The Impact of Color on Players in Human-machine Interaction in Games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this research is to identify how color affects game players in human-machine interaction. Recently, driven by the faster development and popularity of computer games, the game designers start to use a variety of techniques to enhance players’ experiences and interests in a certain video game. Colors influence game players’ experiences and their behaviors. To make this research influential and convincing, the author mainly utilizes the quantitative research method to explore how color affects the users’ emotions in games. Questionnaires are adopted by randomly selecting almost 100 video game players, who are suggested to respond to their attitudes towards and options for color in influencing their interests and perceptions of a certain game. The findings show that colors can allow players to feel interaction and vitality, provide visual continuity, enhance data visualization, etc. Above all, colors convey more useful information for the human-machine interaction in games, and colors first come to players’ eyes. Therefore, the application of colors plays an essential role in the interface of human-machine interaction to some extent.
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.
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 | 0.000 | 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 it