Fork in the road: how self-confidence about terrain influences gaze behaviour and path choice
Bibliographic record
Abstract
ABSTRACT Decisions about where to move occur throughout the day and are essential to life. Different movements may present different challenges and affect the likelihood of achieving a goal. Certain choices may have unintended consequences, some of which may cause harm and bias the decision. Movement decisions rely on a person gathering necessary visual information via shifts in gaze. Here we sought to understand what influences this information-seeking gaze behaviour. We had participants choose between walking across one of two paths that consisted of images of terrain commonly found in either hiking or urban environments. We manipulated the number and type of terrain of each path. We recorded gaze behaviour during the approach to the two paths and had participants rate their self-confidence about walking across each type of terrain as though it was real. Participants did not direct gaze to the path with greater visual information, regardless of how we quantified information. Rather, we show that a person’s self-confidence about their motor abilities predicts how they visually explore the environment with their eyes as well as their choice of action. The greater the self-confidence about walking across one path, the more they directed gaze to it, and the more likely they chose to walk across it. Overall, our results reveal a key role of a cognitive factor (self-confidence) in decision-making during a natural, movement-related behaviour.
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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 | 0.011 |
| 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 | 0.001 | 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".