Visuomotor Memory for Target Location in Near and Far Reaching Spaces
Bibliographic record
Abstract
The authors investigated systematic error associated with endpoints of memory-guided actions performed in near and far reaching spaces. To accomplish that objective, the authors instructed 12 participants to initiate open-loop and memory-guided reaches (0, 2,000, and 5,000 ms of visual delay) from a common start position to remembered midline targets in near (i.e., a backward reach) and far (i.e., a forward reach) reaching spaces. The results indicated that near and far reaches, respectively, over- and undershot veridical target location, and the direction-specific nature of the error was amplified in the memory-guided conditions. The latter finding represents an important aspect of the present research because it suggests that the direction-specific error identified here is related to factors arising within the sensory component of the task rather than mechanical differences in reaching direction. The authors propose that stored target information serving memory guided actions is susceptible to a compression of visual space in memory such that the egocentric distance of a remembered target is underestimated.
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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 | 0.008 |
| 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".