Variability is the spice of learning, and a crucial ingredient for detecting and generalizing in nonadjacent dependencies

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.