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Record W4247022962 · doi:10.32920/ryerson.14662080.v1

Effects of aging and emotional valence on item directed forgetting and source attributions

2021· preprint· en· W4247022962 on OpenAlexaff
Sara N. Gallant

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of WindsorToronto Metropolitan University
Fundersnot available
KeywordsForgettingMotivated forgettingPsychologyAttributionValence (chemistry)Developmental psychologyEmotional valenceYoung adultCognitive psychologyAudiologyCognitionSocial psychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Two experiments investigated the effects of aging and emotion on intentional forgetting. Experiment 1 compared 36 young (aged 18-28, M = 20.22, SD = 3.12) and 36 older adults (aged 65-85, M = 71.53, SD = 5.44) on item directed forgetting and source attributions (i.e., assigning a 'remember', 'forget', or 'new' tag during recognition) of positive, negative, and neutral words. Older adults' directed forgetting was reduced for positive words and their source attributions were differentially affected by emotion. Emotion had no effect on young adults' performance. Experiment 2 examined the role of attention in older adults' intentional forgetting. Thirty-six older adults (aged 65-91, M = 73.92, SD = 7.55) completed an emotional item directed forgetting task that incorporated a probe-detection task during encoding to assess the allocation of attention across valence conditions. Older adults again showed reduced directed forgetting for positive words and emotional effects in source attributions; however, results from the probe-detection task indicated the older adults' attention may not have been influenced by the emotional tone of stimuli during encoding.

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)0.002
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.278
Teacher spread0.249 · 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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