Using Eye Tracking to Expose Cognitive Processes in Understanding Conceptual Models1
Bibliographic record
Abstract
Conceptual models are used to communicate information about a domain during the development of information systems. In two experimental studies using business process models, we demonstrate how eye tracking can contribute to understanding the cognitive processes by which readers use conceptual modeling scripts to perform problem solving tasks. In the first study, we compare scripts generated using two process modeling grammars and demonstrate how attention paid to specific parts of scripts generated using grammar variations, and differences in visual association between parts of a diagram, account for task performance. In the second study, we use a combination of eye tracking and verbal protocol analysis to examine how visual association between parts of conceptual modeling scripts can indicate cognitive integration while performing problem solving tasks. The studies show that task performance can be explained with different mental processes, reflected in specific eye tracking behavior, where scripts developed following different rules invoke different cognitive processes. We show that attention can be measured by eye tracking and can explain task performance. In addition, we show that visual association (which is observable) between parts of a modeling script involves cognitive integration (which is not observable). This finding can be used to improve conceptual modeling grammars in several ways, including understanding the effects of alternative visual arrangements of models on how effectively they communicate domain knowledge for particular tasks, and guiding the design of visual modeling notations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".