Latency, jitter, and dropouts in human pointing performance
Bibliographic record
Abstract
The effects of latency, latency jitter, spatial jitter, and signal dropouts are evaluated for tasks involving acquiring stationary targets (pointing) and following moving targets (pursuit tracking). The properties of the computing system are thoroughly measured and the experiments were carefully designed to separate all the measured effects. The results of the experiments are incorporated into predictive models allowing one to calculate the effects of the aforementioned factors on pointing throughput and tracking errors in advance, i.e., without performing time-consuming empirical evaluations. For tracking tasks, the throughput measure is computed. Although this measure was introduced as early as in 1960, it has not been widely adopted. Using the same metric of throughput for two input tasks of different nature provides an insight at how the fundamental efficiencies of different input modes compare and can be used to estimate the speed at which information could be manually entered into a computer. Parallels between this measure, applied to a tracking task, and the Steering Law, applied to a task in which one needs to navigate a pointer through a tunnel, are highlighted. The findings can be used in designing human-computer interaction scenarios, in particular, when developing electronic games controlled by pointing devices. In addition, device throughput as a measure can be used to predict the theoretical limit of information entry rate with a pointing device both in pointing and in tracking mode.
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 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 | 0.003 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".