Acoustic, and Categorical, Deviation Effects are Produced by Different Mechanisms: Evidence from Additivity and Habituation
Bibliographic record
Abstract
Sounds that deviate, acoustically or semantically, from prevailing auditory backgrounds disrupt ongoing mental activity. An acoustic deviant is held to capture attention, but doubt has been cast on the attentional nature of the semantic, categorical deviation effect. Unlike the acoustical deviation effect, which is typically amenable to top-down cognitive control, the categorical deviation effect is impervious to top-down influences.To shed further light on the mechanisms underpinning acoustic and categorical deviance, we compared the disruptive impact produced by acoustic deviants (change of voice), categorical deviants (change of category) and combined deviants (change of voice and category) randomly inserted into a to-be-ignored sequence while participants performed a visual-verbal serial recall task.In Experiment 1, all deviants disrupted recall, however combined deviants produced greater disruption than acoustic deviants alone. In Experiment 2 only the disruption produced by an acoustic deviant diminished over the course of the experiment. The acoustic and categorical deviation effects combined additively to disrupt performance (Experiment 1) and habituation was only observed for the acoustic deviation effect (Experiment 2).These results gel with the idea that attentional responses to deviants, and habituation thereof (Experiment 2), is a key component of acoustic but not categorical deviation effects. Taken together, these findings support recent assertions that independent mechanisms drive acoustic and categorical deviation effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".