用Minecraft框架追踪玩家行为，实现高精度认知研究
pixelLOG: Logging of Online Gameplay for Cognitive Research
- 通过主动轮询与被动监听结合，每秒最多20次更新采集行为数据
- 支持多人多智能体环境，可区分会话并输出结构化JSON数据
- 适合研究真实虚拟环境中认知过程的学者或游戏心理学研究者
传统认知评估多依赖孤立、以输出为导向的测量，难以捕捉自然情境下人类认知的复杂性。我们提出pixelLOG，一个专为Spigot框架的Minecraft服务器设计的高性能数据采集系统，用于过程导向的认知研究。与仅面向人工智能代理的现有框架不同，pixelLOG还能在多人/多智能体环境中追踪人类行为。系统支持最高超过20次/秒的可配置采样频率，通过主动状态轮询与被动事件监控相结合的方式，全面记录行为数据。依托Spigot的可扩展API，pixelLOG实现稳健的会话隔离，并生成可接入标准分析流程的结构化JSON输出。该框架弥合了脱离情境的实验室评估与更生态有效任务之间的差距，使复杂虚拟环境中认知过程的高分辨率动态分析成为可能。
原文摘要 · Abstract (English)
Traditional cognitive assessments often rely on isolated, output-focused measurements that may fail to capture the complexity of human cognition in naturalistic settings. We present pixelLOG, a high-performance data collection framework for Spigot-based Minecraft servers designed specifically for process-based cognitive research. Unlike existing frameworks tailored only for artificial intelligence agents, pixelLOG also enables human behavioral tracking in multi-player/multi-agent environments. Operating at configurable frequencies up to and exceeding 20 updates per second, the system captures comprehensive behavioral data through a hybrid approach of active state polling and passive event monitoring. By leveraging Spigot's extensible API, pixelLOG facilitates robust session isolation and produces structured JSON outputs integrable with standard analytical pipelines. This framework bridges the gap between decontextualized laboratory assessments and richer, more ecologically valid tasks, enabling high-resolution analysis of cognitive processes as they unfold in complex, virtual environments.
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