Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks
Bibliographic record
Abstract
Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising visualizations that can detect a user's abilities and adapt accordingly during the interaction. In this article, we extend previous user modeling work by investigating for the first time interaction data as an alternative source to predict cognitive abilities during visualization processing when it is not feasible to collect eye-tracking data. We present an extensive comparison of user models based solely on eye-tracking data, on interaction data, as well as on a combination of the two. Although we found that eye-tracking data generate the most accurate predictions, results show that interaction data can still outperform a majority-class baseline, meaning that adaptation for interactive visualizations could be enabled even when it is not feasible to perform eye tracking, using solely interaction data. Furthermore, we found that interaction data can predict several cognitive abilities with better accuracy at the very beginning of the task than eye-tracking data, which are valuable for delivering adaptation early in the task. We also extend previous work by examining the value of multimodal classifiers combining interaction data and eye-tracking data, with promising results for some of our target user cognitive abilities. Next, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from either eye-tracking data and interaction data. Finally, we evaluate how noise in gaze data impacts prediction accuracy and find that retaining rather noisy gaze datapoints can yield equal or even better predictions than discarding them, a novel and important contribution for devising adaptive visualizations in real settings where eye-tracking data are typically noisier than in laboratory settings.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".