Linguistic Attention Control: Attention Shifting Governed by Grammaticized Elements of Language.
Bibliographic record
Abstract
In 2 experiments, the authors investigated attention control for tasks involving the processing of grammaticized linguistic stimuli (function words) contextualized in sentence fragments. Attention control was operationalized as shift costs obtained with adult speakers of English in an alternating-runs experimental design (R. D. Rogers & S. Monsell, 1995). Experiment 1 yielded significant attention shift costs between tasks involving judgments about the meanings of grammatical function words. The authors used a 3-stage experimental design (G. Wylie & A. Allport, 2000), and the emerging pattern of results implicated task set reconfiguration and not task set inertia in these shift costs. Experiment 2 further demonstrated that shift costs were lower when the tasks involved shared attentional resources (processing the same grammatical dimension) versus unshared resources (different grammatical dimensions). The authors discuss the results from a cognitive linguistic perspective and for their implications for the view that language itself can serve a special attention-directing function.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".