New Thoughts On Language Acquisition: Toddlers As Data Miners
Indiana University researchers are studying a ground-breaking theory that young children are able to learn large groups of words rapidly by data-mining.
Their theory, which they have explored with 12- and 14-month-olds, takes a radically different approach to the accepted view that young children learn words one at a time — something they do remarkably well by the age of 2 but not so well before that.
Data mining, usually computer-assisted, involves analyzing and sorting through massive amounts of raw data to find relationships, correlations and ultimately useful information. It often is used and thought of in a business context or used by financial analysts, and more recently, a wide range of research fields, such as biology and chemistry. IU cognitive science experts Linda Smith and Chen Yu are investigating whether the human brain accumulates large amounts of data minute by minute, day by day, and handles this data processing automatically. They are studying whether this phenomenon contributes to a “system” approach to language learning that helps explain the ease by which 2- and 3-year-olds can learn one word at a time.
“This new discovery changes completely how we understand children’s word learning,” Smith said. “It’s very exciting.”
Smith, chair of the Department of Psychological and Brain Sciences at IU Bloomington, and Yu, assistant professor in the department, recently received a $1 million grant from the National Institutes of Health to fund this research for five years. Here are some recent findings:
*In one of their studies, published in the journal Cognition, Yu and Smith attempted to teach 28 12- to 14-month-olds six words by showing them two objects at a time on a computer monitor while two pre-recorded words were read to them. No information was given regarding which word went with which image. After viewing various combinations of words and images, however, the children were surprisingly successful at figuring out which word went with which picture.
*In the adult version of the study, which used the same eye-tracking technology used in the Cognition study, adults were taught 18 words in just six minutes. Instead of viewing two images at a time, they simultaneously were shown anywhere from three to four, while hearing the same number of words. The adults, like the children, learned significantly more than would be expected by chance. Many of the adult subjects indicated they were certain they had learned nothing and were “amazed” by their success. Yu and Smith wrote in the journal Psychological Science, “This suggests that cross-situational learning may go forward non-strategically and automatically, steadily building a reliable lexicon.”
Yu and Smith say it’s possible that the more words tots hear, and the more information available for any individual word, the better their brains can begin simultaneously ruling out and putting together word-object pairings, thus learning what’s what.
Yu, who has a doctorate in computer science and writes much of the software programming for their studies, said that if they can identify key factors involved in this form of learning and how it can be manipulated, they might be able to make learning languages easier, through training DVDs and other means, for children and adults. The learning mechanisms used by the children to learn words also could be used to further machine learning.
Cognition, Volume 106, Issue 3, March 2008, Pages 1558-1568
Infants rapidly learn word-referent mappings via cross-situational statistics
Linda Smith and Chen Yu
First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children’s rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker’s intended referent at that moment. The present study shows that 12- and 14-month-old infants can resolve the uncertainty problem in another way, not by unambiguously deciding the referent in a single word-scene pairing, but by rapidly evaluating the statistical evidence across many individually ambiguous words and scenes.
Psychological Science May 2007 (Volume 18, Issue 5 Page 369-468)
Rapid Word Learning Under Uncertainty via Cross-Situational Statistics
There are an infinite number of possible word-to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and attentional constraints at a single moment. This article discusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, most important, across the co-occurrences of words and referents at multiple moments. We briefly exposed adults to a set of trials that each contained multiple spoken words and multiple pictures of individual objects; no information about word-picture correspondences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning conditions varied the degree of within-trial reference uncertainty, the number of trials, and the length of trials. Overall, the remarkable performance of learners in various learning conditions suggests that they calculate cross-trial statistics with sufficient fidelity and by doing so rapidly learn word-referent pairs even in highly ambiguous learning contexts.