Stimulus-Locked Responses on Human Upper Limb Muscles and Corrective Reaches are Preferentially Evoked by Low Spatial Frequencies
Bibliographic record
Abstract
Abstract In situations requiring immediate action, humans can generate visually-guided responses at remarkably short latencies. Here, to better understand the visual attributes that best evoke such rapid responses, we recorded upper limb muscle activity while participants performed visually-guided reaches towards Gabor patches composed of differing spatial frequencies. We studied reaches initiated from a stable posture (experiment 1, a static condition), or during on-line reach corrections to an abruptly displaced target (experiment 2, a dynamic condition). In both experiments, we detail the latency and prevalence of stimulus-locked responses (SLRs), which are brief bursts of EMG activity that are time-locked to target presentation rather than movement onset. SLRs represent the first wave of EMG recruitment influenced by target presentation, and enable quantification of rapid visuomotor transformations. In both experiments, reach targets composed of low spatial frequencies elicited the shortest latency and most prevalent SLRs, with SLR latency increasing and SLR prevalence decreasing for reach targets composed of progressively higher spatial frequencies. SLRs could be evoked in either the static or dynamic condition, and when present in experiment 2, were associated with shorter latency and larger magnitude corrections. Furthermore, SLRs evolved at shorter latencies (~20 ms) when the arm was already in motion. These results demonstrate that stimuli composed of low spatial frequencies preferentially evoke the most rapid visuomotor responses which, in the context of rapidly correcting an on-going reaching movement, are associated with earlier and larger on-line reach corrections. Significance Statement Humans have a remarkable capacity to respond quickly to changes in our visual environment. Although our visual world is composed of a range of spatial frequencies, surprisingly little is known about which frequencies preferentially evoke rapid reaching responses. Here, we systematically varied the spatial frequency of peripheral reach targets while measuring EMG activity on an upper limb muscle. We found that visual stimuli composed of low-spatial frequencies elicit the most rapid and robust EMG responses and corrective reaches. Thus, when time is of the essence, low spatial frequencies preferentially drive fast visuomotor responses.
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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".