The Impact of Motion and Delay on Selecting Game Targets with a Mouse
Bibliographic record
Abstract
All real-time computer games, particularly networked computer games, have a delay from when a player starts an action (e.g., clicking the mouse) until the game renders the result (e.g., firing a projectile). This delay can degrade both player performance (e.g., reduced game score) and quality of experience (e.g., the game is less fun). While previous work has studied the effects of delay on commercial games and individual game actions, a more detailed understanding is needed of the effects of delay on moving target selection with realistic target motion, a common scenario in many games. This paper presents an in-depth study of the effects of delay on the fundamental game action of selecting a moving target with a mouse while varying two parameters for the target motion – turn frequency and turn angle. We design and implement a custom game where players select moving targets using a mouse, while the game controls both the target motion and input delay. Analysis of data gathered in a 56-person user study shows both target selection time and accuracy degrade with delay. However, both selection time and accuracy increase with the frequency and angle of changes in the target’s movement, because turning slows targets down even while making them less predictable. We set these results in the context of other studies of delay and target selection by comparing our findings to those in seven other previously published papers that investigated the effects of delay on target selection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".