Simultaneously emerging Braitenberg codes and compositionality
Simultaneously emerging Braitenberg codes and compositionality
复制标题
同时出现的布雷滕伯格代码和组合性
DOI：
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发表时间：
2011
影响因子：
1.6
通讯作者：
Martin Volker Butz
中科院分区：
文献类型：
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作者：
Y. Sugita;J. Tani;Martin Volker Butz
尽管许多研究人员认为，构成概念应该以感觉运动为基础，但实现这一目标的方法尚不清楚。本文介绍了一种基于感觉运动时间序列数据的含参数偏差的二阶神经网络（sNNPB）。数据是由一个模拟机器人产生的，该机器人执行不同的对象交互（移动和朝向）。我们表明，各种sNNPB设置可以学习组合模仿对象相互作用，而不是专门训练的相互作用，这是以前的神经网络（NN）架构（包括循环神经网络（rnn））所不可能的。我们还表明，这些模仿能力是通过在PB值中发展自组织的、几何排列的组合概念结构和在隐藏的感觉层中发展面向任务的、类似布里滕伯格的感觉编码来实现的。由于二级连接是完成任务所必需的，我们假设这种连接可能是驱动以感觉运动为基础的组成结构和以布里滕伯格为导向的行为导向的“亲呈现”的学习所必需的。“从认知的角度来看，我们展示了如何处理感觉运动时间序列的相互作用，以产生发展语义组成结构所需的信号。
Although many researchers have suggested that compositional concepts should be sensorimotor grounded, the method to accomplish this remains unclear. This article introduces a second-order neural network with parametric biases (sNNPB) that learns compositional structures based on sensorimotor time series data. The data was produced by a simulated robot that executed distinct object interactions (move-to and orient-toward). We show that various sNNPB setups can learn to compositionally imitate object interactions beyond the interactions that were specifically trained, which was not possible with previous neural network (NN) architectures, including recurrent neural networks (RNNs). We also show that these imitation capabilities are accomplished by developing a self-organized, geometrically arranged compositional concept structure in the PB values and task-oriented, Braitenberg-like sensory encodings in hidden sensory layers. Because second-order connections were necessary to accomplish the task, we hypothesize that such connections may be essential to drive the learning of both sensorimotor-grounded compositional structures and Braitenberg-like, behavior-oriented ‘‘pro-presentations.’’ From a cognitive perspective, we show how sensorimotor time series of interactions may be processed to generate the signals necessary to develop semantically compositional structures.
婴儿模仿和记忆：九个月大的婴儿进行即时和延迟测试。
影响因子：
4.6
作者：
Meltzoff,AN
通讯作者：
Meltzoff,AN