Department of Experimental Psychology, Ghent University, Ghent, Belgium.
PLoS Comput Biol. 2024 Feb 20;20(2):e1011312. doi: 10.1371/journal.pcbi.1011312. eCollection 2024 Feb.
Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.
人类具有制定抽象、长期且层次化的计划的能力。例如,在考虑如何做晚餐意大利面时,我们通常会关注任务中的有用“子目标”,例如切洋葱、煮面条和做酱汁,而不是具体到要对洋葱进行多少次切割,或者确切地收缩哪些肌肉。一个核心问题是这种将更抽象的任务分解为逻辑子任务的过程是如何首先发生的。先前的研究表明,人类对一种名为“社区结构”的高阶统计学习形式敏感。社区结构是具有逻辑子任务排序的抽象任务的共同特征。这种结构可以通过一种模型来捕捉,在该模型中,人类学习对未来多个步骤中即将发生的事件的预测,同时对时间更远的事件的预测进行折扣。其中一种模型是“后继表示”,有人认为它对于层次抽象很有用。到目前为止,还没有研究令人信服地表明这种层次抽象可以用于目标导向的行为。在这里,我们研究参与者是否利用所学的社区结构来制定具有层次信息的目标导向行为行动计划。参与者被要求在虚拟博物馆中搜索绘画作品,这些绘画作品被分组在一起,形成代表博物馆中社区结构的“翅膀”。我们发现,参与者的选择与博物馆的层次结构一致,并且他们的反应时间可以通过后继表示来最好地预测。反应时间反映博物馆社区结构的程度与多项绩效指标相关,包括制定具有时间抽象性的行动计划的能力。这些结果表明,后继表示学习支持与目标导向行为相关的层次抽象。