Distributional Language Learning: Mechanisms and Models of ategory Formation

Abstract

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

Figure 1

The distribution of nonsense words in Experiment 1 from Reeder et al. (2013). The three words that preceded and followed the three X-words provide robust evidence for categories A, X, and B.

Figure 2

The distribution of nonsense words in Experiment 3 from Reeder et al. (2013). Only two words preceded and followed the three X-words, providing systematic gaps in the evidence for categories A, X, and B.

Figure 3

The distribution of nonsense words in the subcategorization experiment from Reeder et al. (2009). Separate sets of three words preceded and followed two subsets of X-words, providing robust evidence for two A, X, B sub-categories.

Figure 4

Model predictions from Qian et al. (in preparation) when a bias parameter is allowed to vary from a coarse to a fine granularity in how gaps in the input are fit to the underlying category structure.