How does exogenous alerting impact endogenous preparation on a temporal cueing task
Bibliographic record
Abstract
Temporal attention is a cognitive mechanism that allows individuals to prepare to respond to an anticipated event. Lawrence, M. A., & Klein, R. M. (2013. Isolating exogenous and endogenous modes of temporal attention. Journal of Experimental Psychology: General, 142(2), 560–572. https://doi.org/10.1037/a0029023) distinguished two forms of temporal attention: one elicited by purely endogenous alerting mechanisms and one elicited through exogenous alerting mechanisms. Recently, McCormick, C. R., Redden, R. S., Lawrence, M. A., & Klein, R. M. (2018. The independence of endogenous and exogenous temporal attention. Attention, Perception, & Psychophysics, 80(8), 1885–1891. https://doi.org/10.3758/s13414-018-1575-y) found that these mechanisms generate additive effects on reaction time, however more informative speed and accuracy comparisons were not possible due to the effects being measured in a simple detection task. The current pair of experiments aims to compare two forms of temporal attention in a discrimination task while measuring both speed and accuracy by inducing methodological modifications that lower task demands. These manipulations were successful, as temporal cueing effects were observed for both the combined form and the less-studied purely endogenous form. However, speed-accuracy performance for these two forms of temporal attention did not align with our predictions based on prior work.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".