Testing between the gunpowder fuse and the filling-hose analogy for mental curve tracing using electroencephalography
Bibliographic record
Abstract
Curve tracing occurs when a line is followed covertly to accomplish a task, for example, to determine whether two landmarks are on the same line or not (could Highway 61 take Robert Johnson from New Orleans to St Louis?). Previous work suggests that attention either moves along the curve, momentarily activating local representations of the curve during this process, leaving little or no trace of this activation once attention has passed, or attention spreads along the curve, resulting in an activated state along the entire portion of the curve that was traced. We re-examined this issue using event-related potentials. Curves to be traced were presented briefly to encourage a rapid deployment of attention. The curves started on the vertical midline and passed into the left or right visual field and terminated either on the vertical midline or at a lateral position. We measured a posterior contralateral negativity (relative to the visual field of the traced curve) that offset more rapidly when the curve was traced back to the midline than when it remained lateral. The results suggest that attention travels along the curve like fire on a fuse, with activation returning to baseline once the flame has passed.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".