Implicit Learning (IL) involves the fundamental problem of human potential development, and it has been a hot and difficult topic for many years. Traditional artificial neural networks can simulate IL, but there are some shortcomings. A few years ago, people used a morphological neural network (MNN) to simulate IL, but the support in theory and practice is weak. The contribution of this study is threefold. Firstly, based on the theory of unified framework of morphological associative memories (UFMAM), this paper makes a deep exploration for simulating IL by MNNs. Since both MNN and UFMAM are based on strict mathematical morphology, the research is established on a solid theoretical basis. Secondly, three experiments were designed, and the results were analyzed and discussed according to the theory of UFMAM. Thus, the depth and breadth of this research of IL were further expanded, new simulation methods and research examples were provided, and the MNN model of IL was established. Thirdly, it provides an example for the coordinated development of artificial neural networks, artificial intelligence, cognitive psychology, neural science and brain science. The research shows that the IL model based on MNN is superior to the traditional IL model in automation, comprehension, abstraction and anti-interference. Therefore, it will play an important role in the future study of IL and bring new inspiration to reveal the neural mechanism of IL. There is an inseparable relationship between MNN and IL, i.e. the former provides new research tools and means for the latter, while the latter provides psychological and neuroscientific supports for the former, which will make both of them have a more solid scientific foundation. It is reasonable to believe that computer simulation of IL and other cognitive phenomena will have an important impact on promoting the coordinated development of multidisciplinary.

内隐学习（IL）涉及人类潜能开发的根本问题，多年来一直是一个热门和难点的话题。传统的人工神经网络可以模拟IL，但存在一些不足。几年前，人们使用形态神经网络（MNN）来模拟IL，但理论和实践支持较弱。这项研究的贡献有三方面。首先，本文基于形态联想记忆统一框架（UFMAM）理论，对MNNs模拟IL进行了深入探索。由于 MNN 和 UFMAM 都基于严格的数学形态学，因此研究建立在坚实的理论基础之上。其次，设计了三个实验，并根据UFMAM理论对实验结果进行了分析和讨论。因此，进一步拓展了IL研究的深度和广度，提供了新的仿真方法和研究实例，建立了IL的MNN模型。第三，为人工神经网络、人工智能、认知心理学、神经科学和脑科学的协调发展提供了范例。研究表明，基于MNN的IL模型在自动化、理解、抽象和抗干扰方面优于传统的IL模型。因此，它将在未来IL的研究中发挥重要作用，为揭示IL的神经机制带来新的启示。MNN与IL有着密不可分的关系，即前者为后者提供了新的研究工具和手段，而后者为前者提供了心理和神经科学的支持，这将使他们俩都有更坚实的科学基础。有理由相信，计算机模拟IL等认知现象将对促进多学科协同发展产生重要影响。