pixelLOG: Logging of Online Gameplay for Cognitive Research
Zeyu Lu, Dennis L. Barbour
TL;DR
pixelLOG addresses the need for ecologically valid cognitive assessment by offering a high-frequency, plugin-based data logger for Spigot Minecraft servers that supports both human players and multi-agent contexts. It combines adaptive, per-player polling with event-driven data capture, and integrates streams into structured JSON suitable for statistical analysis and ML workflows. The framework emphasizes per-player data isolation, scalability to many concurrent players, and flexible output options, including local storage and remote forwarding. By enabling fine-grained behavioral trajectories within rich virtual environments, pixelLOG expands cognitive research beyond laboratory constraints and into dynamic, interactive tasks with high ecological validity.
Abstract
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.