Evidence for a stimulus intensity dependent two-component model of startle reflex activation
Bibliographic record
Abstract
The reflexive startle response can be used as a tool to study the neurophysiology and pathophysiology of neural circuits. Higher intensity stimuli are generally more likely to elicit the startle response. There is currently debate as to whether the startle reflex consists of a single response or multiple components subserved by separate neural pathways. The purpose of this study was to examine how the startle response was affected by stimulus intensity during a reaction time task. Startle response data from two experiments (Carlsen et al, 2007; Carlsen, in review) that required participants to react to an auditory stimulus of varying intensity (82-124 dB) were analyzed. We integrated EMG from sternocleidomastoid (SCM, a robust indicator of startle), in three time frames following the stimulus (0-50 ms, 50-100 ms, 100-150 ms). SCM activity was analyzed via a 3 (time bin) X 5 (stimulus intensity) repeated measures ANOVA. In both experiments a significant interaction was found (p < .05) whereby an early startle component (50-100 ms) was observed only at higher intensities (≥ 116 dB) followed by a later startle component (100-150 ms) in response to lower stimulus intensities (103 dB and above). These results suggest that the early component involves a fast, high threshold pathway, whereas the later component uses a slower and lower threshold circuit. This threshold-dependent two-component model provides a novel description of the neurophysiology underlying the startle reflex circuitry. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".