Sometimes more (overlap) is better! Action plan overlap impacts the interference between visually-guided touch and multiple-object tracking (MOT)
Bibliographic record
Abstract
When two tasks are performed simultaneously their action plans can overlap with one another. Past findings suggest that the overlap can either improve or degrade performance, depending on the relatedness of the required actions (e.g., Fournier et al. 2015). In this study we assessed the impact of overlapping action plans in a multiple-object tracking (MOT) task. Participants tracked 1-4 MOT targets while also touching moving items in MOT that changed colour. To determine the effects of action plan overlap between the MOT and touch task, we manipulated the way that participants reported the identity of the targets at the end of the trial (untimed). In the touch task participants always used the index finger of their dominant hand. To report the targets participants either typed in the letters corresponding to the targets with their non-dominant hand (minimal overlap) or touched MOT targets with the index finger of their dominant hand (maximal overlap). Target report method had no effect on single-task MOT performance. However, when participants had to touch moving items that changed colour during tracking (dual-task), MOT performance was significantly worse when overlap was minimized. It also took participants longer to touch moving items that changed colour - even though target report occurred 7-8 seconds later. Nonetheless, MOT performance was always better and touch latencies lower when the touched items were targets as compared to distractors in MOT; report technique had no effect. This shows a dissociation between the effects of attentional selection in MOT and overlapping action plans.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".