Individual differences in gaze behavior: Comparing high-level and sensory contributions
Bibliographic record
Abstract
Individual differences in gaze behavior are well-established across a range of tasks and stimuli, from examining natural scenes to visual search, but their source is less well-understood. Some differences may arise from variation in high-level strategies for a specific visual task, while others may arise from individual differences in sensory processing (e.g., variation in visual crowding). To examine the contributions of these factors to individual differences in scan paths, we asked participants to perform one of two tasks (a visual search task or an ensemble perception task) on the same stimulus array, consisting of a set of radial frequency patterns arranged in an annulus around an initial central fixation. In the ensemble task, participants reported the average amplitude of the patterns, and in the visual search task, they reported whether a four-pointed shape was present among five-pointed shapes. In each task, we found large individual differences in scan paths between participants, with large variability in initial saccade direction. As expected, in each task, we observed higher trial-to-trial correlations in scan paths within than between participants. These correlations were affected by task alone, since the stimuli were identical; trial-to-trial correlations were higher within participants in our ensemble task than in our search task, indicating that strategies based on task demands contribute to these differences. Finally, to measure the contributions of differences in peripheral vision, we separately measured local differences in the strength of crowding for each participant. We measured critical spacing at a set of 12 peripheral locations around the annulus, comparing these values to participants’ initial saccade direction. This revealed a correlation (r = .21) between the proportion of initial saccades to a given location and critical spacing at the corresponding location. Together, these results suggest that high-level differences and sensory differences contribute differently to idiosyncrasies in participants’ gaze behavior.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".