pixelLOG: Logging of Online Gameplay for Cognitive Research

2026-02-09

AI Summary

Traditional cognitive assessments often rely on static output metrics, which fail to capture the dynamic nature of cognitive processes in naturalistic settings. To address this limitation, this work proposes a high-performance behavioral logging framework tailored for multiplayer Minecraft environments, enabling, for the first time, synchronized tracking of both human and AI agent trajectories in multi-player/multi-agent scenarios. Built upon the Spigot API, the framework integrates active state polling with passive event listening to generate structured JSON logs at over 20 Hz, achieving high temporal resolution while maintaining strong ecological validity. This approach effectively bridges the gap between controlled laboratory paradigms and real-world task demands, offering a scalable and analysis-ready tool for process-oriented cognitive research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• cognitive assessment
• naturalistic settings
• ecological validity
• behavioral tracking
• process-based research

Innovation

• pixelLOG
• behavioral tracking
• process-based cognitive research
• Minecraft
• ecological validity