哪种神经网络架构与人工语法学习中的人类行为相匹配？
Which Neural Network Architecture matches Human Behavior in Artificial Grammar Learning?
摘要：
近年来，人工神经网络在几个领域取得了接近或优于人类的性能：以前属于人类特权的任务，如语言处理，在最先进的模型中有了显着的改进。这种技术推动的一个好处是便于比较不同的神经网络和人类的表现，以加深我们对人类认知的理解。在这里，我们研究哪种神经网络架构（前馈与递归）与人工语法学习中的人类行为相匹配，人工语法学习是语言习得的一个重要方面。先前的实验研究证明，人工语法可以在很少接触的情况下被人类主体学习，并且通常没有明确的基本规则知识。我们在人类以及前馈和递归网络中测试了四种具有不同复杂度的语法。我们的研究结果表明，这两种架构都可以在与人类相同数量的训练序列后“学习”（通过错误反向传播）语法，但递归网络的表现比前馈网络更接近人类，无论语法复杂度如何。此外，类似于视觉处理，其中前馈和递归架构与无意识和有意识的过程有关，我们的研究结果表明，外显学习最好由递归架构建模，而前馈网络更好地捕捉内隐学习中涉及的动态。
英文摘要：
In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feed-forward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can 'learn' (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, our results suggest that explicit learning is best modeled by recurrent architectures, whereas feedforward networks better capture the dynamics involved in implicit learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
