What the body knows: Exploring the benefits of embodied metaphors in hybrid physical digital environments
Bibliographic record
Abstract
A recent trend in ubiquitous computing is the development of new forms of interfaces, which rely on embodied interaction. We focus on the definition of embodiment that refers to the ways in which abstract concepts rely on metaphorical extensions of embodied schemata shaped by processes below the level of conscious awareness as explored by Lakoff and Johnson [Lakoff, G., Johnson, M., 1980. Metaphors We Live By. Chicago Press, Chicago, IL, USA]. Our inquiry focuses on understanding the role embodied metaphors may play in supporting people to understand the possibilities for physical interaction in augmented spaces. We explore this issue through the development and evaluation of an interactive audio environment. We instantiate metaphor theory by using embodied schemata as the basis for the interactional metaphor that relates full-body input actions to audio output responses. We demonstrate and explore the benefits of this approach through a comparative experiment in which adults and children learn to use our audio environment. The results from our experiment indicated that embodied metaphors improve usability however, other factors including discoverability, perceivability of feedback and duplicity of structural isomorphism may mediate these metaphor-based benefits. We have generalized our main findings as a set of suggestions for the design of embodied style interfaces that rely on physical interaction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".