Artificial grammar learning

Artificial grammar learning (AGL) is a paradigm of study within cognitive psychology and linguistics. Its goal is to investigate the processes that underlie human language learning by testing subjects' ability to learn a made-up grammar in a laboratory setting. It was developed to evaluate the processes of human language learning but has also been utilized to study implicit learning in a more general sense. The area of interest is typically the subjects' ability to detect patterns and statistical regularities during a training phase and then use their new knowledge of those patterns in a testing phase. The testing phase can either use the symbols or sounds used in the training phase or transfer the patterns to another set of symbols or sounds as surface structure.

Many researchers propose that the rules of the artificial grammar are learned on an implicit level since the rules of the grammar are never explicitly presented to the participants. The paradigm has also recently been utilized for other areas of research such as language learning aptitude and to investigate which brain structures are involved in syntax acquisition and implicit learning.

Apart from humans, the paradigm has also been used to investigate pattern learning in other species, e.g. cottontop tamarins and starlings.

More than half a century ago George A. Miller established the paradigm of artificial grammar learning in order to investigate the influence of explicit grammar structures on human learning, he designed a grammar model of letters with different sequences. His research demonstrated that it was easier to remember a structured grammar sequence than a random sequence of letters. His explanation was that learners could identify the common characteristics between learned sequences and accordingly encode them to a memory set. He predicted that subjects could identify which letters will most likely appear together as a sequence repeatedly and which letters would not and that the subjects would use this information to form memory sets. Those memory sets served participants as a strategy later on during their memory tests.

Reber doubted Miller's explanation. He claimed that if participants could encode the grammar rules as productive memory sets, then they should be able to verbalize their strategy in detail. He conducted research that led to the development of the modern AGL paradigm. This research used a synthetic grammar learning model to test implicit learning. AGL became the most used and tested model in the field. As in the original paradigm developed by Miller, participants were asked to memorize a list of letter strings which were created from an artificial grammar rule model. It was only during the test phase that participants were told that there was a set of rules behind the letter sequences they memorized. They were then instructed to categorize new letter strings based on the same set of rules which they had not previously been exposed to. They classified new letter strings as "grammatical" (constructed from the grammar rule), vs. "randomly constructed" sequences. If subjects correctly sorted the new strings above chance level, it could be inferred that subjects had acquired the grammatical rule structure without any explicit instruction of the rules. Reber found that participants sorted out new strings above chance level. While they reported using strategies during the sorting task, they could not actually verbalize those strategies. Subjects could identify which strings were grammatically correct but could not identify the rules that composed grammatical strings.

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