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Probing the Limits of Human Formal Grammar Learning with Artificial Grammar Learning and Anodal tDCS
Poster Session C, Thursday, October 1, 10:45 am - 12:45 pm, Wangari Maathai
This poster is part of the Sandbox Series.
Teruo KINJO1, Fuka HIBINO1, Shinri OHTA1; 1Kyushu University, Fukuoka, Japan
Computational linguistics has proposed that human language can be adequately characterized by mildly context-sensitive (MCS) grammars. However, it remains unclear whether MCS grammars define the upper bound of the formal languages that the human brain can learn, or whether the human brain is capable of learning languages that exceed the generative capacity of MCS grammars. To address this question, we combine an artificial grammar learning (AGL) paradigm with anodal transcranial direct current stimulation (tDCS) over the left inferior frontal gyrus (LIFG), a core region involved in syntactic processing. Specifically, we examine whether human linguistic ability is constrained by MCS grammars or extends beyond them. We hypothesize that, if the human language faculty can acquire a formal language beyond the recognition capacity of MCS grammars, enhancing LIFG activity with anodal tDCS will improve AGL performance relative to sham stimulation. To test whether anodal tDCS over the LIFG facilitates AGL, we use two types of artificial grammars: a MIX language, consisting of strings with equal numbers of a, b, and c in arbitrary order, such as abbcac and bacccbbaa, whose dependencies exceed the generative capacity of MCS grammars; and a mirror-image language, such as aa, abba, and abccba, which is analogous to center-embedding in natural language and serves as a control condition. To control for placebo effects, we employ a single-blind, sham-controlled design. Anodal tDCS is delivered over the LIFG using the MxN-5 tES system (Soterix Medical; electrodes: FC3, C5, FT7, F5, and FC5; 20 min; ±2 mA), while EEG is recorded during the task using 64 active electrodes (Neurofax EEG-1200, Nihon Kohden). Following up to five learning sessions, each lasting one hour, participants (N = 30) judge whether visually presented sequences conform to the artificial grammar rules. Behavioral performance under anodal tDCS and sham stimulation is compared in terms of accuracy and reaction time. In addition, event-related potentials (ERPs) during the grammaticality judgment task are analyzed to assess whether participants distinguish grammatical from ungrammatical sequences. We expect that anodal tDCS will lead to higher accuracy and shorter reaction times in grammaticality judgments compared with sham stimulation. If participants successfully learn the MIX language, this would suggest that the human brain is capable of learning formal languages that exceed the generative capacity of MCS grammars. Furthermore, ungrammatical sequences are expected to elicit enhanced left-anterior negativity and P600 effects relative to grammatical sequences. By combining artificial grammar learning, non-invasive brain stimulation, and EEG, this study aims to clarify the relationship between human linguistic ability and formal language theory, thereby contributing to a better understanding of the neural basis of language.
Topic Areas: Syntax and Combinatorial Semantics,