title: Probabilistically-Cued Patterns Trump Perfect Cues in Statistical Language Learning.
abstract: Probabilistically-cued co-occurrence relationships between word categories are common in natural languages but difficult to acquire. For example, in English, determiner-noun and auxiliary-verb dependencies both involve co-occurrence relationships, but determiner-noun relationships are more reliably marked by correlated distributional and phonological cues, and appear to be learned more readily. We tested whether experience with co-occurrence relationships that are more reliable promotes learning those that are less reliable using an artificial language paradigm. Prior experience with deterministically-cued contingencies did not promote learning of less reliably-cued structure, nor did prior experience with relationships instantiated in the same vocabulary. In contrast, prior experience with probabilistically-cued co-occurrence relationships instantiated in different vocabulary did enhance learning. Thus, experience with co-occurrence relationships sharing underlying structure but not vocabulary may be an important factor in learning grammatical patterns. Furthermore, experience with probabilistically-cued co-occurrence relationships, despite their difficultly for naïve learners, lays an important foundation for learning novel probabilistic structure.
authors.affiliation: University of Notre Dame.
authors.forename: Jill
authors.initials: J
authors.lastname: Lany
authors.affiliation: The University of Arizona.
authors.forename: Rebecca L
authors.initials: RL
authors.lastname: Gómez