Feature-based generalisation as a source of gradient acceptability

Phonological judgements are often gradient: blickZ?bwickZ*bnickZ**bzick. The mechanisms behind gradient generalisation remain controversial, however. This paper tests the role of phonological features in helping speakers evaluate which novel combinations receive greater lexical support. A model is proposed in which the acceptability of a string is based on the most probable combination of natural classes that it instantiates. The model is tested on its ability to predict acceptability ratings of nonce words, and its predictions are compared against those of models that lack features or economise on feature specifications. The proposed model achieves the best balance of performance on attested and unattested sequences, and is a significant predictor of acceptability even after the other models are factored out. The feature-based model’s predictions do not completely subsume those of simpler models, however. This may indicate multiple levels of evaluation, involving segment-based phonotactic probability and feature-based gradient phonological grammaticality.