Revealing the impact of expertise on human planning with a two-player board game
Bas van Opheusden, Gianni Galbiati, Ionatan Kuperwajs, Zahy Bnaya, Yunqi li, Wei Ji
Abstract
In recent years, artificial intelligence has made great progress in improving machine performance in tasks that require planning many steps ahead. By comparison, cognitive science has lagged behind in understanding human planning in complex tasks. One question of long-standing interest in this domain is whether skilled decision-makers plan further into the future than novices. Traditionally, the study of expertise in planning has focused on board games like chess, but the complexity of these games poses a barrier to detailed behavioral modeling. Conversely, common planning tasks in cognitive science are often lower-complexity and impose a ceiling for the depth to which any player can plan. Here, we investigate expertise in a complex board game that offers ample opportunity for skilled players to plan deeply. Despite this complexity, we show that human behavior can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times, eye movements and perform a Turing test. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Our results highlight the promise of investigating human planning in complex tasks with precise behavioral modeling.