Nam Andrew J, McClelland James L
Department of Psychology, Stanford University, Stanford, CA, USA.
Open Mind (Camb). 2024 Mar 1;8:148-176. doi: 10.1162/opmi_a_00123. eCollection 2024.
We investigate human adults' ability to learn an abstract reasoning task quickly and to generalize outside of the range of training examples. Using a task based on a solution strategy in Sudoku, we provide Sudoku-naive participants with a brief instructional tutorial with explanatory feedback using a narrow range of training examples. We find that most participants who master the task do so within 10 practice trials and generalize well to puzzles outside of the training range. We also find that most of those who master the task can describe a valid solution strategy, and such participants perform better on transfer puzzles than those whose strategy descriptions are vague or incomplete. Interestingly, fewer than half of our human participants were successful in acquiring a valid solution strategy, and this ability was associated with completion of high school algebra and geometry. We consider the implications of these findings for understanding human systematic reasoning, as well as the challenges these findings pose for building computational models that capture all aspects of our findings, and we point toward a role for learning from instructions and explanations to support rapid learning and generalization.
我们研究了成年人类快速学习抽象推理任务并在训练示例范围之外进行泛化的能力。我们使用一个基于数独解决方案策略的任务，为初次接触数独的参与者提供一个简短的指导性教程，并使用有限范围的训练示例给予解释性反馈。我们发现，大多数掌握该任务的参与者在10次练习试验内就能做到，并且能很好地将其泛化到训练范围之外的谜题上。我们还发现，大多数掌握该任务的人能够描述有效的解决策略，而且这些参与者在迁移谜题上的表现比那些策略描述模糊或不完整的参与者更好。有趣的是，我们的人类参与者中成功获得有效解决策略的不到一半，而这种能力与完成高中代数和几何课程有关。我们考虑了这些发现对于理解人类系统推理的意义，以及这些发现对构建能够捕捉我们所有发现方面的计算模型所带来的挑战，并且我们指出了从指令和解释中学习以支持快速学习和泛化的作用。
