Stimulus-locked responses on human upper limb muscles prefer low spatial frequency, high contrast, and fast moving targets
Bibliographic record
Abstract
To reach towards a visible target, such as a coffee mug, visual information has to be transformed into motor commands. Visual information such as the colour, shape, and size of the mug is processed within numerous brain areas, then relayed to the motor periphery. However, in order to catch a falling mug, we must transform vision into action as quickly as possible. These fast visuomotor transformations, and their underlying neurological substrates, are poorly understood in humans. Fundamental questions still remain regarding what visual information is necessary or available to a fast visuomotor system. My presentation will focus on work which attempts to better understand the nature of visual input received by the fast visuomotor system, beginning with a description of stimulus-locked responses (SLRs) which provide a measure to study the fast visuomotor system. SLRs are the first wave of motor recruitment directly influenced by visual stimulus presentation and are hypothesized to be mediated via the Superior Colliculus. To study the effects of visual stimulus attributes on the SLR, human subjects generated visually guided reaches towards stimuli using a robotic manipulandum, as surface electrodes recorded electromyographic (EMG) activity from the pectoralis major muscle. Recently, we have detailed SLRs in response to systematically varied visual parameters including spatial frequency, speed and contrast. Our results suggest that a combination of low spatial frequency, high contrast, and fast moving targets evoke SLRs in all tested participants. These findings may be consistent with preferential magnocellular (M) pathway input into a fast visuomotor system. Importantly, those stimuli that best evoke SLRs are also those that elicit the earliest “mid-flight” corrections during an on-going reaching movement. Detailing the visual stimulus attributes that best evoke SLRs will aid future studies of a fast visuomotor system across the lifespan, and in health and disease.
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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) | 0.003 | 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".