Emmanuel M. Pothos & Todd M. Bailey
Abstract
Generalisation to novel stimuli can be seen as being driven
by either a similarity-to-old-exemplars mechanism (e.g., Nosofsky,
1989) or a mechanism involving some abstract, rule-based
representation of the instances encountered so far (see Chater & Hahn,
1997, for a review). Artificial Grammar Learning (AGL) has provided a
suitable experimental framework to address this issue (Reber, 1989,
Brooks & Vokey, 1991). In this work, we have tried to investigate the
extent to which Nosofsky's Generalised Context Model (GCM; Nosofsky,
1989) of categorisation can account for AGL competence results,
improving on previous research whereby the measures of similarity
employed to model AGL performance have been criticised as being
insensitive (Knowlton & Squire, 1996; Redington, 1997, 1996). Our
results provide support for the hypothesis that both adherence to the
rule structure of the grammar used to construct grammatical items, and
similarity of test items to training items independently motivated
subjects' grammaticality endorsements in test.