Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta‐Analysis Approach. (8th September 2019)

• Record Type: Journal Article
• Authors: Trotter, Antony S.; Monaghan, Padraic; Beckers, Gabriël J. L.; Christiansen, Morten H.
• Is Part Of: Topics in cognitive science. Volume 12:Number 3(2020)
• Page Start: 875
• Page End: 893
• Publication Date: 2019-09-08
• Subjects: Artificial grammar learning -- Meta‐analysis -- Comparative studies -- Visual modality -- Auditory modality -- Adjacent dependencies -- Non‐adjacent dependencies; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12454
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: Artificial grammar learning (AGL) has become an important tool used to understand aspects of human language learning and whether the abilities underlying learning may be unique to humans or found in other species. Successful learning is typically assumed when human or animal participants are able to distinguish stimuli generated by the grammar from those that are not at a level better than chance. However, the question remains as to what subjects actually learn in these experiments. Previous studies of AGL have frequently introduced multiple potential contributors to performance in the training and testing stimuli, but meta‐analysis techniques now enable us to consider these multiple information sources for their contribution to learning—enabling intended and unintended structures to be assessed simultaneously. We present a blueprint for meta‐analysis approaches to appraise the effect of learning in human and other animal studies for a series of artificial grammar learning experiments, focusing on studies that examine auditory and visual modalities. We identify a series of variables that differ across these studies, focusing on both structural and surface properties of the grammar, and characteristics of training and test regimes, and provide a first step in assessing the relative contribution of these design features of artificial grammars as well as species‐specific effects for learning. Abstract : Studies of AGL have frequently used training and test stimuli that might provide multiple cues for learning, raising the question what subjects have actually learned. Using a selected subset of studies on humans and non‐human animals, Trotter et al. demonstrate how a meta‐analysis can be used to identify relevant experimental variables, providing a first step in asssessing the relative contribution of design features of grammars as well as of species‐specific effects on AGL.