Hand-specificity in gaze-dependent memory-guided reach errors
Bibliographic record
Abstract
Reaching movements toward remembered visual targets in a dark environment show overshoot effect relative to the gaze direction, but the exact origin of this overshoot is unknown. Because the reach errors depend on the visual target eccentricity, all previous studies (e.g., Henriques et al. 1998; McGuire & Sabes 2009) have assumed that it reflects biases in target‑related inputs to the visuomotor transformation. This possibility predicts the error pattern is hand-independent. So far, however, it has only been studied for the right hand. Here, we directly compared left and right arm reaching movements toward remembered visual targets in the dark. Right-handed subjects sat in front of a screen behind which five bicolor LEDs were attached at 0, ±10, and ±20 deg. of visual angle in a completely dark room. While fixating in the direction of a previously shown fixation LED, a red LED appeared to signal the reach target. After a memory delay of 1000 ms subjects started reaching to the remembered target position as accurately as possible (i.e., without any time pressure). They executed reaching movements with their left and right index finger alternately. Eye and hand movements were recorded using EyeLink II and Optotrak, respectively. Results so far showed gaze-dependent overshoots for both hands (P<.005). Interestingly, the effect of target eccentricity interacted with hand (P<.005), which appeared to reflect a relatively stronger overshoot effect for the left hand in the right visual field. This finding shows that the overshoot does not only reflect biases in target‑related, but also in hand-related inputs to the visuomotor transformation. This has implications for studies of reaching movements towards proprioceptive targets defined by the non-reaching hand. Meeting abstract presented at VSS 2012
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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 | 0.002 |
| 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) | 0.002 | 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".