Decisions in motion: vestibular contributions to saccadic target selection
Bibliographic record
Abstract
The natural world continuously presents us with many opportunities for action, and thus a process of target selection must precede action execution. While there has been considerable progress in understanding target selection in stationary environments, little is known about target selection when we are in motion. Here we investigated the effect of self-motion signals on saccadic target selection in a dynamic environment. Human subjects were sinusoidally translated (f = 0.6 Hz, 30-cm peak-to-peak displacement) along an interaural axis with a vestibular sled. During the motion two visual targets were presented asynchronously but equidistantly on either side of fixation. Subjects had to look at one of these targets as quickly as possible. With an adaptive approach, the time delay between these targets was adjusted until the subject selected both targets equally often. We determined this balanced time delay for different phases of the motion in order to distinguish the effects of body acceleration and velocity on saccadic target selection. Results show that acceleration (or position, as these are indistinguishable during sinusoidal motion), but not velocity, affects target selection for saccades. Subjects preferred to look at targets in the direction of the acceleration-the leftward target was preferred when the sled accelerated to the left, and vice versa. Saccadic reaction times mimicked this selection bias by being reliably shorter to targets in the direction of acceleration. Our results provide evidence that saccade target selection mechanisms are modulated by self-motion signals, which could be derived directly from the otolith system.
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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) | 0.002 | 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".