Problem solving is a crucial skill needed to accomplish everyday tasks and overcome potential obstacles. One way to measure individual differences in problem solving ability is through performance differences on multiply-constrained problem solving tasks. Multiple cognitive processes are involved in multiply-constrained problem solving. An individual uses prospective metacognitive monitoring judgments to gauge future allocation of resources before engaging in the necessary semantic search. Problem solvers also vary in their semantic search strategies, and use either an active analytical strategy or a passive insight strategy to arrive at asolution. Prospective metacognitive monitoring judgments and solution strategies are two aspects of the problem solving process that occur at specific points in the process while motivation influences problem solving throughout the process. The goal of this study is to examine prospective metacognitive judgments, problem solving accuracy, solution strategy, and motivation in multiply-constrained problem solving. Motivation was manipulated using a performance based monetary incentive. Participants self reported prospective Feeling-of-Knowing judgments after brief exposure to the problem, and solution strategy ratings after each problem. No significant differences were found to support the effect of motivation on problem solving accuracy, prospective metacognitive judgments, relative accuracy, or solution strategies. Significant differences were found between groups when comparing the number of problems skipped, indicating that participants were sensitive to the incentive structure. The findings suggest that motivation may not be an overarching mediator in multiply-constrained problem solving or problem solving may require a specific type of incentive structure to increase accuracy. However, little is known in the research literature about the type of incentive structure needed to consistently increase individual motivation.
- Pay to Play: Metacognitive Judgements, & Motivation in Multiply-Constrained Problem Solving