Startle activation is additive with voluntary cortical activation irrespective of stimulus modality
Bibliographic record
Abstract
When a startling acoustic stimulus (SAS: >120dB) is presented during a simple reaction time (RT) task it has been shown to trigger the prepared movement through an involuntary initiation pathway. To investigate the time course of initiation related activity, a previous study (Maslovat et al. 2014) presented a SAS at various times following the go-signal (i.e., during the RT interval), with RT results suggesting that the activations related to both the voluntary and startle-related initiation processes were additive. In the current study the predictions of an additive neural activation model were tested by replicating the methods of Maslovat et al., but changing the modality of the go-signal. As voluntary RT latencies are delayed for visual stimuli compared to acoustic stimuli it was hypothesised that the time course of additive activation would be similarly delayed. Participants performed 150 RT trials requiring a targeted 20° wrist extension in response to a visual go-signal. In 20% of trials, a SAS was randomly presented 0, 25, 50, 75, 100, or 125 ms following the go-signal. As predicted, RT to the visual go-signal (186 ms) was increased compared to the previous study (127 ms; Maslovat et al.). Furthermore, results showed that RTs for the 25 to 125 ms SAS delays (110, 132, 146, 158, 172 ms, respectively) were very similar to RT values predicted by an additive initiation model (112, 130, 145, 161, 175 ms). Together the results support an additive neural model of startle and voluntary initiation-related activation, even when the go-signal is of a different modality than the SAS. In addition, these results indicate that RT differences due to stimulus modality are attributable to processes occurring prior to the increase in initiation-related activation.Acknowledgments: Research supported by NSERC
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".