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From navigation in unfamiliar environments to career plan- ning, people typically first sample information before com- mitting to a plan. However, most studies find that people adopt myopic strategies when sampling information. Here we challenge those findings by investigating whether contingency planning is a driver of information sampling. To this aim, we developed a novel navigation task that is a shortest path find- ing problem under uncertainty of bridge closures. Participants (n = 109) were allowed to sample information on bridge sta- tuses prior to committing to a path. We developed a computa- tional model in which the agent samples information based on the cost of switching to a contingency plan. We find that this model fits human behavior well and is qualitatively similar to the approximated optimal solution. Together, this suggests that humans use contingency planning as a driver of information sampling. 

One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without ex- plicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later. 

Psychological research on learning and memory has tended to emphasize small-scale laboratory studies. However, large datasets of people using educational software provides opportunities to explore these issues from a new perspective. In this paper we describe our approach to the Duolingo Second Language Acquisition Modeling (SLAM) competition which was run in early 2018. We used a well-known class of algorithms (gradient boosted decision trees), with features partially informed by theories from the psychological literature. After detailing our modeling approach and a number of supplementary simulations, we reflect on the degree to which psychological theory aided the model, and the potential for cognitive science and predictive modeling competitions to gain from each other.