Action matters! Target report technique affects interference between visually guided touch and multiple-object tracking
Bibliographic record
Abstract
When participants carry out concurrent tasks there can be overlap in action plans. This study shows the effects of action-plan overlap in a multiple-object tracking (MOT) task where participants tracked 1–4 targets while touching any items that changed colour during the item motion phase of tracking trial (either targets or distractors in MOT). We manipulated the way that participants reported MOT targets at the end of the trial. Participants (untimed) either reported targets by touching them with the index finger of their dominant hand (maximal overlap between target report and touching items that changed colour) or typed in letters corresponding to targets with their non-dominant hand (minimal overlap). Target report had no effect on single-task MOT performance. However, when participants had to touch items that changed colour during tracking, MOT was significantly worse when participants reported targets by typing them in rather than touching them and it also took participants longer to touch items that changed colour even though these colour changes preceded target report by 7–8 s. Nonetheless, target-report did not affect the performance discrepancy between the target- and distractor-touch conditions, which suggests performance differences between these two conditions reflect differences in attentional selection rather than action-plan overlap.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".