A hallmark of human intelligence is the ability to plan multiple steps into the future^1,2. Despite decades of research^3,4,5, it is still debated whether skilled decision-makers plan more steps ahead than novices^6,7,8. Traditionally, the study of expertise in planning has used board games such as chess, but the complexity of these games poses a barrier to quantitative estimates of planning depth. Conversely, common planning tasks in cognitive science often have a lower complexity^9,10 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. We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times and eye movements. We also perform a Turing test and a reconstruction experiment. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Experts memorize and reconstruct board features more accurately. Using complex tasks combined with precise behavioural modelling might expand our understanding of human planning and help to bridge the gap with progress in artificial intelligence.

人类智能的一个标志是能够规划未来的多个步骤^1,2。尽管进行了数十年的研究^3,4,5，但熟练的决策者是否比新手提前计划更多步骤仍存在争议^6,7,8。传统上，规划专业知识的研究使用国际象棋等棋盘游戏，但这些游戏的复杂性对规划深度的定量估计构成了障碍。相反，认知科学中的常见规划任务通常具有较低的复杂性^9,10，并且对任何玩家可以规划的深度施加了上限。在这里，我们研究了复杂棋盘游戏的专业知识，为熟练的玩家提供了充分的机会进行深入的计划。我们使用模型拟合方法来表明可以使用基于启发式搜索的计算认知模型来捕获人类行为。为了验证这个模型，我们预测人类的选择、反应时间和眼球运动。我们还进行了图灵测试和重建实验。使用该模型，我们找到了利用实验室和大规模移动数据方面的专业知识来增加规划深度的有力证据。专家可以更准确地记忆和重建棋盘特征。使用复杂的任务与精确的行为模型相结合可能会扩大我们对人类规划的理解，并有助于缩小与人工智能进步的差距。