Learning categories defined by the relations among objects supports the transfer of knowledge from initial learning contexts to novel contexts that share few surface similarities. Often relational categories have correlated (but nonessential) surface features, which can be a distraction from discovering the category-defining relations, preventing knowledge transfer. This is one explanation for "the inert knowledge problem" in education wherein many students fail to spontaneously apply their learning outside the classroom. Here we present a series of experiments using artificial categories that correlate surface features and relational patterns during learning. Our goal was to determine what task parameters and individual differences in learners shift focus to the relational aspect of the category and foster transfer to novel disparate exemplars. We consistently showed that the effectiveness of task structure manipulations (e.g., the sequence of learning exemplars) depended on the learners' strategies (e.g., whether learners are oriented toward discovering rules or focusing on exemplars). Further, we found support that "inference-learning," wherein learners are presented with incomplete exemplars and learn how to infer the missing pieces, is an effective way to promote relational discovery and transfer, even for learners who are not predisposed to make such discoveries. (PsycINFO Database Record

How do people plan ahead when searching for rewards? We investigate planning in a foraging task in which participants search for rewards on an infinite two-dimensional grid. Our results show that their search is best-described by a model which searches approximately 3 steps ahead. Furthermore, participants do not seem to update their beliefs during planning, but rather treat their initial beliefs as given, a strategy sometimes called root-sampling. This planning algorithm corresponds well with participants' behavior in test problems with restricted movement and varying degrees of information, outperforming more complex models. These results enrich our understanding of adaptive planning in complex environments.