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儿童有意识递归推理中的模拟

Simulation in children's conscious recursive reasoning.

作者信息

Bucciarelli M, Mackiewicz R, Khemlani S S, Johnson-Laird P N

机构信息

Dipartimento di Psicologia and Centro di Logica, Linguaggio e Cognizione, Università di Torino, 10123, Torino, Italy.

Department of Psychology, University of Social Sciences and Humanities, Warsaw, Poland.

出版信息

Mem Cognit. 2018 Nov;46(8):1302-1314. doi: 10.3758/s13421-018-0838-0.

Abstract

When do children acquire the ability to understand recursion-that is, repeated loops of actions, as in cookery recipes or computer programs? Hitherto, studies have focused either on unconscious recursions in language and vision or on the difficulty of conscious recursions-even for adults-when learning to program. In contrast, we examined 10- to 11-year-old fifth-graders' ability to deduce the consequences of loops of actions in informal algorithms and to create such algorithms for themselves. In our experiments, the children tackled problems requiring the rearrangement of cars on a toy railway with a single track and a siding-an environment that in principle allows for the execution of any algorithm-that is, it has the power of a universal Turing machine. The children were not allowed to move the cars, so each problem's solution called for them to envision the movements of cars on the track. We describe a theory of recursive thinking, which is based on kinematic simulations and which we have implemented in a computer program embodying mental models of the cars and track. Experiment 1 tested children's ability to deduce rearrangements of the cars in a train from descriptions of algorithms containing a single loop of actions. Experiment 2 assessed children's spontaneous creation of similar sorts of algorithms. The results showed that fifth-grade children with no training in computer programming have systematic abilities to deduce from and to create informal recursive algorithms.

摘要

儿童何时获得理解递归的能力,即理解像烹饪食谱或计算机程序中那样的重复动作循环?迄今为止,研究要么聚焦于语言和视觉中的无意识递归,要么聚焦于即使对成年人来说在学习编程时有意识递归的难度。相比之下,我们研究了10至11岁五年级学生推断非正式算法中动作循环的结果以及自行创建此类算法的能力。在我们的实验中,孩子们解决的问题是在一条单轨带一个侧轨的玩具铁路上重新排列火车车厢——这样一个原则上允许执行任何算法的环境,也就是说,它具有通用图灵机的能力。孩子们不被允许移动车厢,所以每个问题的解决方案都要求他们想象车厢在轨道上的移动。我们描述了一种基于运动学模拟的递归思维理论,并且我们已经在一个体现车厢和轨道心理模型的计算机程序中实现了该理论。实验1测试了孩子们根据包含单个动作循环的算法描述推断火车车厢重新排列的能力。实验2评估了孩子们自发创建类似算法的能力。结果表明,没有接受过计算机编程训练的五年级学生具有系统的能力来推断和创建非正式递归算法。

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