Artificial Grammar Learning Capabilities in an Abstract Visual Task Match Requirements for Linguistic Syntax.

ABSTRACT: Whether pattern-parsing mechanisms are specific to language or apply across multiple cognitive domains remains unresolved. Formal language theory provides a mathematical framework for classifying pattern-generating rule sets (or "grammars") according to complexity. This framework applies to patterns at any level of complexity, stretching from simple sequences, to highly complex tree-like or net-like structures, to any Turing-computable set of strings. Here, we explored human pattern-processing capabilities in the visual domain by generating abstract visual sequences made up of abstract tiles differing in form and color. We constructed different sets of sequences, using artificial "grammars" (rule sets) at three key complexity levels. Because human linguistic syntax is classed as "mildly co

SUBMITTER: Westphal-Fitch G 

PROVIDER: S-EPMC6066649 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature