AbstractTo behave adaptively, people must choose actions that maximize their expected future rewards. Engaging in such goal-directed decision-making in turn requires the capacity to (1) develop an internal model of one’s environment (i.e., representing the relationship between current and future states; structure inference), and (2) navigate this cognitive model to determine the action(s) that will lead to the most rewarding future state (model-based planning). While previous work has identified putative mechanisms underlying these two processes, it has yet to test the prediction that one’s ability to infer structure should constrain their ability to engage in model-based planning. Here we test this prediction using a novel task we developed to specifically isolate individual differences in structure inference ability. Participants (N=77) viewed a series of object pairs. Unbeknownst to them, each pair was drawn at random from adjacent nodes in an underlying graph. They then performed two tasks that measured the extent to which participants were able to infer the graph structure from these disjointed pairs: (1) judging the relative distances of sets of three nodes, (2) constructing the graph. We identified a single underlying factor that captured variability in performance across these tasks, and showed that this variability in this measure of structure inference ability was selectively associated with the extent to which participants exhibited model-based planning in the two-step task (Daw et al., 2011), a well-characterized assay of such behavior. Our work validates a new method for isolating one’s capacity for structure inference, and confirms that individuals who are more limited in this capacity are less likely to engage in model-based planning. These findings bridge separate areas of research that examine goal-directed planning and its component processes. They further provide a path towards better understanding deficits in these component processes, and how they constrain one’s ability to achieve long-term goals.
Hierarchical Bayesian Approach to Estimating Variability of Liquefaction Resistance of Sandy Soils Considering Individual Differences in Laboratory Tests
