Lion—Tiger—Stripes: Delimiting the Semantic Association Effect on Working Memory With Mediated Association
Bibliographic record
Abstract
Semantic relatedness of items improves working memory performance. We targeted one type of semantic relatedness, semantic association. The beneficial effect of association is often explained by the spreading-activation process: Encoding/maintaining an item would activate its associated item. Nevertheless, as associated words can be similar to each other (e.g. similarity based on the Latent Semantic Analysis), other processes, rather than spreading activation, may also explain the association effect. To target spreading activation selectively, a novel hypothesis unique to this process was tested. Specifically, we tested the effect of mediated or two-step association, which the spreading-activation theory assumes (e.g. lion → tiger → stripes). To examine the mediated association effect, Experiments 1, 2A, and 2B presented word pairs with indirect association (e.g. "lion" and "stripes") without mediators (e.g. "tiger"). Only one of the three experiments provided moderate evidence for a beneficial effect of mediated association. Additionally, cross-experiment and item-level analyses did not support the mediated association effect. By contrast, Experiments 3 and 4 presented word pairs with direct association (e.g. "tiger" and "stripes") and demonstrated extreme evidence for a beneficial effect of direct association. The negligible effect of mediated association would aid in delimiting the scope of association's influence on working memory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".