Binocular advantage for decision making during a prehension task
Bibliographic record
Abstract
Purpose: Binocular vision provides an advantage for detecting and discriminating objects (i.e., binocular summation) and during reaching (i.e., higher peak velocities). However, the role of binocular vision in decision-making processes in more complex environments has not been examined. The purpose of this study was to examine the role of binocular vision during a prehension task with varied levels of difficulty. Methods: Seven, right-handed adults (age 21±1.8 years) reached and grasped a cylinder while eye movements were recorded with an eye-tracker (EyeLink) and reach kinematics with the Optotrak system. Participants had to discriminate the size of a circle shown at fixation and then pick up the corresponding cylinder (match size) from a set of cylinders. Difficulty was manipulated with set size (target shown among 3 or 6 objects) and target discriminability (using color), which were randomized during binocular (BV) and monocular viewing (MV). Results: Primary saccade latency was not affected by viewing condition or display complexity (BV: 398±97 ms; MV: 403±83 ms). Regardless of display complexity, reach latency was significantly longer during MV compared to BV (784±255 vs. 716±216 ms; p=0.016). Furthermore, set size influenced the delay in reach initiation after the primary saccade: reach initiation was prolonged during MV on average by 30 ms for the smaller set size and by 75 ms for the larger set size. Conclusion: Preliminary data support the hypothesis that binocular vision facilitates the acquisition of relevant information for decision-making to guide reaching and grasping movements. The role of this input increases in environments of greater complexity.Acknowledgments: Canada Foundation for Innovation
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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 | 0.000 | 0.003 |
| 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) | 0.003 | 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".