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Tracking the Cognitive Band in an Open-Ended Task.
John R Anderson1, Shawn Betts1, Daniel Bothell1
1Department of Psychology, Carnegie Mellon University.
Cognitive Science
|May 22, 2024
Summary
This study analyzed open-ended tasks using Newell's Cognitive Band. The Unit-Task level best matches human behavior and electroencephalogram (EEG) data for tracking cognition.
Area of Science:
- Cognitive Science
- Human-Computer Interaction
- Neuroscience
Background:
- Open-ended tasks present challenges for cognitive modeling and performance tracking.
- Newell's Cognitive Band provides a framework for decomposing complex tasks into hierarchical levels: Unit-Task, Operation, and Deliberate-Act.
Purpose of the Study:
- To analyze the video game Co-op Space Fortress at the three levels of Newell's Cognitive Band.
- To evaluate the match between a cognitive model and subject behavior.
- To investigate the use of electroencephalogram (EEG) for tracking subject cognition during task performance.
Main Methods:
- Analysis of gameplay in Co-op Space Fortress.
- Development and application of a cognitive model.
- Collection and analysis of electroencephalogram (EEG) data from subjects.
- Comparison of model behavior with subject behavior across task levels.
Main Results:
- A strong match was observed between subject behavior and the cognitive model at the Unit-Task level.
- EEG signals effectively tracked the success or failure of Unit Tasks.
- EEG signals at the Operation level were weaker, but allowed for better-than-chance reconstruction of subject play.
- Significant discrepancies were found between subject and model behavior at the Operation level.
- Overlapping key actions at the Deliberate-Act level complicated model interpretation and EEG signal identification.
Conclusions:
- The Unit-Task level is the most appropriate for understanding and tracking performance in open-ended tasks.
- EEG is a viable tool for monitoring cognitive performance, particularly at higher task levels.
- Current cognitive models may require refinement to capture the nuances of lower-level task execution.

