Sensor Noise and Ecological Interface Design: Effects of Increasing Noise Magnitude on Operators' Performance
Bibliographic record
Abstract
We studied the impact of sensor noise on operators' performance using a display based on the Ecological Interface Design (EID) framework with a representative thermal-hydraulic process simulation. A previous study conducted by St-Cyr and Vicente (2004) showed no difference between EID and non-EID interfaces when the magnitude of sensor noise was randomly increased. In this paper, we describe a study that was designed to investigate the impact of gradually increasing the magnitude of sensor noise on EID versus non-EID interfaces. We hypothesized that as the magnitude of sensor noise increase, performance would worsen for both EID and non-EID participants. Our results suggest that increasing the magnitude of sensor noise does compromise both EID and non-EID interfaces. However, the EID group experienced a significantly larger decrease in performance. This may be explained by the fact that participants in the EID group had to deal with distorted emergent features.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".