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ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments

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Abstract

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA (https://enigma-lang.org) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners’ performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331–361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

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Data Availability

Since the experiment data will be reported and made publicly available in a separate study, they will be shared by the authors upon reasonable requests for the time being.

Notes

  1. , Hulstijn (2015) warned that we should not assume that all L1 patterns must be acquired subconsciously. The acquisition of morphosyntactic aspects, for instance, could be delayed substantially until early childhood, when explicit instruction may begin to become influential in learning the L1 knowledge.

  2. Artificial grammar learning differs from artificial language learning in that the learning input is semantic-free in the former (e.g., meaningless letter strings or sound sequences) but not in the latter (e.g., sound-picture pairs). Readers are referred to Morgan-Short (2020) for subtle distinctions between different artificial linguistic systems. For simplicity in this article, we use AGL as an inclusive term for experiments that expose learners to an artificial linguistic system and test the learnability of hidden structural regularities.

  3. See Hamrick and Sachs (2018) and Reber and Perruchet (2003) for reviews of other common within-group and between-group designs in AGL studies and their pros and cons.

  4. We searched for entries with ‘artificial grammar’ or ‘artificial language’ and ‘experiment’ and without ‘review,’ ‘simulation,’ and ‘reply’ in anywhere except full text to help us narrow down to experimental AGL studies. We still had to manually exclude studies from the search output that do not involve AGL experiments with the typical design reviewed in this section (7 from the 1972–2000 collection and 30 from the 2000–2022 collection; see Appendix C for the excluded studies). The search may have missed some experimental AGL studies without using ‘artificial grammar’ or ‘artificial language’ as a keyword (e.g., Carpenter, 2010; Chan & Leung, 2018; Cristiá et al., 2013; Koo & Callahan, 2012) or because the studies had yet to be indexed in the database at the time (e.g., Beguš, 2022; Chen, 2022). However, we believe the quantitative difference will hold even with a more precise search in an updated database.

  5. It is important to note that this issue may not be specific to AGL studies, as Winter (2019, p. 174) suspected that “most studies in linguistics are underpowered.”

  6. For a guide to power analysis, see Cohen (1988, 1992). Brysbaert and Stevens (2018), Green and MacLeod (2016), and Kumle et al. (2021) are useful tutorials for power analysis in mixed-effects regression modeling.

  7. Martin and Peperkamp (2020) is another rare example that was not included in our database search output. In addition, several AGL studies in our collection were designed to separate training and test phases with an irrelevant task to distract learners from training input. The test phases were thus ‘delayed’ in a way. However, we do not consider these studies to be examples of investigating the lasting effect of learning since the comparison of learning performance across multiple test phases could not be made.

  8. Other Web applications that are both an online experiment toolkit and a crowdsourcing service include Gorilla (Anwyl-Irvine et al., 2021), Pavlovia.org (Open Science Tools, 2022), and PsyToolKit (Stoet, 2017).

  9. For other non-linguistic studies showing the validity of data collected via MTurk, see Buhrmester et al. (2011), Crump et al. (2013), and Paolacci et al. (2010).

  10. https://www.mturk.com/help#can_requesters_outside_us_use_mturk (Retrieved on Nov 20, 2023).

  11. ENIGMA is built using Meteor v2.15 (https://meteor.com), a free and open-source package for developing JavaScript-based Web, mobile, and desktop applications. Researchers who hope to understand the underlying structure of ENIGMA or modify the application for their use could download the source codes of ENIGMA at https://github.com/nthuTYChen/ENIGMA and compile the codes with Meteor.

  12. The stats are based on the result of the Pingdom website speed test (https://tools.pingdom.com/) on Apr 17, 2024.

  13. We understand that the term ‘gender’ is open to different interpretations. Thus, we included an option ‘non-binary’ for sexual orientations that cannot be easily labeled with the traditional male-female dichotomy. In addition, we also include the ‘do not disclose’ option for those who consider this information sensitive and prefer not to share it in ENIGMA. We thank an anonymous reviewer for bringing this issue to our attention.

  14. It is important to note that in Daneman and Merikle’s (1996) meta-analysis, reading and listening WM was a stronger predictor of language comprehension than O-Span WM. However, whether this difference holds for learners’ performance in AGL experiments awaits further investigation. Since operation-span WM is correlated with verbal WM (e.g., Daneman & Tardif, 1987; Kyllonen, 1993; Turner & Engle, 1989), we assume that effects of WM in AGL studies, if any, could still be revealed with O-Span WM measures.

  15. We thank an anonymous reviewer who recommended including these different recall scoring systems in ENIGMA for research purposes.

  16. A complete list of the element settings can be found in our experimenter manual at https://lngproc.hss.nthu.edu.tw/enigmaFiles/exp_instruction_en-us.pdf.

  17. A positive correlation between confidence level and judgment accuracy could be the evidence of target knowledge awareness. Conversely, the lack of this correlation could be treated as the evidence of the unconscious retrieval and application of acquired knowledge.

  18. Participation in other experiments between two test phases of the same experiments is strictly prohibited in ENIGMA, and participants are not allowed to check the experiment list unless they complete or quit their ‘ongoing challenge.’

  19. The data of Chen (2020, 2022) were retrieved from https://osf.io/k36qx/ and https://osf.io/zt8jh/.

  20. The by-item random intercept and random slopes of the main effect were not included since they increased model complexity and led to convergence problems.

  21. The linear mixed-effects model that converged for this separate analysis is logRT ~ Confidence × Device + (1 + Confidence|Participant) + (1|Item).

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Acknowledgements

I am extremely grateful for the encouraging and helpful comments on the work from two anonymous reviewers and the assistance from Ssu-Han Chang, Wei-Chin Chang, Yi-Shan Lin, Wei-Hsin Lo, Tzu-Hsuan Tseng, Yan-Yun Tu, Hong-Yi Wang, and Bo-Ting Yang in the project. I also thank James Myers for his input at an early stage of the ENIGMA development.

Funding

The research was funded by the project “A Comprehensive Examination of Evidence and Methodology in Artificial Grammar Learning” of the Ministry of Science and Technology (now known as the National Science and Technology Council) in Taiwan (Project #108-2410-H-007-030-MY3).

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  1. Department of Foreign Languages and Literature, National Tsing Hua University, 101, Section 2, Guangfu Road, Hsinchu, 300044, Taiwan

    Tsung-Ying Chen

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  1. Tsung-Ying Chen
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Appendices

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Chen, TY. ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments. J Psycholinguist Res 53, 38 (2024). https://doi.org/10.1007/s10936-024-10078-5

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