Sass Sophia-Helen, Gönner Lorenz, Schwöbel Sarah, Frölich Sascha, Glöckner Franka, Kiebel Stefan J, Li Shu-Chen, Smolka Michael N
Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Würzburger Str. 35, 01187, Dresden, Germany.
Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.
Sci Rep. 2025 May 9;15(1):16260. doi: 10.1038/s41598-025-00905-7.
When planning an action sequence, it has been shown that humans prune decision trees to reduce computational complexity, instead of considering all possible options. However, little is understood about pruning employed in probabilistic environments, where actions result in multiple outcomes with varying probabilities, and how decision biases, such as discounting of probabilistic rewards, influence decisions. This study investigates whether participants prune low-probability options in a three-step decision-making task and analyzes the impact of probability discounting on planning. Potential age-related differences in planning strategies are explored in groups of young (aged 18-35 years; n = 57) and older (aged 65-75 years; n = 50) adults. By using reinforcement-learning modeling and model comparison, we show that participants reduce computational demands by pruning decision tree branches of lower probability-a highly efficient strategy in this environment. Additionally, participants reduce their planning depth, i.e., the number of considered steps. Planning is further influenced by discounting high-probability outcomes. Older individuals show stronger reductions in planning depth, an increase in decision noise, and more pronounced probability discounting, which contributes to the observed age-related decline in planning performance. Our findings suggest directions for future research to elucidate the underlying meta-control mechanisms guiding the application of planning strategies.
在规划一个行动序列时，研究表明，人类会修剪决策树以降低计算复杂度，而不是考虑所有可能的选项。然而，对于概率环境中的修剪，即在行动会导致具有不同概率的多个结果的情况下，以及诸如概率奖励折扣等决策偏差如何影响决策，我们了解得很少。本研究调查了参与者在一个三步决策任务中是否会修剪低概率选项，并分析了概率折扣对规划的影响。在年轻（18至35岁；n = 57）和年长（65至75岁；n = 50）成年人组中探索了规划策略中潜在的年龄相关差异。通过使用强化学习建模和模型比较，我们表明参与者通过修剪较低概率的决策树分支来降低计算需求——这在这种环境中是一种高效策略。此外，参与者会减少他们的规划深度，即所考虑步骤的数量。规划还会受到对高概率结果折扣的影响。年长个体在规划深度上的降低更为明显，决策噪声增加，概率折扣更为显著，这导致了观察到的与年龄相关的规划表现下降。我们的研究结果为未来研究指明了方向，以阐明指导规划策略应用的潜在元控制机制。
