Visual feedback during a goal-directed movement decreases performance on an inspection time task
Bibliographic record
Abstract
Performing goal-directed movements has been shown to increase resistance to visual illusions, possibly due to enhanced visual processing. An inspection time (IT) task can be used to measure visual processing ability by having participants identify the longer leg of an asymmetric pi figure that is presented for various times (15-105ms), and then immediately backward masked for 400 ms to prevent further processing. A previous experiment used an IT paradigm during production of a goal-directed movement, and contrary to predictions, found degraded visual ability. However, online visual feedback of the limb was not provided, which may account for this finding. Thus, the purpose of the present experiment was to examine whether provision of visual feedback during movement production would enhance performance in an IT task. Participants (n=12) performed an IT task under three conditions: no-movement, during production of a 30-deg targeted right arm extension movement without visual feedback, and during production of the same movement with online visual feedback. Results revealed a main effect for condition, F(2, 22) = 4.71, p = .02, whereby IT performance was significantly poorer in the movement with visual feedback condition compared to the no-movement condition. These results suggest that adding online visual feedback during movement execution while also completing an IT task may exceed the capacity of the visual system, and degrade IT task performance.Acknowledgments: Supported by NSERC and the Ontario Ministry of Research and Innovation and Science
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".