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.

翻译：传统认知评估通常依赖于孤立且以输出为导向的测量方法，这些方法可能难以捕捉自然情境下人类认知的复杂性。我们提出了pixelLOG，一个专为基于过程的认知研究设计、面向Spigot架构Minecraft服务器的高性能数据采集框架。与现有仅适用于人工智能体的框架不同，pixelLOG还支持在多玩家/多智能体环境中进行人类行为追踪。该系统以最高可超过每秒20次更新的可配置频率运行，通过主动状态轮询与被动事件监测相结合的混合方法，捕获全面的行为数据。通过利用Spigot的可扩展API，pixelLOG实现了稳健的会话隔离，并生成可与标准分析流程集成的结构化JSON输出。该框架在去情境化的实验室评估与更丰富、更具生态效度的任务之间架起了桥梁，使得在复杂虚拟环境中展开的认知过程能够进行高分辨率分析。