Probabilistic Feature Attention as an Alternative to Variables in Phonotactic Learning

Since Halle 1962, explicit algebraic variables (often called alpha notation) have been commonplace in phonological theory. However, Hayes and Wilson (2008) proposed a variable-free model of phonotactic learning, sparking a debate about whether such algebraic representations are necessary to capture human phonological acquisition. While past experimental work has found evidence that suggested a need for variables in models of phonology (Berent et al. 2012, Moreton 2012, Gallagher 2013), this article presents a novel mechanism, Probabilistic Feature Attention, that allows a variable-free model of phonotactics to predict a number of these phenomena. This approach also captures experimental results involving phonological generalization that cannot be explained by variables. These results cast doubt on whether variables are necessary to capture human-like phonotactic learning and provide a useful alternative to such representations.