Online visual feedback of the hand suppresses gaze-dependent overshoots in memory-guided reach
Bibliographic record
Abstract
Reaching movements in the dark overshoot memorized visual targets relative to the gaze direction held just before reach onset, even if a saccade intervenes between target presentation and reach onset. The latter pattern has been cited as evidence for the updating of memorized visual target positions in a gaze-centered reference frame. However, the exact origin of the gaze-dependent overshoot is not known. 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. An alternative possibility, as of yet untested, is that the error is associated with biases in hand-related signals. Here, we tested this hypothesis through its prediction that visual feedback of the hand during the reaching movement should greatly reduce or even abolish the gaze-dependent overshoot. Six subjects sat in the dark in front of a screen behind which target and fixation LEDs were mounted 10 deg. of visual angle apart, while their eye and finger movements were recorded using EyeLink II and Optotrak, respectively. All subjects showed the typical gaze-dependent overshoot reported before (~±2 deg. at ±10 deg. retinal target eccentricity; P<0.005). The overshoot disappeared when they could see their finger during the reach (~±0.2 deg. at ±10 deg. retinal target eccentricity; P>0.37). This effect was most parsimoniously explained by a reach planning model that included biases in the transformation of proprioceptive signals into visual coordinates, for the purpose of calculating a reach vector in visual coordinates (further predictions of this model are currently under investigation). This is the first demonstration that overshoots to remembered visual targets can be suppressed entirely in healthy human subjects. This finding is consistent with our hypothesis that the overshoot arises within hand-related inputs into the visuomotor transformation. 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 | 0.001 |
| 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) | 0.001 | 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".