New Models for Old Questions: Evolutionary Robotics and the 'A Not B' Error
New Models for Old Questions: Evolutionary Robotics and the 'A Not B' Error
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老问题的新模型：进化机器人和“A Not B”错误
DOI：
10.1007/978-3-540-74913-4_114
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
E. Paolo
中科院分区：
文献类型：
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作者：
Rachel Wood;E. Paolo
在心理学上，“A而不是B”的错误，即婴儿在玩具被移动到一个新的位置后，仍然坚持到达先前隐藏的位置，自皮亚杰（Piaget）首次发现这一错误以来，已经进行了五十年的研究。本文描述了“a而不是B”错误范式的一种新实现，该范式用于测试最小系统进化机器人建模可用于探索发展过程并为自然实验群体中的测试生成新的假设的概念。该模型表明，由可塑性连续时间递归神经网络控制的主体可以执行“A而不是B”任务，并且可塑性的稳态调解可以产生类似于人类婴儿观察到的持续性错误模式。此外，模型在开发过程中出现的持续性误差也呈现出不断减少的发展趋势。
In psychology the ‘A not B’ error, whereby infants perseverate in reaching to the location where a toy was previously hidden after it has been moved to a new location, has been the subject of fifty years research since it was first identified by Piaget [1]. This paper describes a novel implementation of the ‘A not B’ error paradigm which is used to test the notion that minimal systems evolutionary robotics modelling can be used to explore developmental process and to generate new hypotheses for test in natural experimental populations. The model demonstrates that agents controlled by plastic continuous time recurrent neural networks can perform the ‘A not B’ task and that homeostatic mediation of plasticity can produce perseverative error patterns similar to those observed in human infants. In addition, the model shows a developmental trend for the production of perseverative errors to reduce during development.
迈向斯宾诺莎机器人学：探索行为偏好的最小动态
DOI：
--
发表时间：
2007
期刊：
Adaptive Behavior Vol15 No4
影响因子：
--
作者：
Nose;Masahiko;Iggesen;Oliver;野瀬昌彦;Ezequiel Di Paolo;Hiroyuki Iizuka
通讯作者：
Hiroyuki Iizuka