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人类问题解决中的适应性规划深度。

Adaptive planning depth in human problem-solving.

作者信息

Eluchans Mattia, Lancia Gian Luca, Maselli Antonella, D'Alessandro Marco, Gordon Jeremy Raboff, Pezzulo Giovanni

机构信息

Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.

Sapienza University of Rome, Roma, Lazio, Italy.

出版信息

R Soc Open Sci. 2025 Apr 9;12(4):241161. doi: 10.1098/rsos.241161. eCollection 2025 Apr.

Abstract

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them is not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources-namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. These results support the view of problem-solving as a bounded rational process, which adapts costly cognitive resources to task demands.

摘要

我们人类有能力解决具有挑战性的规划问题,但我们用于解决这些问题的一系列适应性策略尚未得到充分描述。在这里,我们设计了一系列需要在不同深度进行规划的解决问题任务。在系统地比较了参与者和规划模型的表现后,我们发现,当面对需要规划一定数量子目标(从1到8)的问题时,参与者会适应性地利用他们的认知资源——也就是说,他们倾向于选择具有所需最小深度的初始计划,而不是为所有问题选择相同的深度。这些结果支持将解决问题视为一个有限理性过程的观点,即根据任务需求调整成本高昂的认知资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9b5/11978448/d937574db964/rsos.241161.f001.jpg

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