A model of novice and expert navigation performance in constrained-input interfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many interactive systems require users to navigate through large sets of data and commands using constrained input devices—such as scroll rings, rocker switches, or specialized keypads—that provide less power and flexibility than traditional input devices like mice or touch screens. While performance with more traditional devices has been extensively studied in human-computer interaction, there has been relatively little investigation of human performance with constrained input. As a result, there is little understanding of what factors govern performance in these situations, and how interfaces should be designed to optimize interface actions such as navigation and selection. Since constrained input is now common in a wide variety of interactive systems (such as mobile phones, audio players, in-car navigation systems, and kiosk displays), it is important for designers to understand what factors affect performance. To aid in this understanding, we present the Constrained Input Navigation (CIN) model, a predictive model that allows accurate determination of human navigation and selection performance in constrained-input scenarios. CIN identifies three factors that underlie user efficiency: the performance of the interface type for single-level item selection (where interface type depends on the input and output devices, the interactive behavior, and the data organization), the hierarchical structure of the information space, and the user's experience with the items to be selected. We show through experiments that, after empirical calibration, the model's predictions fit empirical data well, and discuss why and how each of the factors affects performance. Models like CIN can provide valuable theoretical and practical benefits to designers of constrained-input systems, allowing them to explore and compare a much wider variety of alternate interface designs without the need for extensive user studies.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it