One piece at a time: Learning complex rules through self-directed sampling
Douglas Benjamin Markant, Todd Matthew Gureckis · eScholarship (California Digital Library) · 2012
Self-directed information sampling—the ability to collect in-formation that one expects to be useful—has been shown to improve the efficiency of concept acquisition for both human andmachine learners. However, little is known about how peo-ple decide which information is worth learning about. In this study, we examine self-directed learning in a relatively complex rule learning task that gave participants the ability to “design and test ” stimuli they wanted to learn about. On a subset of trials we recorded participants ’ uncertainty about how to clas-sify the item they had just designed. Analyses of these uncer-tainty judgments show that people prefer gathering informa-tion about items that help reĕne one rule at a time (i.e., those that fall close to a pairwise category “margin”) rather than items that have the highest overall uncertainty across all relevant hy-potheses or rules. Our results give new insight into how people gather information to test currently entertained hypotheses in complex problem solving tasks.