Artificial Grammar Learning (AGL) Task

AKA: Grammar Learning Task.
Context:
- It can be solve by a AGL System that implements a AGL algorithm.
Example(s):
- GraSp,
- Wolff's SNPR,
- …
Counter-Example(s):
- Grammatical Error Correction Task,
- Spelling Correction Task.
See: Formal Language Theory, Cognitive Psychology, Linguistics, Implicit Learning, Language Learning Aptitude, Syntax, Cottontop Tamarin, Cognitive Psychology, Lexicon.

References

(Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Artificial_grammar_learning Retrieved:2021-7-4.
- 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.

(Trotter et al., 2020) ⇒ Antony S. Trotter, Padraic Monaghan, Gabriel J. L. Beckers, and Morten H. Christiansen (2019-2020). "Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta‐Analysis Approach". In: Topics in cognitive science, 12(3), 875-893.
- QUOTE: Artificial grammar learning (AGL) has become an important tool used to understand aspects of human language learning and whether the abilities underlying learning may be unique to humans or found in other species. Successful learning is typically assumed when human or animal participants are able to distinguish stimuli generated by the grammar from those that are not at a level better than chance. However, the question remains as to what subjects actually learn in these experiments. Previous studies of AGL have frequently introduced multiple potential contributors to performance in the training and testing stimuli, but meta-analysis techniques now enable us to consider these multiple information sources for their contribution to learning — enabling intended and unintended structures to be assessed simultaneously. We present a blueprint for meta-analysis approaches to appraise the effect of learning in human and other animal studies for a series of artificial grammar learning experiments, focusing on studies that examine auditory and visual modalities (...)

(Fitch & Friederici, 2012) ⇒ W. Tecumseh Fitch, and Angela D. Friederici (2012). "Artificial Grammar Learning Meets Formal Language Theory: An Overview". In: Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1598), 1933-1955.

(Pothos, 2007) ⇒ Emmanuel M. Pothos (2007). "Theories of Artificial Grammar Learning". In: Psychological bulletin, 133(2), 227.

(Wolff, 1982) ⇒ J. Gerard Wolff (1982). "Language acquisition, data compression and generalization". In: Elsevier- Language & Communication, 2(1), 57-89.