The Role of Simple Semantics in the Process of Artificial Grammar Learning
This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL experiment; in the without-semantics control condition, participants did not receive semantic information. It was hypothesized that semantics would generally facilitate grammar acquisition and that the learning benefit in the with-semantics conditions would increase with increasing grammar complexity. Experiment 1 showed learning effects for all grammars but no performance difference between conditions. Experiment 2 replicated the absence of a semantic benefit for all grammars even though semantic information was more prominent during grammar acquisition as compared to Experiment 1. Thus, we did not find evidence for the idea that semantics facilitates grammar acquisition, which seems to support the view of an independent syntactic processing component.
KeywordsGrammar acquisition Semantics–syntax interplay Chomsky Hierarchy
This data was part of the Ph.D. thesis of the first author. While conducting this research the first author received support from the German National Academic Foundation. In addition, this research was supported by the workshop Artificial Grammar Learning: Learnability, Complexity and Meaning funded by the Fritz Thyssen Foundation and the SFB 833, a grant from the German Research Foundation awarded to Barbara Kaup (SFB 833, Project B4) as well as the ERC Advanced Grant 324246 Language Evolution: The Empirical Turn to Gerhard Jäger.
Compliance with Ethical Standards
Conflict of interest
The authors declare no conflict of interest.
In all experiments of the reported study participants signed an informed consent form prior to participating in the experiment. They were informed that they were free to terminate the experiment at any time without facing disadvantages prior to the experiment.
- de Vries, M. H., Christiansen, M. H., & Petersson, K. M. (2011). Learning recursion: Multiple nested and crossed dependencies. Biolinguistics, 5(1–2), 010–035.Google Scholar
- Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I., & Anwander, A. (2006). The brain differentiates human and non-human grammars: Functional localization and structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 103(7), 2458–2463.CrossRefPubMedPubMedCentralGoogle Scholar
- Joshi, A. K., Shanker, K. V., & Weir, D. (1991). The convergence of mildly context-sensitive grammar formalisms. In P. Sekks, S. Shieber & T. Wasow (Eds.), Processing of Linguistic Strucutre. Cambridge (MA): MIT Press.Google Scholar
- Moeser, S. D. (1977). Semantics and miniature artificial languages. In J. Macnamara (Ed.), Language learning and thought (pp. 227–250). New York: Academic Press.Google Scholar
- Schlesinger, I. (1977). Miniature artificial languages as research tools. In J. Macnamara (Ed.), Language learning and thought (pp. 251–260). New York: Academic Press.Google Scholar
- Van den Bos, E., & Poletiek, F. (2015). Learning simple and complex artificial grammars in the presence of a semantic reference field: Effects on performance and awareness. Frontiers in Psychology, 6. doi: 10.3389/fpsyg.2015.00158.