Process control and risky decision-making: moderation by general mental ability and need for cognition

Area of Science:

• Cognitive Psychology
• Human Factors Engineering
• Industrial-Organizational Psychology

Background:

• Human factors and ergonomics research often overlooks individual differences, particularly trait variables.
• Understanding these variables is crucial for optimizing performance in complex tasks.
• Previous research has not fully explored the moderating effects of individual differences on process control performance.

Purpose of the Study:

• To investigate the role of individual differences, specifically general mental ability (GMA) and need for cognition (NC), as moderators of process control performance.
• To examine how risky decision-making (RDM) interacts with GMA and NC to influence performance.
• To demonstrate the utility of moderator analysis in understanding trait variables and performance.

Main Methods:

• Fifty engineering students participated in a 3-hour computer-based process control simulation.
• Risky decision-making was assessed using a computerized gambling task.
• General mental ability and need for cognition were measured via questionnaires.
• Moderator analyses were conducted to examine the interaction effects.

Main Results:

• Risky decision-making, in interaction with general mental ability, significantly predicted process control performance.
• Risky decision-making, in interaction with need for cognition, also significantly predicted process control performance.
• These interaction effects explained variance in performance beyond the main effects of the individual variables.

Conclusions:

• The analysis of moderator effects is a promising approach for enhancing the understanding of trait variables in human factors research.
• Individual differences, such as GMA and NC, significantly influence how individuals engage in risky decision-making and perform in process control tasks.
• Moderator analyses provide a valuable framework for uncovering complex relationships between individual characteristics and performance outcomes.