Increases in stimulus intensity lead to a greater rate of activation accumulation in primary motor cortex
Bibliographic record
Abstract
The stimulus intensity effect is a phenomenon whereby in a simple reaction time (RT) task, as the go-signal stimulus intensity increases (e.g., brighter, louder), RT decreases. While the stimulus intensity effect is highly robust, it is unclear how response initiation processes are affected by the more intense stimuli. To investigate the neural processes underlying stimulus intensity effects, participants (n=14) completed a simple RT task requiring targeted wrist extension in response to an acoustic stimulus of 60, 70, 80, or 90dB. On each trial transcranial magnetic stimulation (TMS) was applied (110% of resting threshold) over the wrist extensor representation of the primary motor cortex (M1) at 0, 30, 45, 60, and 75% of each participant's respective baseline RT (determined from a block of 10 trials with an 80dB go-signal). Results confirmed a stimulus intensity effect, whereby the 90dB stimulus resulted in faster RTs than all other intensities (p=.025). Analysis of motor evoked potential (MEP) amplitude elicited by TMS revealed an intensity by time interaction (p=.003). While all MEP amplitudes increased in size as TMS was presented later in the RT interval, the 90dB stimulus elicited drastically larger MEP amplitudes than all other intensities when delivered at the latest time point (75% of baseline RT). These results show that M1 excitability for the 90dB stimulus rapidly increases just prior to response onset, demonstrating that the stimulus intensity effect may occur due to a faster rate of increase in M1 activation levels prior to response execution for louder stimuli.Acknowledgments: Supported by NSERC and the Ontario Ministry of Research and Innovation and Science.
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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) | 0.004 | 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".