Evaluation of a Contextual Assistant Interface Using Cognitive Models
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
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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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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