Long-distance expectation-based sequence learning

During language acquisition, infants are faced with the task of extracting complex regularities from linguistic input, with little explicit feedback or training. It is by now uncontroversial that adults, children, and infants (Saffran et al. 1996b,a) alike display sensitivity to statistical patterns in many types of sequential data, even with fairly brief exposure. At a higher level of abstraction, individuals of all ages are also sensitive to a variety of grammatical patterns in sequential data (Reber 1967, Marcus et al. 1999). Given this large body of research demonstrating the lifelong human capacity for implicit sequence learning, it is an important for cognitive science to establish exactly how individuals come to recognize and utilize sequential patterns. Several studies have examined the developmental trajectory of statistical learning abilities. Saffran (2002) tested adults and children for the influence of predictive dependencies between adjacent items in an artificial grammar, and found that while adults outperformed children overall, the two groups showed the same sensitivity to statistical relationships in the data. Gómez (2002) found similar correspondences between adults and 18 month old infants; both groups learned to recognize statistical dependencies between separated vocabulary items when the set of potential interveners was large, but not when it was small. Following up on this result, Gómez and Maye (2005) ran the same experiment on 12 and 15 month olds, demonstrating that even with a highly variable set of intervening elements, infants at 12 months failed to notice the dependencies between nonadjacent vocabulary items, but by 15 months, they performed like adults. This age range corresponds to the time in which children begin to show sensitivity to grammatical dependencies between separated morphemes (e.g. ‘is quickly running’ vs. ‘can quickly running’) (Santelmann and Jusczyk 1998). These studies show that even relatively challenging sequence learning abilities — like tracking statistics between nonadjacent items — are broadly similar between children and adults, and demonstrate the utility of investigating how statistical learning abilities emerge and correlate with natural language acquisition. However, artificial language studies have largely prescinded from questions concerning the learning mechanism itself, focusing instead on the outcomes of learning tasks. One counterexample to this comes from (Romberg and Saffran 2012), who presented infants with sequences of spatially-arranged targets, and measured their eye movements in anticipation of upcoming stimuli. For half of the infants, all of the first eight targets were presented in a single location, e.g. in the same place on a screen to the left of the infant. For the other half, six of the first eight targets appeared in that location on the left, but the second and sixth targets appeared on a screen to the right. Then for infants in both groups, the ninth target was presented in that secondary location. For the first group, this was the first appearance of an element on the right screen; for the second, it was the third appearance of an element on the right screen. They found that this low-probability event had a larger influence on subsequent expectations for the infants with variable early exposure than it did for the infants with uniform exposure. That is, infants in the uniform condition continued to look to the left screen in anticipation of the tenth trial, as they had before the anomalous event on the right, but infants in the probabilistic condition were more likely to look to the unlikely right screen on the tenth trial than they had been before. The authors concluded