The current research aimed to investigate the role that prior knowledge played in what structures could be implicitly learnt and also the nature of the memory buffer required for learning such structures. It is already established that people can implicitly learn to detect an inversion symmetry (i.e. a cross-serial dependency) based on linguistic tone types. The present study investigated the ability of the Simple Recurrent Network (SRN) to explain implicit learning of such recursive structures. We found that the SRN learnt the symmetry over tone types more effectively when given prior knowledge of the tone types (i.e. of the two categories tones were grouped into). The role of prior knowledge of the tone types in learning the inversion symmetry was tested on people: When an arbitrary classification of tones was used (i.e. in the absence of prior knowledge of categories), participants did not implicitly learn the inversion symmetry (unlike when they did have prior knowledge of the tone types). These results indicate the importance of prior knowledge in implicit learning of symmetrical structures. We further contrasted the learning of inversion symmetry and retrograde symmetry and showed that inversion was learnt more easily than retrograde by the SRN, matching our previous findings with people, thus showing that the type of memory buffer used in the SRN is suitable for modeling the implicit learning of symmetry in people.