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, structural priming 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.
History 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.
This research was replicated and expanded upon by many others. The conclusions of most of these studies were congruent with Reber's hypothesis: the implicit learning process was done with no intentional learning strategies. These studies also identified common characteristics for the implicitly acquired knowledge:
Abstract representation for the set of rules. Unconscious strategies that can be tested with performance.
The modern paradigm
The modern AGL paradigm can be used to investigate explicit and implicit learning, although it is most often used to test implicit learning. In a typical AGL experiment, participants are required to memorize strings of letters previously generated by a specific grammar. The length of the strings usually ranges from 2-9 letters per string. An example of such a grammar is shown in figure 1.
Figure 1: Example of an artificial grammar rule Ruleful strings: VXVS, TPTXVS Unruleful strings: VXXXS, TPTPS
In order to compose a grammatically "ruleful" string of letters, according to the predetermined grammar rule, a subject must follow the rules for the pairing of letters as represented in the model (figure 1). When observing a violation of the grammatical rule system that composes the string, it is considered an "unruleful" or randomly constructed string.
In the case of a standard AGL implicit learning task, subjects are not told that the strings are based on a specific grammar. Instead, they are simply given the task to memorize the letter strings for a memory. After the learning phase, subjects are told that the letter strings presented during the learning phase were based on specific rules, but are not explicitly told what the rules are. During a test phase, the subjects are instructed to categorize new letter strings as "ruleful" or "unruleful". The dependent variable usually measured is the percentage of correctly categorized strings. Implicit learning is considered to be successful when the percentage of correctly sorted strings is significantly higher than chance level. If this significant difference is found, it indicates the existence of a learning process that is more involved than memorizing the presented letter strings.
Bayesian learning The mechanism behind the implicit learning that is hypothesized to occur while people engage in artificial grammar learning is statistical learning or, more specifically, Bayesian learning. Bayesian learning takes into account types of biases or "prior probability distributions" individuals have that contribute to the outcome of implicit learning tasks. These biases can be thought of as a probability distribution that contains the probability that each possible hypothesis is likely to be correct. Due to the structure of the Bayesian model, the inferences output by the model are in the form of a probability distribution rather than a single most probable event. This output distribution is a "posterior probability distribution". The posterior probability of each hypothesis in the original distribution is the probability of the hypothesis being true given the data and the probability of data given the hypothesis is true. This Bayesian model for learning is fundamental for understanding the pattern detection process involved in implicit learning and, therefore, the mechanisms that underlie the acquisition of artificial grammar learning rules. It is hypothesized that the implicit learning of grammar involves predicting co-occurrences of certain words in a certain order. For example, "the dog chased the ball" is a sentence that can be learned as grammatically correct on an implicit level due to the high co-occurrence of "chase" being one of the words to follow "dog". A sentence like "the dog cat the ball" is implicitly recognized as grammatically incorrect due to the lack of utterances that contain those words paired in that specific order. This process is important for teasing apart thematic roles and parts of speech in grammatical processing (see grammar). While the labeling of the thematic roles and parts of speech is explicit, the identification of words and parts of speech is implicit.
Explanatory models
Traditional approaches to AGL claim that the stored knowledge obtained during the learning phase is abstract. Other approaches argue that this stored knowledge is concrete and consists of exemplars of strings encountered during the learning phase or "chunks" of these exemplars. In any case, it is assumed that the information stored in memory is retrieved in the test phase and is used to aid decisions about letter strings. Three main approaches attempt to explain the AGL phenomena:
Abstract Approach: According to this traditional approach, participants acquire an abstract representation of the artificial grammar rule in the learning stage. That abstract structure helps them to decide if the new string presented during the test phase is grammatical or randomly constructed. Concrete knowledge approach: This approach proposes that during the learning stage participants learn specific examples of strings and store them in their memory. During the testing stage, participants do not sort the new strings according to an abstract rule; instead they will sort them according to their similarity to the examples stored in memory from the learning stage. There are multiple opinions concerning how concrete the learned knowledge really is. Brooks & Vokey argue that all of the knowledge stored in memory is represented as concrete examples of the full examples studied during the learning stage. The strings are sorted during the testing stage according to a full representation of the string examples from the learning stage. On the other hand, Perruchet & Pacteau claimed that the knowledge of the strings from the learning stage is stored in the form of "memory chunks" where 2 - 3 letters are learned as a sequence along with knowledge about their permitted location in the full string. Dual Factor approach: Dual process learning model, combines the approaches described above. This approach proposes that a person will rely on concrete knowledge when they can. When they cannot rely on concrete knowledge (for example on a transfer of learning task), the person will use abstract knowledge of the rules.
Research with amnesia patients suggests the "Dual Factor approach" may be the most accurate model. A series of experiments with amnesiac patients support the idea that AGL involves both abstract concepts and concrete exemplars. Amnesiacs were able to classify stimuli as "grammatical" vs. "randomly constructed" just as well as participants in the control group. While able to successfully complete the task, amnesiacs were not able to explicitly recall grammatical "chunks" of the letter sequence while the control group was able to explicitly recall them. When performing the task with the same grammar rules but a different sequence of letters than those that they were previously tested on, both amnesiacs and the control group were able to complete the task (although performance was better when the task was completed using the same set of letters used for training). The results of the experiment support the dual factor approach to artificial grammar learning in that people use abstract information to learn rules for grammars and use concrete, exemplar-specific memory for chunks. Since the amnesiacs were unable to store specific "chunks" in memory, they completed the task using an abstract set of rules. The control group was able to store these specific chunks in memory and (as evidenced by recall) did store these examples in memory for later reference.
Automaticity debate