Study on modeling implicit learning based on MAM framework

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.