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 context-sensitive,” we specifically included a visual grammar at this complexity level. Acquisition of these three grammars was tested in an artificial grammar-learning paradigm: after exposure to a set of well-formed strings, participants were asked to discriminate novel grammatical patterns from non-grammatical patterns. Participants successfully acquired all three grammars after only minutes of exposure, correctly generalizing to novel stimuli and to novel stimulus lengths. A Bayesian analysis excluded multiple alternative hypotheses and shows that the success in rule acquisition applies both at the group level and for most participants analyzed individually. These experimental results demonstrate rapid pattern learning for abstract visual patterns, extending to the mildly context-sensitive level characterizing language. We suggest that a formal equivalence of processing at the mildly context sensitive level in the visual and linguistic domains implies that cognitive mechanisms with the computational power to process linguistic syntax are not specific to the domain of language, but extend to abstract visual patterns with no meaning.

Baboons (Papio papio) Process a Context-Free but Not a Context-Sensitive Grammar

“…Firstly, humans reacted to violations of both the mirror and repeat grammars, contrarily to baboons. These data, showing that humans computational abilities were not restricted to the context-free grammar in the current task, are consistent with previous reports from the literature [31][32][33][34] . Secondly, humans' RTs remained flat across targets.…”

“…non-sense syllables 31,32 ) or non-linguistic stimuli (e.g. visual shapes 33,34 ). Overall these data indicate that processing context-free and non-context-free (i.e.…”

Recursion in programs, thought, and language

“…The various proposals about grammars (e.g., Stabler, 2004) and experiments on the learning of artificial grammars (Westphal-Fitch et al, 2018) have converged on similar results. Natural language has mildly context-sensitive rules of the sort in various systems, including tree-adjoining grammars (Joshi et al, 1975) and combinatory categorial grammars (Steedman, 2019).…”

Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar

“…In addition, Stobbe et al (2012) used a forcedchoice paradigm, meaning that the fact that participants chose the correct sequence does not necessarily mean that they believed this sequence to be ungrammatical; they may have simply believed it to be less bad than the other choice. Westphal-Fitch, Giustolisi, Cecchetto, Martin, and Fitch (2018) For Westphal-Fitch et al (2018), ungrammatical sequences were generated by removing one element from a grammatical sequence. Thus, the task could be solved purely by counting the number of A's and B's, without having learned any center-embedded structure, meaning that the length generalization that they observed may have been purely about counting rather than about extrapolating center embedding.…”