2008 Annual Meeting of the Belgian Association for Psychological Science pages:63-63
Belgian Association for Psychological Science location:Leuven date:26 May 2008
Performance on probabilistic reasoning tasks varies widely, yet people can take advantage of data attributes supplied to construct better mental representations and outperform their peers. It is those people who exhibit combinatorial reasoning competence in their reasoning strategy that outperform others in uncertain probabilistic tasks.
200 participants solved problems with binomial chance situations (coins and dice) to provide either probability estimates or ranks of proposed alternatives. The test group gets questions formulated to facilitate the distinction between mental representations, which otherwise are confounded (control condition). The statistical reasoning assessment used to measure combinatorial reasoning competence require respondents to compare multiple combinations of outcomes and make one choice.
We observe that in a probability estimating task with binomial chance situations, only 16% of participants get it right. However, reformulating the problem as ranking of alternatives has a direct impact on performance, eliciting 71% correct responses on average, which is significantly better than guessing. Further improvement occurs when the description of a coin tossing problem is written with a distinguishing attribute such as gold versus silver coin, lifting the performance to 79% correct, compared with 64% correct in the control condition. This significant improvement in performance correlates with combinatorial reasoning competence: participants who exhibit some combinatorial competence have the biggest increase in correct answers.
Facilitation in the construction of correct mental models is necessary to improve performance; but only people with the adequate combinatorial reasoning strategy can effectively utilise the information attributes tagged to their mental models.