Date of Award
Level of Access Assigned by Author
Doctor of Philosophy (PhD)
Second Committee Member
Third Committee Member
Additional Committee Members
J. Caleb Speirs
Uncertainty is commonly experienced by many people during learning and decision making. Given that many career paths require the ability to monitor uncertainty, it’s important to understand how metacognitive processes influence cognitive performance. In attempts to explore how uncertainty monitoring impacts learning, three experiments were conducted. The first and second experiment utilized a categorization task in which participants explicitly learned to categorize Chemistry concepts. The third experiment assessed the impact of uncertainty monitoring on implicit learning and utilized a different task to tap into the implicit learning system. The present dissertation is one of few to investigate the role of uncertainty monitoring during explicit and implicit category learning within the context of education. Findings from Experiment 1 revealed an overall benefit of uncertainty monitoring. Performance was superior for participants who had the option to report uncertainty compared to participants who did not. Experiment 2 was designed to replicate the results from Experiment 1 and investigated other training factors and metacognitive processes that impact performance. Specifically, Experiment 2 assessed the role of feedback on performance by matching task feedback across training conditions. Additional measures of metacognition were implemented in Experiment 2 to examine participants’ confidence and judgements of performance. Findings from Experiment 2 generally supported those from Experiment 1 as it replicated the advantage of uncertainty monitoring training on task accuracy and revealed that participants’ confidence and judgments of performance were influenced by a combination of training factors that help monitor and address decision uncertainty. Experiment 3 expanded upon the results from Experiments 1 and 2 and assessed whether uncertainty monitoring could also support implicit learning. Results revealed a marginal enhancement in task accuracy during the initial stages of learning; however, enhancements did not remain after learning was complete. Taken together, the present experiments suggest a general benefit of uncertainty monitoring on explicit learning and transfer. However, these benefits may be limited in supporting different types of learning, as enhancements were not as pronounced during implicit learning. This research has important implications for cognitive science and education as it highlights the benefits and limits of uncertainty monitoring on category learning.
Deng, Rose, "Uncertainty Monitoring in Category Learning and Transfer" (2022). Electronic Theses and Dissertations. 3723.