Argus: a suite of tools for research in complex cognition

Area of Science:

• Cognitive science
• Human-computer interaction
• Experimental psychology

Background:

• Cognitive workload measurement and modeling are crucial for understanding complex human behaviors.
• Existing simulated task environments may lack flexibility or comprehensive data handling capabilities.

Purpose of the Study:

• To introduce Argus, a novel simulated task environment for cognitive workload research.
• To detail Argus's features for flexible experimenter control, data collection, and interaction with computational models.
• To describe its application in experimentation and compare it with other complex task environments.

Main Methods:

• Argus simulates a radar-like target classification task.
• The system allows for flexible experimenter control over cognitive workload parameters.
• It incorporates extensive data collection and playback facilities.
• Embodied computational models interact with Argus via the same interface as human subjects.

Main Results:

• Argus provides a flexible and data-rich environment for studying cognitive workload.
• The system supports both single-subject and team-based research paradigms.
• Its design facilitates iterative research into complex human behaviors.

Conclusions:

• Argus offers a versatile platform for cognitive workload research, accommodating human and computational agents.
• The system's features support in-depth analysis of complex behaviors and cognitive processes.
• Argus presents a valuable alternative to existing complex task environments.