Dissociating Implicit and Explicit Category Learning Systems using Confidence Reports

Dual-process models of categorization (e.g., COVIS) have relied mostly on double-dissociation paradigms and participants' classification accuracy to highlight differences between explicit and implicit modes of learning. In these models, the implicit system uses procedural learning in the absence of attention whereas the explicit system uses hypothesis-testing requiring attentional resources. These accounts assume that the explicit system dominates early stages of learning whereas the implicit system dominates later stages of learning. Thus, differences in response accuracy over the course of learning and between category structures are taken as evidence for explicit and implicit processes. In four experiments, I will consider the utility of using subjective measures of performance (i.e., confidence reports) to continuously sample from participants’ explicit representation of the category structure while also examining changes in these reports over the course of training. In Experiment 1, participants were presented with stimuli using the randomization technique using either a rule-based or information-integration category structure and provided with trial-to-trial and block feedback. Block feedback was removed in Experiment 2. In Experiment 3, feedback was delayed to interfere with the implicit learning system while leaving the explicit learning system unaffected. Finally, in Experiment 4, the performance asymptote was lowered to increase overconfidence in participants’ performance. Importantly, I observed systematic biases in the relationship between accuracy and confidence reports across training. Confidence reports were more closely associated with explicit representations, produce significant overconfidence for rule-based category structures but only marginally overconfidence for information-integration category structures. These results have important implications for both models of categorization and confidence reports.