Variability is the spice of learning
Luca Onnis, Padraic Monaghan, Morten H. Christiansen, & Nick Chater
Abstract
An important aspect of language acquisition involves learning the syntactic nonadjacent dependencies
that hold between words in sentences, such as subject/verb agreement or tense marking in English.
Despite successes in statistical learning of adjacent dependencies, the evidence is not conclusive
for learning nonadjacent items. We provide evidence that discovering nonadjacent dependencies is possible
through statistical learning, provided it is modulated by the variability of the intervening material
between items. We show that generalization to novel syntactic-like categories embedded in nonadjacent
dependencies occurs with either zero or large variability. In addition, it can be supported even in more
complex learning tasks such as continuous speech, despite earlier failures.
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