Gaze behaviour: a window into quantifying task difficulty and performance using the Tower of London Task
Bibliographic record
Abstract
Actively deciding where to direct our gaze is crucial to the acquisition of visual information regarding our surroundings. Previous studies have demonstrated the potential of examining gaze behaviour to establish overt indices of cognitive processes, such as attention, visuospatial planning and problem solving. The current study aimed to characterize the eye movement pattern during visuospatial planning and problem solving using the Tower of London (TOL) task. Participants (n=9) were shown a series of pictures depicting coloured balls arranged in three columns above fixation (i.e., Goalspace) and below fixation (i.e., Workspace). The task was to plan and execute the shortest movement sequence required to match the ball arrangement in the workspace to that of the goalspace. Participants completed the task across 4 difficulty levels (i.e., optimal sequence lengths 3-6). Our results demonstrated that as task difficulty increased, dwell time, saccade frequency, gaze alternations between goal- and workspace, and saccade path length increased significantly (p<0.01). Notably, non-optimal trials, where participants used more moves than necessary to solve the problem, were associated with longer fixations in areas of the display that were not relevant to the task goal during the initial planning interval (X=375 ms) compared to optimal trials (X=285 ms) (p=0.034). This suggests that fixating on irrelevant areas might interfere with information processing and problem solving. Furthermore, analysis revealed that initial gaze location had a significant influence on initial planning time. Specifically, trials with initial fixations directed to the goalspace were associated with longer initial thinking times (X=7606 ms) compared to the workspace (X=5084 ms) (p=0.021). This finding suggests that initial gaze location contributes to the efficiency of TOL performance. We conclude that gaze behaviour analyses provide useful insights into task difficulty and corresponding behavioural performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".