Multiple regression analyses in artificial-grammar learning: The importance of control groups

Abstract

In artificial-grammar learning, it is crucial to ensure that above-chance performance in the test stage is due to learning in the training stage but not due to judgemental biases. Here we argue that multiple regression analysis can be successfully combined with the use of control groups to assess whether participants were able to transfer knowledge acquired during training when making judgements about test stimuli. We compared the regression weights of judgements in a transfer condition (training and test strings were constructed by the same grammar but with different letters) with those in a control condition. Predictors were identical in both conditions—judgements of control participants were treated as if they were based on knowledge gained in a standard training stage. The results of this experiment as well as reanalyses of a former study support the usefulness of our approach.

Keywords:

• Artificial-grammar learning
• Multiple regression analyses
• Transfer
• Control groups

Notes

¹ Note that we use the term “transfer” to describe test performance solely with strings instantiated in a different vocabulary. Elsewhere, the same term is used for performance on any kind of new stimuli (e.g., Vokey & Brooks, 1992).

² The local repetition structure of an item can be identical to or different from its global repetition structure. For example, the training strings KZQ and XZH have the same local repetition structure as the test string XHX: Each letter is different from its predecessor. However, the global repetition structure of XHX differs from that of both training strings, as in the test string there is a repetition of one letter within the string in contrast to the training strings KZQ and XZH.