Temporal processing of self-motion: modeling reaction times for rotations and translations
Bibliographic record
Abstract
In this paper, we show that differences in reaction times (RT) to self-motion depend not only on the duration of the profile, but also on the actual time course of the acceleration. We previously proposed models that described direction discrimination thresholds for rotational and translational motions based on the dynamics of the vestibular sensory organs (otoliths and semi-circular canals). As these models have the potential to describe RT for different motion profiles (e.g., trapezoidal versus triangular acceleration profiles or varying profile durations), we validated these models by measuring RTs in human observers for a direction discrimination task using both translational and rotational motions varying in amplitude, duration and acceleration profile shape in a within-subjects design. In agreement with previous studies, amplitude and duration were found to affect RT, and importantly, we found an influence of the profile shape on RT. The models are able to fit the measured RTs with an accuracy of around 5 ms, and the best-fitting parameters are similar to those found from identifying the models based on threshold measurements. This confirms the validity of the modeling approach and links perceptual thresholds to RT. By establishing a link between vestibular thresholds for self-motion and RT, we show for the first time that RTs to purely inertial motion stimuli can be used as an alternative to threshold measurements for identifying self-motion perception models. This is advantageous, since RT tasks are less challenging for participants and make assessment of vestibular function less fatiguing. Further, our results provide strong evidence that the perceived timing of self-motion stimulation is largely influenced by the response dynamics of the vestibular sensory organs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".