A Cognitive and Affective Neuroergonomics Approach to Game Design
Bibliographic record
Abstract
While the usefulness of games extends beyond their entertainment value, the act of playing a game remains essentially tied to its positive experience. Techniques to assess the player’s experience have greatly improved in the past decade, yet several challenges remain such as identifying objective and dynamic measures that reflect the player’s emotions during the game. In this paper, we describe an innovative approach to capture the player’s experience that relies on cognitive sciences and affective neuroscience. Our research endeavor is to contribute to the development of systems capable of predicting the player’s fun based on psychophysiology and in-game behaviors, and adapting the game to maximize that value. We present a use case of our techniques to elicit the player’s affective and cognitive states using an online strategic card game. Preliminary results revealed that electrodermal and respiratory activities were positively associated to the casual gamers’ affective and cognitive states. Such findings suggest that psychophysiological metrics combined with behavioural measures offer a promising avenue to assess the player’s experience in a comprehensive and objective manner.
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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".