The influence of crowding on grip scaling during grasping
Bibliographic record
Abstract
It is well known that nearby objects influence the perception of a target (crowding), but little is known about how nearby objects influence our actions towards the target. In this study, a white disk of either 3 cm or 3.75 cm in diameter, was presented along the horizontal meridian at an eccentricity of 30° either in isolation (uncrowded) or surrounded by six disks of different sizes (crowded). At the beginning of each trial, LCD goggles worn by the participants were closed. Participants held down the start button with their thumb and index fingers pinched together. After the disks had been placed on the table, the goggles were opened. On perceptual trials, participants were required to manually indicate the size of the target disk using their thumb and index finger, and after that to pick up the disk. On grasping trials, participants were required to grasp the target disk with their thumb and index finger as quickly and accurately as possible. On some trials, the goggles were closed as soon as the start button was released (open loop) so that participants could not see their hands or the disks during the execution of the movement. On other trials, the goggles were closed 3 s after participants released the button (closed loop), permitting a full view of the moving hand and the target. In all tasks, the distance between the index finger and thumb was measured with OPTOTRAK. Even though participants could not indicate the size of the targets on perceptual trials, they scaled their grip aperture to the size of the target on grasping trials. These results were observed on both closed- and open-loop trials. Overall, these findings support the dissociation between vision-for-action and vision-for-perception – and suggest that the neural coding of objects may be different for these two systems. Meeting abstract presented at VSS 2013
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".