unbroken letters. When a chunk precedes or follows a common chunk, then the model determines disjunctive classes in terms of the first set. For example, when the model encounters "the-dog-chased" and "the-cat-chased" it classifies "dog" and "cat" as being members of the same class since they both precede "chase". While the model sorts chunks into classes, it does explicitly define these groups (e.g., noun, verb). Early AI models of grammar learning such as these ignored the importance of negative instances of grammar's effect on grammar acquisition and were also lacking in the ability to connect grammatical rules to pragmatics and semantics. Newer models have attempted to factor these details in. The Unified Model attempts to take both of these factors into account. The model breaks down grammar according to "cues". Languages mark case roles using five possible cue types: word order, case marking, agreement, intonation and verb-based expectation (see grammar). The influence that each cue has over a language's grammar is determined by its "cue strength" and "cue validity". Both of these values are determined using the same formula, except that cue strength is defined through experimental results and cue validity is defined through corpus counts from language databases. The formula for cue strength/validity is as follows: Cue strength/cue validity = cue availability * cue reliability Cue availability is the proportion of times that the cue is available over the times that it is needed. Cue reliability is the proportion of times that the cue is correct over the total occurrences of the cue. By incorporating cue reliability along with cue availability, The Unified Model is able to account for the effects of negative instances of grammar since it takes accuracy and not just frequency into account. As a result, this also accounts for the semantic and pragmatic information since cues that do not produce grammar in the appropriate context will have low cue strength and cue validity. While MacWhinney's model also simulates natural grammar learning, it attempts to model the implicit learning processes observed in the AGL paradigm. Cognitive neuroscience and the AGL paradigm Contemporary studies with AGL have attempted to identify which structures are involved in the acquisition of grammar and implicit learning. Agrammatic aphasic patients (see Agrammatism) were tested with the AGL paradigm. The results show that breakdown of language in agrammatic aphasia is associated with an impairment in artificial grammar learning, indicating damage to domain-general neural