Linking Learning to Looking: Habituation and Association in Infant Statistical Language Learning

Linking Learning to Looking: Habituation and Association in Infant Statistical Language Learning Daniel Yurovsky, Shohei Hidaka, Chen Yu, and Linda B. Smith {dyurovsk, shhidaka, chenyu, smith4} @indiana.edu Department of Psychological and Brain Science, and Cognitive Science Program 1101 East 10 th Street Bloomington, IN 47405 USA Abstract Recent experiments have shown the importance of statistical learning in infant language acquisition. Computational models of such learning, however, often take the form of corpus analyses and are thus difficult to connect to empirical data. We report a cross-situational learning experiment which demonstrates robust individual differences in learning between infants. We then present a novel generative model of cross-situational learning combining two competing processes – habituation and association. The model’s parameters are set to best reproduce each infant’s individual looking behavior from trial-to-trial in training and testing. We then isolate each infant’s word-referent learning function to explain the variance found in preferential looking tests. Keywords: statistical learning; computational modeling; cognitive development; language acquisition Introduction Language acquisition should be hard but young children nonetheless move from babbling to complex sentences in a remarkably short time. One might thus expect the underlying language learning mechanism to also be complex, involving constraints and biases (Markman, 1990) and sophisticated inferences (Xu & Tenenbaum, 2007). However, even if the final mechanism is complex, it must begin with something simple – language learners develop. By understanding the tools available to very young learners, we may develop insight into how more complex mechanisms are created and how they might be understood as products of simpler mechanisms. One candidate for a simple mechanism is the accumulation of associations between words and objects in a child’s ambient environment (Hollich, Hirsh-Pasek, & Golinkoff, 2000, Smith, 2000). If the co-occurrence structure of the world is informative, such that words frequently occur with the objects they label, a child who can attend to this information could find a wedge into learning the more complicated structural aspects of her language (Landau, Smith, & Jones, 1988). Recently, Smith and Yu (2008) have provided evidence of just such a sensitivity in 12 and 14-month-old infants. In the cross-situational learning paradigm, infants are exposed to a series of individually ambiguous learning trials containing multiple words and objects. While each trial contains several potential mappings, some of which are spurious, a child who can attend to the overall co-occurrence structure can unambiguously determine the correct mappings. Attempts to understand the mechanism underlying this competence, however, have been aimed primarily at the abstract computational level. Computational models have taken the form of corpus analyses (Fazly, Alishahi, & Stevenson, in press, Frank, Goodman, & Tenenbaum, 2009, Yu, 2008) and thus have resisted direct comparison to empirical data. To understand the mechanisms available to budding language learners, however, models must account for and explain the behavior of young infants. Because preferential looking is the primary measure of learning in studies of preverbal infants, it is this looking data that computational model must explain. Yu and Smith (in press) took a first step towards this goal. Using an associative model, they found that the number of words for which an individual infant showed preferential looking behavior was predictable from that infant’s own eye movement in training. This might seem to fit an associative learning mechanism: one learns to associate words to the objects at which one is looking when one hears the words. However, Yu and Smith were unable to predict which word- referent mappings were learned. If associative learning is the relevant mechanism, something is still missing. We propose to take two more steps towards understanding the mechanism supporting cross-situational learning. First, whereas Yu and Smith’s model was descriptive – using patterns in training behavior to predict test behavior – we present a generative model of eye movements. That is, we construct a model which produces eye-movement behavior matching that of infants during training, and then show that the same model accounts for the test data. Second, we predict not only how many word-referent mappings each infant learned, but also which ones. This modeling is done at the individual infant level, allowing us to explain behavior as it unfolds trial-by-trial throughout training and testing. To motivate our model, we first present results from a cross-situational learning experiment with 15-month-old infants. Analysis of preferential looking test results shows robust individual differences among infants, underscoring the importance of understanding cross-situational learning at a process level. We then construct a model that generates fixations through the competition of two well-known processes that organize infant behavior and learning – association (Smith, 2000) and habituation (Hunter & Ames, 1988. Model parameters are fit to best account for each individual infant’s looking behavior over the course of the experiment, and then inferences about learning are drawn from these parameter fits.