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ESTABLISHING EVIDENCE OF LEARNING IN EXPERIMENTS EMPLOYING ARTIFICIAL LINGUISTIC SYSTEMS

Published online by Cambridge University Press:  09 February 2017

Phillip Hamrick  and
Rebecca Sachs
Phillip Hamrick*
Affiliation:
Kent State University
Rebecca Sachs
Affiliation:
Virginia International University
*
*Correspondence concerning this article should be addressed to Phillip Hamrick, Language and Cognition Research Laboratory, Department of English, Kent State University, 475 Janik Drive, Kent, Ohio 44242. E-mail: phamric1@kent.edu
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Abstract

Artificial linguistic systems (ALSs) offer many potential benefits for second language acquisition (SLA) research. Nonetheless, their use in experiments with posttest-only designs can give rise to internal validity problems depending on the baseline that is employed to establish evidence of learning. Researchers in this area often compare experimental groups’ performance against (a) statistical chance, (b) untrained control groups’ performance, and/or (c) trained control groups’ performance. However, each of these methods can involve unwarranted tacit assumptions, limitations, and challenges from a variety of sources (e.g., preexisting perceptual biases, participants’ fabrication of rules, knowledge gained during the test), any of which might produce systematic response patterns that overlap with the linguistic target even in the absence of learning during training. After illustrating these challenges, we offer some brief recommendations regarding how triangulation and more sophisticated statistical approaches may help researchers to draw more appropriate conclusions going forward.

Information

Type
Research Article
Information
Studies in Second Language Acquisition , Volume 40 , Issue 1 , March 2018 , pp. 153 - 169
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

We would like to thank Ronald Leow, Alison Mackey, Nick B. Pandža, Kelli Ryan, and the reviewers for their helpful comments on earlier versions of this paper. We are also grateful to Patrick Rebuschat for his valuable guidance on our early forays into implicit learning research, and for collaborations with him that have positively influenced our work in numerous productive ways. All remaining errors are our own. Both authors contributed equally to this paper.

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