pixelLOG: Logging of Online Gameplay for Cognitive Research
This is an incremental tool for cognitive researchers, bridging the gap between laboratory assessments and ecologically valid tasks in virtual environments.
The authors tackled the problem of traditional cognitive assessments being too isolated by developing pixelLOG, a high-performance data collection framework for Minecraft servers that captures human behavioral data at up to 20 updates per second, enabling high-resolution analysis of cognitive processes in complex virtual environments.
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.