Trial-by-trial adjustments in control triggered by incidentally encoded semantic cues
Bibliographic record
Abstract
Cognitive control mechanisms provide the flexibility to rapidly adapt to contextual demands. These contexts can be defined by top-down goals-but also by bottom-up perceptual factors, such as the location at which a visual stimulus appears. There are now several experiments reporting contextual control effects. Such experiments establish that contexts defined by low-level perceptual cues such as the location of a visual stimulus can lead to context-specific control, suggesting a relatively early focus for cognitive control. The current set of experiments involved a word-word interference task designed to assess whether a high-level cue, the semantic category to which a word belongs, can also facilitate contextual control. Indeed, participants exhibit a larger Flanker effect to items pertaining to a semantic category in which 75% of stimuli are incongruent than in response to items pertaining to a category in which 25% of stimuli are incongruent. Thus, both low-level and high-level stimulus features can affect the bottom-up engagement of cognitive control. The implications for current models of cognitive control are discussed.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".