A Deep Learning Neural Network for Number Cognition: A bi-cultural study with the iCub

A. D. Nuovo, V. Cruz, A. Cangelosi
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引用次数: 18

Abstract

The novel deep learning paradigm offers a highly biologically plausible way to train neural network architectures with many layers, inspired by the hierarchical organization of the human brain. Indeed, deep learning gives a new dimension to research modeling human cognitive behaviors, and provides new opportunities for applications in cognitive robotics. In this paper, we present a novel deep neural network architecture for number cognition by means of finger counting and number words. The architecture is composed of 5 layers and is designed in a way that allows it to learn numbers from one to ten by associating the sensory inputs (motor and auditory) coming from the iCub humanoid robotic platform. The architecture performance is validated and tested in two developmental experiments. In the first experiment, standard backpropagation is compared with a deep learning approach, in which weights and biases are pre-trained by means of a greedy algorithm and then refined with backpropagation. In the second experiment, six bi-cultural number learning conditions are compared to explore the impact of different languages (for number words) and finger counting strategies. The developmental experiments confirm the validity of the model and the increase in efficiency given by the deep learning approach. Results of the bi-cultural study are presented and discussed with respect to the neuro-psychological literature and implications of the results for learning situations are briefly outlined.
数字认知的深度学习神经网络:与iCub的双文化研究
新的深度学习范式提供了一种生物学上高度合理的方法来训练具有多层的神经网络架构,其灵感来自于人脑的分层组织。事实上,深度学习为研究人类认知行为建模提供了一个新的维度,并为认知机器人的应用提供了新的机会。在本文中,我们提出了一种新的深度神经网络结构,用于通过手指计数和数字单词来进行数字认知。该架构由5层组成,其设计方式允许它通过关联来自iCub人形机器人平台的感官输入(运动和听觉)来学习从1到10的数字。在两个开发实验中验证了该体系结构的性能。在第一个实验中,将标准反向传播与深度学习方法进行了比较,其中深度学习方法通过贪婪算法预训练权重和偏差,然后使用反向传播进行改进。在第二个实验中,比较了六种双文化数字学习条件,探讨了不同语言(对数字单词)和手指计数策略的影响。发展实验证实了模型的有效性和深度学习方法带来的效率提高。双文化研究的结果在神经心理学文献中被提出和讨论,并简要概述了结果对学习情境的影响。
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