Evaluation of a Contextual Assistant Interface Using Cognitive Models
Bibliographic record
Abstract
Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces, and also simulating the interaction with these interfaces. The action of predicting is based on a task analysis which analyses what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the functionalities of the system to be modeled. Cognitive models are part of the analytical approaches that do not make necessarily appeal to the user during the interface development process. This paper presents a study about the evaluation of a human machine interaction (HMI) with an interface of a contextual assistant, using ACT-R and GOMS cognitive models. It shows how these techniques may be applied in HMI evaluation, design and research, emphasizing on the task analysis in one side, and on the time execution of tasks in the other side. In order to validate and support our results, an experimental study of user performance, during the interaction with the contextual assistant interface is conducted at the DOMUS laboratory. The results of our models show that both models GOMS and ACT-R give good to very good predictions of user performance at the task level as well as the object level, our results are very close to those obtained in the experimental study. Keywords—HMI, interface evaluation, cognitive modeling, user modeling, user performance. I.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".