Perceptual illusion and the real-time control of action
Bibliographic record
Abstract
Participants were cued by an auditory tone to grasp a target object from within a size-contrast display. The peak grip aperture was unaffected by the perceptual size illusion when the target array was visible between the response cue and movement onset (vision trials). The grasp was sensitive to the illusion, however, when the target array was occluded from view when the response was cued (occlusion trials). This was true when the occlusion occurred 2.5 s before the response cue (delay), but also when the occlusion coincided with the response cue (no-delay). Unlike previous experiments, vision and occlusion trials were presented in random sequence. The results suggest that dedicated, real-time visuomotor mechanisms are engaged for the control of action only after the response is cued, and only if the target is visible. These visuomotor mechanisms compute the absolute metrics of the target object and therefore resist size-contrast illusions. In other situations (e.g. prior to the response cue, or if the target is no longer visible), a perceptual representation of the target object can be used for action planning. Unlike the real-time visuomotor mechanisms, perception-based movement planning makes use of relational metrics, and is therefore sensitive to size-contrast illusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".