Gaze behaviour when visually searching for targets to be reached toward is influenced by movement-related costs imposed by obstacles
Bibliographic record
Abstract
Real world action tasks often involve operating in a cluttered environment, in which we search for a target object among distractors. In many cases, the structure of these environments dictate large movement costs associated with reaching the located target (e.g., reaching a cup on a high shelf). While it is well established that people are sensitive to movement costs when selecting between potential movements, it is unclear whether movement costs likewise influence visual search behaviour. Here we tested whether visual search behaviour, as measured by gaze, is biased by the movement costs associated with acting on a target object. In each trial, an obstacle was briefly displayed and then a set of 36 objects, including 4 targets and 32 distractors, were displayed. The length, location, and orientation of the obstacle and the locations of the target objects were randomly varied. The task was to locate a target and then reach for it using a cursor controlled by the handle of a robotic manipulandum. The handle could apply forces simulating any contact between the cursor and the unseen obstacle. The cursor start position was in the center of the display and the objects in a given trial were either on the ‘near’ or ‘far’ side of the obstacle. We found that search, and hence target selection, was biased towards the near side of the obstacle, thus reducing the time and energy costs associated with reaching. This result suggests that humans can readily incorporate the movement costs of an environment when forming visual search strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".