A window into behavioural strategies used in visuomotor adaptation
Bibliographic record
Abstract
Our lab has previously demonstrated that the computer-based Viewing Window task is useful for investigating the interactions between action and perception (Baugh and Marotta, 2007). During the task participants identify blurred images by controlling a small window through which they can see part of the image clearly. Past research has used manipulation of the relationship between the participant's action and resulting viewing window movement to investigate visuomotor adaptation. However, only large distortions apparent to the participant have been used. In daily life, we frequently make subtle adjustments that are performed without our awareness. We hypothesized that by altering viewing window control with subtle, gradual changes we could also elicit non-conscious adaptation. In the present study the first 10 trials (pre-measures) of the Viewing Window task were performed using normal control followed by 61 experimental trials during which control changed by either: 1. large jumps to −22.5° and −45° (sudden condition), or 2. small changes of -0.73° during each trial to a maximum of −45° (gradual condition). The Viewing Window then returned to normal control for 10 trials (post measures). Scan paths of movement revealed more complex pathways during the sudden condition compared to the gradual condition. Additionally, a significant interaction between the rotation condition and the time spent on/off the object during the pre- and post-distortion trials was found. Both groups demonstrated greater time spent scanning areas off of the object once viewing window control returned to normal. However, this effect was larger in the gradual rotation group indicating larger after-effects. The results of this study demonstrate that the Viewing Window task can be used to elicit two common strategies of visual motor adaptation and can therefore provide a tool to investigate neural networks underlying conscious and non-conscious visual motor adaptation.
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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 | 0.001 |
| 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".