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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Exploring variation between artificial grammar learning experiments
T2 - Outlining a meta-analysis approach
AU - Trotter, Tony
AU - Monaghan, Padraic
AU - Beckers, Gabriel
AU - Christiansen, Morten H.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
KW - Artificial grammar learing
KW - Meta‐analysis
KW - Comparative studies
KW - Visual modality
KW - Auditory modality
KW - Adjacent dependencies
KW - Non‐adjacent dependencies
U2 - 10.1111/tops.12454
DO - 10.1111/tops.12454
M3 - Journal article
VL - 12
SP - 875
EP - 893
JO - Topics in Cognitive Science
JF - Topics in Cognitive Science
SN - 1756-8757
IS - 3
ER -