Morphological convergence as on-line lexical analogy

The English past-tense contains pockets of variation, where regular and irregular forms compete (e.g. learned/learnt, or weaved/wove). Individuals vary considerably in the degree to which they prefer irregular forms. This paper examines the degree to which individuals may converge on their regularization patterns and preferences. We report on a novel experimental methodology, using a cooperative game involving nonce verbs. Analysis of participants' post-game responses indicates that their behavior has shifted in response to an automated co-player's preferences, on two dimensions. First, players regularize more after playing with peers with high regularization rates, and less after playing with peers with low regularization rates. Second, players' overall pattern of regularization is also affected by the particular distribution of (ir)regular forms produced by the peer. We model the effects of the exposure on participants' morphological preferences, using both a rule-based model and an instance-based analogical model (Albright & Hayes, 2003; Nosofsky, 1988). Both models contribute separately and significantlyto explaining participants' pre-exposure regularization processes. However, only the instance-based model captures the shift in preferences that arises after exposure to the peer. We argue that the results suggest an account of morphological convergence in which new word forms are stored in memory, and online generalizations are formed over these instances