Article

The use of control groups in artificial grammar learning

Department of Psychology, University of Bern, Bern, Switzerland.
The Quarterly Journal of Experimental Psychology A 02/2003; 56(1):97-115. DOI: 10.1080/02724980244000297
Source: PubMed

ABSTRACT Experimenters assume that participants of an experimental group have learned an artificial grammar if they classify test items with significantly higher accuracy than does a control group without training. The validity of such a comparison, however, depends on an additivity assumption: Learning is superimposed on the action of non-specific variables-for example, repetitions of letters, which modulate the performance of the experimental group and the control group to the same extent. In two experiments we were able to show that this additivity assumption does not hold. Grammaticality classifications in control groups without training (Experiments 1 and 2) depended on non-specific features. There were no such biases in the experimental groups. Control groups with training on randomized strings (Experiment 2) showed fewer biases than did control groups without training. Furthermore, we reanalysed published research and demonstrated that earlier experiments using control groups without training had produced similar biases in control group performances, bolstering the finding that using control groups without training is methodologically unsound.

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Available from: Rolf Reber, Aug 02, 2015
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    • "A potential hypothesis may be that an online-learning effect depends on the proportion of grammatical chunks contained in the ungrammatical structures. A detailed exploration for, and explanation of, this effect, as well as the questions raised in the discussion following Redington and Chater (1996), Reber and Perruchet (2003) and Dienes and Altmann (2003) requires further experimental work. "
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    • "Moreover, the fact that effects of grammaticality as well as associative chunk strength can develop already during classification prior to acquisition suggest that the mechanism engaged (not necessarily the same as in artificial grammar learning proper) can work on surprisingly scarce input. For example, Reber and Perruchet (2003) list a number of features (e.g., number of letters in strings, multiple letter position, letter repetitions, and bigram reoccurrence) which they suggested untrained subjects might employ during baseline or the initial classification phase. However, in this context in the current study, it is important to observe that the participants classified at random at the very beginning of the baseline classification and that the instruction effects developed subsequently over the baseline session (see Fig. 3a). "
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