The automaticity of semantic processing revisited: Auditory distraction by a categorical deviation.
Bibliographic record
Abstract
Automatic information processing has been and still is a debated topic. Traditionally, automatic processes are deemed to take place autonomously and independently of top-down cognitive control. For decades, the literature on reading has brought to the fore empirical phenomena such as Stroop and semantic priming effects that provide support for the assumption that semantic information can be accessed automatically. More recently, there has been growing evidence that semantic processing is in fact susceptible to higher-level cognitive influences, suggesting that this form of processing is instead conditionally automatic. The purpose of the present study was to revisit this debate using a novel approach: The automatic access to the meaning of irrelevant auditory stimuli was tested through the assessment of their distractive power. More specifically, we aimed to examine whether a categorical change in the content of to-be-ignored auditory sequences composed of speech items that are personally nonsignificant to participants (e.g., a digit among letters) can disrupt an unrelated visual focal task. In seven experiments, we assessed this categorical deviation effect and its functional properties. We established that distraction by categorical deviation is noncontingent on the activated task set and appears resistant to top-down control manipulations. By suggesting not only that the semantic content of the irrelevant sound can be extracted preattentively, but also that such semantic activation is ineluctable during auditory distraction, these findings shed new light on the automatic nature of semantic processing. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".