pixelLOG：用于认知研究的在线游戏日志记录
pixelLOG: Logging of Online Gameplay for Cognitive Research
AI总结：
pixelLOG是一种用于认知研究的高性能数据收集框架，通过混合方法跟踪多人游戏中的行为，实现高分辨率分析。
摘要：
传统的认知评估通常依赖于孤立的、以输出为导向的测量方法，可能无法捕捉到自然环境中人类认知的复杂性。我们提出了pixelLOG，这是一个针对基于Spigot的Minecraft服务器的高性能数据收集框架，专门用于基于过程的认知研究。与现有仅针对人工智能代理的框架不同，pixelLOG还能够在多人/多代理环境中跟踪人类行为。系统以可配置的频率运行，最高可达并超过每秒20次更新，通过主动状态轮询和被动事件监控的混合方法捕获全面的行为数据。通过利用Spigot可扩展的API，pixelLOG实现了稳健的会话隔离，并生成结构化的JSON输出，可与标准分析流程集成。该框架弥合了脱离上下文的实验室评估与更丰富、更具生态效度的任务之间的差距，使在复杂虚拟环境中认知过程的高分辨率分析成为可能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
