In Manually-Assisted Search, Perception Supervises Rather Than Directs Action
Bibliographic record
Abstract
During manually-assisted search, where participants must actively manipulate search items, it has been reported that participants will often select and move the target of search itself without recognizing it (Solman et al., 2012a). In two experiments we explore the hypothesis that this error results from a naturally-arising strategy that decouples perception and action during search, enabling motor interactions with items to outpace the speed of perceptual analysis. In Experiment 1, we report that the error is prevalent for both mouse and touch-screen interaction modes, and is uninfluenced by speeding or slowing instructions--ruling out these task-specific details as causes of the error. In Experiment 2 we manipulate motor speed, and show that reducing the speed of individual movements during search leads to a reduction in error rates. These findings support the conclusion that the error results from incoordination between motor and perceptual processes, with motor processes outpacing perceptual abilities.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".