Emotion and Explicit Verbal Memory: Evidence Using Malay Lexicon
Bibliographic record
Abstract
The dichotomy of emotional system and cognitive system has been debated for centuries. As a result, there is still much to know about the effects of emotions on memory. In two experiments, emotion was manipulated to investigate its effect on recall and recognition of words. Experiment 1 used a recognition test and experiment 2 used a free recall test. Emotion was manipulated in word stimuli: positive words, negative words and neutral words (baseline condition). Malay words were used as the verbal stimuli. The valences of the words were taken from ANEW. Results revealed that memory was better on the recognition test than recall test. Emotion had a significant effect on memory performance where both positive and negative emotions improved memory when compared to the baseline condition. The implication is that Malay words elicit a different result on explicit memory when emotional content of the words was manipulated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".