This paper proposes that the set of frequencies that the human language processor keeps track of are those that are useful to it in learning. In a computational experimental setting, we investigate four liguistically motivated features which distinguish subclasses of intransitive verbs, and suggest that those features that are the most useful to automatically classify verbs into lexical semantic classes are related to mechanisms used in adult processing to resolve structural ambiguity.

Models of the human sentence processing mechanism have traditionally appealed to innate architectural restrictions to explain observed patterns of behavior. Recently, a number of proposcds have instead emphasized the role of linguistic experience in guiding sentence interpretation, suggesting that various frequency measures play a crucial role in ambiguity resolution. What has been lacking thus far is a detailed analysis of the linguistic and computational properties that could explain why those particular aspects of experience are effective in shaping behavior. In this paper, we present a linguistic analysis that reveals restrictions on the representational ability of the sentence processor, explaining its sensitivity to particular factors in the linguistic environment. The proposal receives strong support from a large-scale corpus analysis.