Bibliographic record
Abstract
Crowding refers to the deleterious effect of nearby objects on the identification of a target in the peripheral visual field. A recent study (Chen, Sperandio, & Goodale, 2015) showed that when a three-dimensional (3D) disk was crowded by disks of different sizes, participants could scale their grip aperture to the size of the target, even when they could not perceive its size. It is still unclear, however, whether or not grasping can also escape to some degree the crowding of other object features, such as shape. To test this, we presented 3D rectangular blocks in isolation or crowded by other blocks in the periphery. The target and flanking blocks had the same surface area but different dimensions. Participants were required either to grasp the target block across its width or to estimate its width. We found that, consistent with what we observed earlier with size, participants can also scale their grasp to the width of the target block even when they could not perceive its width. To further explore whether or not the effect of crowding on grasping depends on how proficient people are with their right hand, we had right-handed participants perform the same test but with their left hand. We found that left-hand grasping did not escape the crowding effect on shape perception at all. Taken together, our results suggest that people can also use invisible shape information to guide actions and that this ability depends on the proficiency of the action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".