The manual following response: In-motion or stationary background cues do not influence the online control of reaching movements
Bibliographic record
Abstract
A number of researchers have demonstrated that retinal motion signals influence goal-directed reaches in the direction of the observed motion: a behavioral phenomenon referred to as the manual following response (MFR). In the present study, we sought to determine if retinal motion cues influence online reaching control as well as determine if target eccentricities influence the MFR. Participants completed mediolateral reaches to briefly presented (i.e., 150 ms) targets (eccentricities: 10.2 and 19.5 cm) within a stationary, left or rightward moving (50°/s) random dot kinematogram. Target onset served as the movement imperative cue and occurred in time with activation of background motion. Results indicated that reaction times increased with increasing target eccentricity; however, the different background motion conditions did not modulate this effect. In addition, left and right background motion cues elicited a reliable MFR, but only for the 19.5 cm target eccentricity. Notably, detailed analyses of reach trajectories indicated that the observed MFR could not be attributed to differences in the online control of the movement. Thus, our results suggest that retinal motion signals require some minimum time to be incorporated into a response and/or that a critical movement amplitude is required to elicit the MFR. Moreover, our results provide the first direct evidence that the elicitation of the MFR is not attributed to putative differences in online control.
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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".