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arXiv AI By Emanuel Dopater, Daniel Gavlas, Luk\'a\v{s} Jochymek, Kannan Paul, Miloslav Szczypka, David Voj\'a\v{c}ek, Josef \v{Z}\'a\v{c}ek, Jan Plucar, Radek Svoboda, Ivan Zelinka

Toward Fast Human Reaction Modeling from High-Frequency Digital Interaction Data

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The paper explores whether rapid, salience-driven behavior can be detected in high‑frequency Counter‑Strike 2 telemetry by reconstructing players’ perceptual states with a millisecond‑precision visibility engine. Using a 60–120 ms reaction window, the authors derive fight‑or‑flight labels and train two telemetry‑native models—EMBER (gradient boosting) and ORBIT (custom transformer)—which outperform a majority‑class baseline and a large language model while requiring far less inference time and cost. Adding a short post‑trigger window further improves accuracy to 85.15%, suggesting a potential probe of the shift from rapid to deliberative control, though the study remains hypothesis‑generating rather than mechanistic.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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