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Record W2119488082 · doi:10.1027/1614-0001/a000158

Individual Differences in Anxiety Influence Verbal Memory Accuracy and Confidence

2015· article· en· W2119488082 on OpenAlexaff
Bethany R. Delleman, Myra A. Fernandes

Bibliographic record

VenueJournal of Individual Differences · 2015
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyEncoding (memory)AnxietyDevelopmental psychologyConfidence intervalRecognition memoryLow ConfidenceAudiologyCognitive psychologyCognitionSocial psychologyStatisticsNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

We examined the influence of encoding duration in high- and low-anxious undergraduates on memory accuracy and confidence. Participants encoded words for 750 or 4,000 ms, and later made recognition and confidence judgments in their memory for targets and lures. The high-anxious had poorer memory accuracy than the low-anxious group, and endorsed lower confidence specifically for correct memory responses. There was no differential effect of encoding time across groups, though longer encoding time benefited both accuracy and confidence. As accuracy increased so did confidence in the low-anxious group, indicating higher resolution and Gamma correlations for meta-memory judgments, though this was not the case for high-anxious individuals. Results indicate that people with high levels of anxiety have unrealistically low confidence in their memories as their confidence was a poor predictor of accuracy and allowing additional encoding time does not alleviate this effect.

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)0.008
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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 integrity0.0000.001
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 head0.095
GPT teacher head0.307
Teacher spread0.212 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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