Semantic priming by task-irrelevant speech: category-level or item-level processing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it