Visual processing is diminished during movement execution
Bibliographic record
Abstract
Recent research has suggested that visual discrimination and detection may be enhanced during movement preparation and execution, respectively. The current study examined if visual perceptual processing is augmented prior to or during a movement through the use of an Inspection Time (IT) task. The IT task involved briefly presenting (e.g., 15-105 ms) a "pi" figure with differing leg lengths, which was then immediately masked for 400 ms to prevent retinal afterimages. Participants were subsequently required to choose which of the two legs was longer. In Experiment 1, participants (n = 28) completed the IT task under three movement conditions: no-movement (NM), foreperiod (FP), and peak velocity (PV). In the NM condition, participants solely engaged in the IT paradigm. In the FP condition, the IT stimulus was presented prior to movement execution when response planning was expected to occur. Finally, in the PV condition, participants made a rapid movement to a target, and the IT stimulus was presented when their limb reached peak velocity. In Experiment 2, participants (n = 18) also performed the IT task in the PV and NM condition; however, vision of the limb's motion was made available during the PV trials (PV-FV) to investigate the potential influence of visual feedback on IT performance. Results showed no significant differences in performance in the IT task between the NM and FP conditions, suggesting no enhancement of visual processing occurred due to response preparation (Experiment 1). However, IT performance was significantly poorer in the PV condition in comparison to both the NM and FP conditions (Experiment 1), and was even worse when visual feedback was provided (Experiment 2). Together, these findings suggest that visual perceptual processing is degraded during execution of a fast, goal-directed movement.
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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 | 0.003 |
| 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) | 0.002 | 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".