What's your next move? Directional biases for sequential limb and eye movements
Bibliographic record
Abstract
There is considerable interest in movement direction encoding in the central nervous system, in part because of potential applications to the development of neural prosthetic devices. Most studies in this field focus on single movements, with little attention given to the interactions that may occur between successive movements. The purpose of our study was to determine, independent of movement effector system, if a prior movement results in directional reaction time biases for subsequent movements. In our experiments, participants made two consecutive eye movements or two consecutive arm movements in directions indicated by arrows presented at fixation. In experiment one, two target locations were shown during each trial. Here, movements were faster when offset by 90 or 180 degrees from the first movement (relative to movements back to the original target location). This pattern is consistent with 'inhibition of return' (IOR) typically found for repeated movements made in the same direction. In experiment two we presented four possible target locations. Here we found that arm and eye movements were faster only when offset by 90 degrees from the initial movement; the well-established advantage for movements offset by 180 degrees was eliminated. Our results reveal an effect of set size (i.e. 4 vs. 2 possible movement locations) on the spatial gradient of RTs for consecutive movements. That similar results were found for eye and arm movements suggests the existence of a common motor programming principle that may be useful for computational models attempting to 'decode' neural signals for the implementation of neuroprosthetic devices.
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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 | 0.001 | 0.013 |
| 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 | 0.001 | 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".