The Effect of Learned Perceptual Associations on Visuomotor Programming Varies with Kinematic Demands
Bibliographic record
Abstract
The present set of experiments investigated the possibility that learned perceptual information can, under certain circumstances, be utilized by visuomotor programming. In Experiment 1 (N = 28), an association was established between the color and size of square wooden blocks (e.g., red = large; yellow = small, or vice-versa). In Experiment 2 (N = 28), an association was established between the shape and size of plastic objects (e.g., hexagon = large; circle = small, or vice-versa). It was expected that the learned associations would change the perceived size of two probe objects halfway in size between the large and small objects (the probe object matched by color or shape to the large group of objects would appear smaller than the probe object matched to the small group of objects as a result of within-group relative size comparisons). In both experiments, half of the participants grasped the target objects, and the other half estimated the size of the objects by opening their thumb and finger a matching amount. For Experiment 1, it was predicted that an influence of the learned association on the treatment of the probe objects would be seen in manual estimations and in grip scaling because the kinematics of the grasping movement were very similar across trials. As predicted, the learned association between size and color was as easily incorporated into visually guided grasping as it was into visual perceptions. In Experiment 2, it was predicted that an influence of the learned perceptual association would be seen only in manual estimations, and not in grip scaling, because the variability in target object shape from trial to trial would demand changes in precontact finger posture across trials. Despite the significant effect of the size-shape association on size estimations, no influence was seen in preparatory grip scaling, probably because varying shape increased the metrical demands on visuomotor programming from those in Experiment 1. Together, the results suggest that visuomotor programming can make use of learned size information under some, but not all, conditions.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".