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Record W2056736143 · doi:10.5539/ass.v8n10p58

Emotions as Intermediaries for Implicit Memory Retrieval Processing: Evidence Using Word and Picture Stimuli

2012· article· en· W2056736143 on OpenAlexvenueno aff
Rozainee Khairudin, G. M. Valipour, Rohany Nasir, A. Z. Zainah

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyValence (chemistry)Implicit memoryCognitive psychologyPerceptionEmotional valenceNegative informationCognition

Abstract

fetched live from OpenAlex

The significance of emotions are seldom the focus of studies especially those concerning implicit memory. As a result, little is known about the effects of emotions on such memory. In two experiments, perceptual identification test was used to investigate the effects of emotional words and pictures on implicit memory. In Experiment 1, participants viewed lists of positive, negative and neutral words and in Experiment 2, participants saw lists of positive, negative and neutral pictures. Perceptual identification test was conducted after a 30 minute interval. Results showed that participants remembered better on implicit memory when information was with positive valence rather than negative valence: positive pictures and words were remembered more than negative pictures and words. However, the difference in types of information only emerged when the valence was positive. In this case, participants had an advantage for words over pictures only when these were presented with positive emotions, not with negative ones. The findings provide evidence for the significant mediating role of valence on implicit memory retrieval processes.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
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 score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.097
GPT teacher head0.384
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2012
Admission routes1
Has abstractyes

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