人类从带有解释性反馈的简短教程中进行系统学习和归纳。

Q1 Social Sciences Open Mind Pub Date : 2024-03-01 eCollection Date: 2024-01-01 DOI:10.1162/opmi_a_00123
Andrew J Nam, James L McClelland
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引用次数: 0

摘要

我们研究了人类成年人快速学习抽象推理任务以及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题策略的任务,通过使用范围较窄的训练示例,为未接触过数独的参与者提供简短的教学指导和解释性反馈。我们发现,大多数参与者都能在 10 次练习测试内掌握任务,并能很好地泛化到训练范围之外的谜题中。我们还发现,大多数掌握任务的参与者都能描述有效的解题策略,而且这些参与者在转移谜题中的表现要好于那些策略描述模糊或不完整的参与者。有趣的是,只有不到一半的人类参与者能够成功获得有效的解题策略,而这种能力与高中代数和几何的学业成绩有关。我们考虑了这些发现对理解人类系统推理的影响,以及这些发现对建立能捕捉我们发现的所有方面的计算模型所提出的挑战,并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。
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Systematic Human Learning and Generalization From a Brief Tutorial With Explanatory Feedback.

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.

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来源期刊
Open Mind
Open Mind Social Sciences-Linguistics and Language
CiteScore
3.20
自引率
0.00%
发文量
15
审稿时长
53 weeks
期刊最新文献
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