As we hear those patterns repeatedly, they become strengthened in our mind thus being able to learn the language. The model is exemplar-based pointing out that it’s the examples in what we hear that form the backbone of patterns that we extract. Learning is an instance of study and not induction of rules. Connectionism includes different network constructions which include parallel distributed processing. Parallel distributed processing network includes nodes with pathways that strengthen with use. The more we use a particular pathway, the more it becomes strong and vice-versa. Larsen Freeman suggests that the rate at which something happens is a major factor in learning a new language a claim supported by N.Ellis and Schmidt (Gass & Selinker, 2008, p. 220).
Also, the easy with which one learns the second language depends on how strongly the network of the native language is established. In an experiment performed by Sokolik and Smith (1992) on learning of noun gender in French, it was concluded that the easy which one could learn depended on age with young people learning more easily. From this, it’s important to note that the network of the native is not as strong in younger people which enhances learning of the L2.
Similarly, Stephen Krashen developed five hypotheses that helped to understand the learning of the second language: The Acquisition- learning hypothesis, Monitor hypothesis, Natural Order hypothesis, the Input hypothesis and the Affective Filter hypothesis. In the Acquisition-learning hypothesis, he suggests two ways of developing one’s language; by acquisition which entails taking in of knowledge subconsciously and storing it in the brain and by learning a language which takes the form of instructing the language in a formal setting as in a classroom. According to this assessment, the best way to learn a language is through natural communication.
In addition, monitor hypothesis seeks to correct errors in utterances. Krashen