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Record W2155380400 · doi:10.1073/pnas.0506308103

Emotion enhances remembrance of neutral events past

2006· article· en· W2155380400 on OpenAlexafffund
Adam K. Anderson, Peter E. Wais, John D. E. Gabrieli

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2006
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersCanada Research Chairs
KeywordsArousalRecallPsychologyCognitive psychologyRetrograde amnesiaEpisodic memoryMemory consolidationEncoding (memory)Emotional memoryCognitionNeuroscienceAmygdalaAmnesiaHippocampus

Abstract

fetched live from OpenAlex

Emotional events are bestowed with special prominence in memory. This may reflect greater attention oriented to these events during encoding, and/or enhancement of memory consolidation after emotional events have passed. Here we show invoked emotional arousal results in a retrograde enhancement of long-term memory, determining what will later be remembered or forgotten. Subjects saw pictures of neutral faces and houses followed by emotionally arousing scenes at varying intervals. Self-reported emotional arousal responses predicted a retrograde enhancement of memory for preceding neutral events in a 1-week delayed recognition memory test. At longer picture-scene intervals, no enhancement was found, implicating a critical window in which emotional arousal must occur for retrograde memory enhancement. Postencoding manipulation of emotional arousal specifically enhanced conscious recollection rather than familiarity-based discrimination. An additional study revealed no retrograde enhancement for pictures preceding highly memorable, but nonarousing, distinctive scenes. These findings indicate an important role for emotional arousal in the postencoding enhancement of episodic memory consolidation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.309
Teacher spread0.259 · 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

Citations284
Published2006
Admission routes2
Has abstractyes

Explore more

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