Department of Psychology, Princeton University, Princeton, New Jersey, United States of America.
Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
PLoS Comput Biol. 2024 Sep 27;20(9):e1012471. doi: 10.1371/journal.pcbi.1012471. eCollection 2024 Sep.
A fundamental feature of the human brain is its capacity to learn novel motor skills. This capacity requires the formation of vastly different visuomotor mappings. Using a grid navigation task, we investigated whether training variability would enhance the flexible use of a visuomotor mapping (key-to-direction rule), leading to better generalization performance. Experiments 1 and 2 show that participants trained to move between multiple start-target pairs exhibited greater generalization to both distal and proximal targets compared to participants trained to move between a single pair. This finding suggests that limited variability can impair decisions even in simple tasks without planning. In addition, during the training phase, participants exposed to higher variability were more inclined to choose options that, counterintuitively, moved the cursor away from the target while minimizing its actual distance under the constrained mapping, suggesting a greater engagement in model-based computations. In Experiments 3 and 4, we showed that the limited generalization performance in participants trained with a single pair can be enhanced by a short period of variability introduced early in learning or by incorporating stochasticity into the visuomotor mapping. Our computational modeling analyses revealed that a hybrid model between model-free and model-based computations with different mixing weights for the training and generalization phases, best described participants' data. Importantly, the differences in the model-based weights between our experimental groups, paralleled the behavioral findings during training and generalization. Taken together, our results suggest that training variability enables the flexible use of the visuomotor mapping, potentially by preventing the consolidation of habits due to the continuous demand to change responses.
人类大脑的一个基本特征是其学习新运动技能的能力。这种能力需要形成截然不同的视动映射。我们使用网格导航任务，研究了训练变异性是否会增强视动映射（键到方向规则）的灵活使用，从而提高泛化性能。实验 1 和 2 表明，与仅在一对起始目标之间移动的参与者相比，在多个起始目标之间进行训练的参与者表现出更好的向远距离和近距离目标的泛化能力。这一发现表明，即使在没有规划的简单任务中，有限的变异性也会损害决策。此外，在训练阶段，接触更高变异性的参与者更倾向于选择选项，这些选项违反直觉地将光标移离目标，同时在受约束的映射下最小化其实际距离，这表明他们更倾向于基于模型的计算。在实验 3 和 4 中，我们表明，通过在学习早期引入短时间的变异性，或通过在视动映射中引入随机性，可以提高仅在一对目标之间进行训练的参与者的有限泛化性能。我们的计算模型分析表明，在训练和泛化阶段具有不同混合权重的无模型和基于模型计算之间的混合模型，最能描述参与者的数据。重要的是，我们实验组之间基于模型的权重差异，与训练和泛化过程中的行为发现相平行。综上所述，我们的结果表明，训练变异性可以通过不断要求改变反应来防止习惯的巩固，从而实现视动映射的灵活使用。
