Semantic priming by task-irrelevant speech: category-level or item-level processing?
Bibliographic record
Abstract
Recent studies show that task-irrelevant speech affects subsequent behaviour. For instance, category-exemplar production is primed if those exemplars were previously auditory distractors that accompanied the presentation of visual digits for serial recall (Röer et al., Citation2017. Semantic priming by irrelevant speech. Psychonomic Bulletin & Review, 24(4), 1205–1210. https://doi.org/10.3758/s13423-016-1186-3). This study examines semantic organisation as a boundary condition for the semantic priming effect. In a between-participants design, sequences of auditory distractors were either semantically organised (eight exemplars from one category) or random (one exemplar from each of eight categories). Semantic priming was measured by comparing production probability of previously encountered words against a matched unencountered set. Prior research indicates that an unexpected categorical change in task-irrelevant speech disrupts performance, suggesting processing of shared categorical membership enhances semantic priming (e.g. Vachon et al., Citation2020. The automaticity of semantic processing revisited: Auditory distraction by a categorical deviation. Journal of Experimental Psychology: General, 149(7), 1360–1397. https://doi.org/10.1037/xge000071). Consistent with these findings, semantic priming was found when distractor words were semantically organised but was absent with randomly presented exemplars, offering insight into the semantic processing of background sound.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".