Fitts’ Law: On Calculating Throughput and Non-ISO Tasks
Bibliographic record
Abstract
We used a target-selection task to evaluate head-tracking as an input method for mobile devices. First, the method of calculating Fitts’ throughput is described by means of a raw data detailed example. Then, the method of calculating throughput is discussed for non-ISO tasks, since the procedure targets were randomly positioned from trial to trial. Due to a non-constant amplitude within each sequence of trials, throughput was calculated using two methods of data aggregation: the first one, by sequence of trials using the mean amplitude, and the second one, by common A-W conditions. For each data set, we used four methods for calculating throughput. The grand mean for throughput (calculated through the division of means and the adjustment for accuracy) was of 0.74 bps, which is 45 % lower than the value obtained using an ISO task. We recommend to calculate throughput using the division of means plus the adjustment for accuracy, and to avoid using the reciprocal slope of the regression model. We present various design recommendations for non-ISO tasks, such as: i) to keep amplitude and constant target within each sequence of trials, and ii) to use strategies to avoid or remove reaction time. Keywords: Fitts’ law, throughput, ISO 9241-411, mobile HCI, head-tracking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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".