PreGLAM: A Predictive Gameplay-Based Layered Affect Model
Why this work is in the frame
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Bibliographic record
Abstract
We present the Predictive Gameplay-based Layered Affect Model (PreGLAM), an affective game spectator model that flexibly integrates into a game design process. PreGLAM combines elements of real-time Player Experience Models, and Affective Non-Player-Character models to output real-time estimated values for a spectator's valence, arousal, and tension during gameplay. Because tension is related to prospective events, PreGLAM attempts to predict future gameplay events. We implement and evaluate PreGLAM in a custom game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Galactic Defense</i> , which we also describe. PreGLAM significantly outperforms a random walk time series in how accurately it matches ground-truth annotations, and has comparable accuracy to state of the art affect models.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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