In foundational works of generative phonology it is claimed that subjects can
reliably discriminate between possible but non-occurring words and words that
could not be English. In this paper we examine the use of a probabilistic
phonological parser for words to model experimentally-obtained judgements of
the acceptability of a set of nonsense words. We compared various methods of
scoring the goodness of the parse as a predictor of acceptability. We found
that the probability of the worst part is not the best score of acceptability,
indicating that classical generative phonology and Optimality Theory miss an
important fact, as these approaches do not recognise a mechanism by which the
frequency of well-formed parts may ameliorate the unacceptability of
low-frequency parts. We argue that probabilistic generative grammars are
demonstrably a more psychologically realistic model of phonological competence
than standard generative phonology or Optimality Theory.Comment: compressed postscript, 8 pages, 1 figur
