Bibliographic record
Abstract
At last year's VSS meeting (journalofvision.org/8/6/299/) we demonstrated that during a natural grasp, gaze fixations cluster towards the top edge of an object. These fixation locations were tightly coupled to index finger grasp position, which fell across the object's center of mass (COM). The tight link between grasp position and fixation locations has also been shown in situations where participants are instructed to grasp an object at a specific location (de Grave et al., 2008; Johansson et al., 2001). However, these studies have explored fixations while grasping symmetrical objects - whose COM also corresponds to the object's midline. While previous studies have highlighted the importance of an object's COM while grasping, showing that people grasp an object across this point regardless of where the midline of the object is (for example Kleinholdermann et al., 2007), it is still not clear whether such dissociations are present in gaze fixation locations. The purpose of the present study was to explore whether it is an object's midline or an object's COM that is the main focus of fixations while grasping. Participants were presented with complex asymmetrical shapes whose COM was oriented on either the left or right side of the object's midline. In support of previous research, participants grasped the objects across the COM. Likewise, gaze fixations were concentrated towards the top edge of the objects, corresponding to index finger location. Additionally, first fixations were found to be significantly shorter in duration then final fixations, indicating that participants were spending more time looking at the shapes while positioning their fingers on the object. Despite the complexity of the shapes, however, participants did not explore the object area in more detail. This study highlights the importance of an object's COM rather than its midline for the programming of both grasp and gaze fixations.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".