Distractor Interference during a Choice Limb Reaching Task
Bibliographic record
Abstract
According to action-centered models of attention, the patterns of distractor interference that emerge in selective reaching tasks are related to the time and effort required to resolve a race for activation between competing target and non-target response producing processes. Previous studies have only used unimanual aiming tasks and, as such, only examined the effects of competition that occurs within a limb. The results of studies using unimanual aiming movements often reveal an "ipsilateral effect"--distractors on the same side of space as the effector cause greater interference than distractors on the opposite side of space. The cost of the competition when response selection is between the limbs has yet to be addressed. Participants in the present study executed reaching movements to 1 of 4 (2 left, 2 right) possible target locations with and without a distractor. Participants made ipsilateral reaches (left hand to left targets, right hand to right targets). In contrast to studies using unimanual aiming movements, a "contralateral effect" was observed; distractors affording responses for the other hand (in contralateral space) caused more interference than distractors affording responses for the same hand. The findings from the present research demonstrate that when certain portions of response planning must be resolved prior to response initiation, distractors that code for that dimension cause the greatest interference.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".