Bibliographic record
Abstract
In the field of human-machine interaction, it is generally agreed that emotion may affect the users’ response behavior. With this consensus, interfaces of the machine are developed, among which a kind of interface that shows the machine state with human emotions is less studied. This kind of machine interface is called emotional or affective interface. This thesis studied affective interfaces. Two research questions were identified in the study: (1) how an affective interface is built and (2) whether an affective interface of a consumer device (e.g., personal digit assistant, personal computer, cellular phone, etc.) would significantly affect human’s emotion, judgment and decision making behavior?\n\nCorresponding to the two research questions, this research consisted of three works along with their respective objectives: (1) developed a general design approach to affective interfaces; (2) constructed an affective interface test-bed with high fidelity, which is a laptop computer with an attention to its internal temperature state, by using the developed approach in (1); (3) conducted experiments to show that the affective interface of the test-bed has a significant effect on human emotion, judgment and decision making. \n\nThis research concluded (1) the general interface design approach, as developed, is valid to all interfaces including the affective element in human-machine interactions, (2) the human judgment and decision making behaviour will be significantly affected by the affective interface (with the confidence interval being 95%), and (3) affective interfaces significantly change human emotions.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".