The HED Task Catalog is under development. IDs are not stable until formal release. Comments are welcome at github.com/hed-standard/hed-task/issues.

Artificial Grammar Learning Task

HED task ID: hedtsk_artificial_grammar_learning

Family: Conditioning, reinforcement and implicit learning tasks

Also known as: AGL, Artificial Grammar, Artificial Grammar Learning

Exposure to letter strings generated by a finite-state grammar followed by a grammaticality judgment on novel strings; indexes implicit rule learning.

Description

Participants are first exposed to letter strings (e.g., “MXRVXT,” “VXTTRM”) generated by a finite-state grammar, often under the cover story that they are memorizing the strings. In a subsequent test phase, they classify novel letter strings as grammatical or non-grammatical, performing above chance despite being unable to articulate the underlying rules. Reber (1967) introduced this paradigm as the first laboratory demonstration of implicit learning — the acquisition of complex, rule-governed knowledge without conscious awareness. The paradigm has been central to debates about the nature of implicit vs. explicit knowledge, the role of consciousness in learning, and the neural systems supporting rule abstraction.

Inclusion test

An experiment is an instance of this task when its procedure matches, it manipulates at least one of the listed variables, and it records at least one of the listed measures.

Procedure

Participants study letter strings generated by a finite-state grammar during a training phase, then classify novel strings as grammatical or not.

Manipulation

Grammar complexity; training duration; surface features (same vs. changed letter set at test); chunk strength.

Measurement

Endorsement rate (proportion judged grammatical); d-prime separating grammatical from ungrammatical; RT.

Variations

Named versions that change what the participant experiences or does. The identifier of a variation is hedvar_<task>__<variation>.

Variation

Description

Justification

Standard AGL (Reber)

hedvar_artificial_grammar_learning__standard_agl_reber

Exposure to grammatical strings followed by grammaticality judgment of novel strings.

Canonical implicit grammar learning: exposure to strings, then grammaticality judgment

Transfer Version

hedvar_artificial_grammar_learning__transfer_version

Training on strings from one letter set, testing on strings from a different letter set that preserves the abstract grammar; tests rule abstraction vs. surface-feature learning.

Novel surface elements at test; isolates abstract rule knowledge from surface memorization

Chunk Strength Control

hedvar_artificial_grammar_learning__chunk_strength_control

Equating the frequency of letter bigrams and trigrams between grammatical and non-grammatical test items to rule out fragment-based classification.

Stimuli equated for associative chunk strength; controls for alternative explanation

Production Task

hedvar_artificial_grammar_learning__production

Participants generate strings they believe are grammatical rather than classifying; tests the nature of acquired knowledge.

Participant generates grammatical strings rather than judging them; different output requirement

Sequential AGL

hedvar_artificial_grammar_learning__sequential_agl

Presenting strings one letter at a time to study online prediction and temporal learning.

Motor/spatial sequential learning of grammar; different modality and response type

Hierarchical AGL

hedvar_artificial_grammar_learning__hierarchical_agl

Grammars with center-embedded or hierarchical structure; tests whether implicit learning extends to recursive rules.

Nested recursive grammar; structurally distinct grammar type requiring different parsing

Cross-Modal AGL

hedvar_artificial_grammar_learning__cross_modal_agl

Auditory or tactile sequences governed by the same grammar; tests modality-independence of implicit learning.

Stimulus modality change (visual→auditory or vice versa) at test; cross-modal transfer paradigm

Cognitive processes

This task is designed to engage the following processes:

Key references

  • Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6(6), 855–863. (DOI)

  • Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118(3), 219–235. (DOI)

  • Knowlton, B. J., & Squire, L. R. (1996). Artificial grammar learning depends on implicit acquisition of both abstract and exemplar-specific information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(1), 169–181. (DOI, PubMed)

Further references

  • Pothos, E. M. (2007). Theories of artificial grammar learning. Psychological Bulletin, 133(2), 227–244. (DOI, PubMed)

  • Rohrmeier, M. A., & Cross, I. (2014). Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning. Consciousness and Cognition, 27, 155–167. (DOI, PubMed)

  • Batterink, L. J., Reber, P. J., Neville, H. J., & Paller, K. A. (2015). Implicit and explicit contributions to statistical learning. Journal of Memory and Language, 83, 62–78. (DOI, PubMed)