Grammar is both categorical and gradient 1

In this paper, I discuss the results of word-likeness rating experiments with Hebrew and English speakers that show that language users use their grammar in a categorical and a gradient manner. In word-likeness rating tasks, subjects make the categorical distinction between grammatical and ungrammatical – they assign all grammatical forms equally high ratings and all ungrammatical forms equally low ratings. However, in comparative word-likeness tasks, subjects are forced to make distinctions between different grammatical or ungrammatical forms. In these experiments, they make finer gradient well-formedness distinctions. This poses a challenge on the one hand to standard derivational models of generative grammar, which can easily account for the categorical distinction between grammatical and ungrammatical, but have more difficulty with the gradient well-formedness distinctions. It also challenges models in which the categorical distinction between grammatical and ungrammatical does not exist, but in which an ungrammatical form is simply a form with very low probability. I show that the inherent comparative character of an OT grammar enables it to model both kinds of behaviors in a straightforward manner. Introduction There is a growing body of literature showing that phonological grammar influences phonological performance. We know that grammar plays a role in phoneme identification (Coetzee, 2005; Massaro and Cohen, 1983; Moreton, 2002), the segmentation of speech into words (Kirk, 2001; Suomi et al., 1997), lexical decision (Berent, Shimron and Vaknin, 2001), word-likeness ratings (Berent, Everett and Shimron, 2001; Frisch and Zawaydeh, 2001), etc. Once we accept that performance reflects the influence of grammar, we can use performance data as a window on what grammar looks like. In this paper, I discuss performance data showing that grammar is categorical and gradient. Grammar must be able to distinguish between grammatical (possible words) and ungrammatical (impossible words). However, grammar must also be able to make gradient well-formedness distinctions within these two sets. In the set of grammatical forms, there are some forms that are “more” and some that are “less” grammatical. Similarly, there are “more” and “less” ungrammatical forms. These data speak to the very core of grammar. They show that standard generative models in which grammar is simply a function that maps every input onto its unique grammatical output cannot be entirely correct. This would be equivalent to a grammar that makes only the categorical grammatical/ungrammatical distinction. On the other hand, it also shows that models in which grammaticality is only a value on a continuous scale of probability cannot be correct. We need a model of grammar that can make both the qualitative, categorical distinction between grammatical and ungrammatical, and gradient distinctions within the sets of grammatical and ungrammatical forms. I will show that the connections of Optimality Theory (OT) to standard generative grammar enable OT to draw the distinction between grammatical and ungrammatical in a