Isolating exogenous and endogenous modes of temporal attention.
Bibliographic record
Abstract
The differential allocation of information processing resources over time, here termed "temporal attention," may be achieved by relatively automatic "exogenous" or controlled "endogenous" mechanisms. Over 100 years of research has confounded these theoretically distinct dimensions of temporal attention. The current report seeks to ameliorate this oversight by novel application of 2 experimental methodologies. A scheme imported from the animal learning literature (Rescorla's "truly random control" procedure) was used to eliminate any temporal contingency between signals and targets. An auditory stimulus imported from the psychophysical literature (correlated vs. uncorrelated noise) was used to provide a salient signal that entailed no local or global change in intensity. Purely endogenous temporal attention (generated by a reliable signal-target contingency in the absence of a change in intensity) is characterized by robust improvements in speed and accuracy of responding. Purely exogenous temporal attention (generated by an intensity increase in the absence of contingency) is characterized by a brief period of faster responding. When exogenous temporal attention is elicited in the context of endogenous temporal attention, the decrease in response time that follows an intense signal is accompanied by a decrease in response accuracy.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".