A Predictive Model of Human Performance With Scrolling and Hierarchical Lists
Bibliographic record
Abstract
Many interactive tasks in graphical user interfaces involve finding an item in a list but with the item not currently in sight. The two main ways of bringing the item into view are scrolling of one-dimensional lists and expansion of a level in a hierarchical list. Examples include selecting items in hierarchical menus and navigating through “tree” browsers to find files, folders, commands, or e-mail messages. System designers are often responsible for the structure and layout of these components, yet prior research provides conflicting results on how different structures and layouts affect user performance. For example, empirical research disagrees on whether the time to acquire targets in a scrolling list increases linearly or logarithmically with the length of the list; similarly, experiments have produced conflicting results for the comparative efficacy of “broad and shallow” versus “narrow and deep” hierarchical structures. In this article we continue in the human–computer interaction tradition of bringing theory to the debate, demonstrating that prior results regarding scrolling and hierarchical navigation are theoretically predictable and that the divergent results can be explained by the impact of the dataset's organization and the user's familiarity with the dataset. We argue and demonstrate that when users can anticipate the location of items in the list, the time to acquire them is best modeled by functions that are logarithmic with list length and that linear models arise when anticipation cannot be used. We then propose a formal model of item selection from hierarchical lists, which we validate by comparing its predictions with empirical data from prior studies and from our own. The model also accounts for the transition from novice to expert behavior with different datasets.
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".