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首页> 外文会议>Joint IEEE International Conference on Development and Learning and Epigenetic Robotics >A Deep Learning Neural Network for Number Cognition: A bi-cultural study with the iCub
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A Deep Learning Neural Network for Number Cognition: A bi-cultural study with the iCub

机译：数字认知的深度学习神经网络：与ICUB的双重文化研究

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摘要

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.
机译：新颖的深度学习范式提供了一种高度生物学的可符合性的方式来培训具有许多层的神经网络架构，受到人类大脑的分层组织的启发。事实上，深度学习为研究人类认知行为进行了新的维度，为认知机器人提供了新的应用程序。在本文中，我们通过手指计数和数量单词提出了一种用于数量认知的新型神经网络架构。该架构由5层组成，并以一种方式设计，使其通过将来自ICUB人形机器机机器人平台的感觉输入（电动机和听觉）与来自ICUB人形机器机机器人平台的感觉输入（电机和听觉）相关联来设计。架构性能在两个发育实验中验证和测试。在第一次实验中，将标准的反向化与深度学习方法进行比较，其中重量和偏置是通过贪婪算法预先训练的权重和偏置，然后用BackPropagation精炼。在第二个实验中，比较六个双文化数字学习条件，以探索不同语言的影响（数量单词）和手指计数策略。发展实验证实了模型的有效性以及深入学习方法给出的效率的提高。介绍和讨论了双文化研究的结果，并讨论了神经心理文学，并简要概述了学习情况的结果。

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