Using a More Intuitive Cue in a Temporal Attention Discrimination Task to Compare Endogenous and Exogenous Mechanisms
Bibliographic record
Abstract
Temporal attention is a cognitive mechanism that allows individuals to prepare to respond to ananticipated event. Lawrence and Klein (2013) distinguished two forms of temporal attention: oneelicited by purely endogenous alerting mechanisms, and one elicited through exogenous alertingmechanisms. Recently, McCormick et al. displayed that these mechanisms generate additiveeffects on reaction time, however more informative speed and accuracy comparisons were notpossible due to them being measured during a detection task. The current pair of experimentslooks to compare these two forms of temporal attention in a discrimination task while measuringboth speed and accuracy, by inducing methodological modifications that lower task demand.These manipulations were successful, as temporal cueing effects were observed for both thecombined form and the less-studied purely endogenous form. However, speed-accuracyperformance for these two forms of temporal attention did not align with our predictions basedon Lawrence and Klein (2013), leading us to speculate on the generalizability of their results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".