Effects of healthy aging and left hemisphere stroke on statistical language learning.

Spoken sentences are continuous streams of sound, without reliable acoustic cues to word boundaries. We have previously proposed that language learners identify words via an implicit statistical learning mechanism that computes transitional probabilities between syllables. Neuroimaging studies in healthy young adults associate this learning with left inferior frontal gyrus, left arcuate fasciculus, and bilateral striatum. Here, we test the effects of healthy aging and left hemisphere (LH) injury on statistical learning. Following 10-minute exposure to an artificial language, participants rated familiarity of Words, Part-words (sequences spanning word boundaries), and Non-words (unfamiliar sequences). Young controls (N=14) showed robust learning, rating Words>Part-words>Non-words. Older controls (N=28) showed this pattern to a weaker degree. Stroke survivors (N=24) as a group showed no learning. A lesion comparison examining individual differences revealed that "non-learners" are more likely to have anterior lesions. Together, these findings demonstrate that word segmentation is sensitive to healthy aging and LH injury.

Learning a language from inconsistent input: Regularization in child and adult learners.

When linguistic input contains inconsistent use of grammatical forms, children produce these forms more consistently, a process called 'regularization.' Deaf children learning American Sign Language from parents who are non-native users of the language regularize their parents' inconsistent usages (Singleton & Newport, 2004). In studies of artificial languages containing inconsistently used morphemes (Hudson Kam & Newport, 2005, 2009), children, but not adults, regularized these forms. However, little is known about the precise circumstances in which such regularization occurs. In three experiments we investigate how the type of input variation and the age of learners affects regularization. Overall our results suggest that while adults tend to reproduce the inconsistencies found in their input, young children introduce regularity: they learn varying forms whose occurrence is conditioned and systematic, but they alter inconsistent variation to be more regular. Older children perform more like adults, suggesting that regularization changes with maturation and cognitive capacities.

The effect of Zipfian frequency variations on category formation in adult artificial language learning.

Successful language acquisition hinges on organizing individual words into grammatical categories and learning the relationships between them, but the method by which children accomplish this task has been debated in the literature. One proposal is that learners use the shared distributional contexts in which words appear as a cue to their underlying category structure. Indeed, recent research using artificial languages has demonstrated that learners can acquire grammatical categories from this type of distributional information. However, artificial languages are typically composed of a small number of equally frequent words, while words in natural languages vary widely in frequency, complicating the distributional information needed to determine categorization. In a series of three experiments we demonstrate that distributional learning is preserved in an artificial language composed of words that vary in frequency as they do in natural language, along a Zipfian distribution. Rather than depending on the absolute frequency of words and their contexts, the conditional probabilities that words will occur in certain contexts (given their base frequency) is a better basis for assigning words to categories; and this appears to be the type of statistic that human learners utilize.

Aging and the statistical learning of grammatical form classes.

Language learners must place unfamiliar words into categories, often with few explicit indicators about when and how that word can be used grammatically. Reeder, Newport, and Aslin (2013) showed that college students can learn grammatical form classes from an artificial language by relying solely on distributional information (i.e., contextual cues in the input). Here, 2 experiments revealed that healthy older adults also show such statistical learning, though they are poorer than young at distinguishing grammatical from ungrammatical strings. This finding expands knowledge of which aspects of learning vary with aging, with potential implications for second language learning in late adulthood. (PsycINFO Database Record