What is Intuitive Interaction? Balancing Users' Performance and Satisfaction with Natural User Interfaces
Bibliographic record
Abstract
Designers of natural user interfaces are faced with several challenges when creating interaction models for controlling applications, including the wide range of possible input actions and the lack of affordances, which they can use to design controls. In order to contribute to the development of design guidelines in this design space, we conducted an exploratory, mixed methods study. We investigated three top-down approaches to designing intuitive interaction mappings for a whole body system implemented with camera vision. These were metaphoric, isomorphic and ‘everyday’ or conventional. In order to identify some of the benefits and limitations of each approach, we compared the designs based on measures of usability, intuitiveness and engagement with the material represented in the system. From our study, we found that while the metaphoric design enhanced users’ performance at completing tasks, the lack of discoverability of the interaction model left them feeling incompetent and dissatisfied. We found that the isomorphic design enabled users to focus on tasks rather than learning how to use the system. Conversely, designs based on previous conventions had to be learned, had a time cost for the learning and negatively impacted users’ engagement with content. For tasks and controls that can be designed based on an image schematic input action, users performed most accurately with the metaphoric design. There are benefits and limitations to each approach to designing to support intuitive interaction. We conclude with preliminary design considerations, suggest ways to balance performance with high user satisfaction depending on contextual design goals and question a single definition of intuitive intuition within whole body interface design.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".