Auditory Cues 123 Auditory Recognition Cues in Word Memorization & Recall
Bibliographic record
Abstract
When testing working memory, attention can be split between auditory and visual stimulus (Morey et al., 2011). Working memory is often tested with either a word recall or word recognition test, and word recognition has been shown to produce significantly higher scores (Lachman & Forsberg, 1981). I f an auditory stimulus contained the words of a recall test and was played during that test, scores should improve because the subject has a basis for recognition. Thirty-two undergrad students at the University of Western Ontario were given a word list recall test made of words fi-om the song " I f I Had $1000000 " by The Barenaked Ladies. Subjects were then tested under one of four experimental conditions: playing the song during study, recall, both, or neither. Results were not significant for any effect. Possible reasons and suggestions for further research are discussed within. Working memory is influenced by many factors. Attention plays a significant role in working memory, and as Morey, Cowan, Morey and Rouder (2011) have shown, attention can be manipulated through reward. In demonstrating the manipulation of attention, Morey et al. (2011) show that attention can be split, and also distinguish between auditory and visual attention. The question that remains, however, is how would working memory be affected i f attention to visual and auditory stimulus were used for the same task? Two of the most frequently used tests of working memory are the Word Recall and Word Recognition tests. Past research by Lachman and Forsberg (1981) has used both of these tests on subjects and found that recognition test scores are significantly higher than recall test scores. But what i f the word recall test involved recognition?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".