Factors influencing analysis of complex cognitive tasks: a framework and example from industrial process control

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Engineering Psychology

Background:

• Knowledge elicitation is crucial for analyzing complex cognitive tasks.
• Task factors, including materials and goals, can significantly impact knowledge elicitation effectiveness.
• Understanding how different task presentations affect problem-solving behavior across skill levels is essential for designing effective cognitive systems.

Purpose of the Study:

• To investigate the influence of task materials (dynamic simulation vs. static model) and goals on problem-solving behavior.
• To examine these effects across different expertise levels (expert, intermediate, novice).
• To identify contextual factors influencing knowledge elicitation in complex cognitive tasks.

Main Methods:

• A comparative study using an applied engineering problem (steam generation facility cost minimization).
• Two task versions: dynamic computer-based simulation with feedback and a static computer-based model.
• Participants included experts, intermediates, and novices, with performance measured by problem-solving success, time, and protocol richness.

Main Results:

• Experts outperformed novices and intermediates across material conditions.
• Dynamic simulations led to faster problem-solving but less detailed protocols compared to static models.
• Explicit goals benefited experts more than other skill groups; feedback had limited impact on intermediates and novices.

Conclusions:

• Demonstrating performance differences requires specific task materials, distinct from those needed to explicate underlying knowledge.
• Exploiting dynamic task information or explicit goals necessitates substantial domain knowledge.
• The proposed framework of task factors aids in identifying contextual influences on knowledge elicitation for joint cognitive system engineering.