-memory. Here we tested participants’ memory for pairs of positive and neutral words using cued recall, supplemented with a mathematical modeling approach designed to disentangle item- versus association-memory effects that may otherwise confound cued-recall performance. We consistently found enhanced association-memory due to positive emotion. These results provide further evidence that positive information is processed differently than negative and that, when examining association formation, valence as well as arousal must be considered.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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