Individual differences in non-adjacent statistical dependency learning in infants.

There is considerable controversy over the factors that shape infants' developing knowledge of grammar. Work with artificial languages suggests that infants' ability to track statistical regularities within the speech they hear could, in principle, support grammatical development. However, little work has tested whether infants' performance on laboratory tasks reflects factors that are relevant in real-world language learning. Here we tested whether the language that infants hear at home, and their receptive language skills, predict their performance on tasks assessing the ability to learn non-adjacent statistical dependencies (NADs) at 15 months, and whether that in turn predicts sensitivity to native-language NADs at 18 months. We found evidence for some (though not all) of these relations, and primarily for females. The results suggest that performance on the artificial language-learning task reveals something about the mechanisms of grammatical development, and that females and males may be learning NADs differently.

Do Infants Learn Words From Statistics? Evidence From English-Learning Infants Hearing Italian.

Infants are sensitive to statistical regularities (i.e., transitional probabilities, or TPs) relevant to segmenting words in fluent speech. However, there is debate about whether tracking TPs results in representations of possible words. Infants show preferential learning of sequences with high TPs (HTPs) as object labels relative to those with low TPs (LTPs). Such findings could mean that only the HTP sequences have a word-like status, and they are more readily mapped to a referent for that reason. But these findings could also suggest that HTP sequences are easier to encode, just like any other predictable sequence. Here we aimed to distinguish between these explanations. To do so, we built on findings that infants become resistant to learning labels that are not typical of their native language as they approach 2 years of age and add words to their lexicons. If tracking TPs in speech results in identifying candidate words, at this age TPs may have reduced power to confer lexical status when they yield a unit that is very dissimilar to word forms that are typical of infants' native language. Indeed, we found that at 20 months, English-learning infants with relatively small vocabularies learned HTP Italian words (but not LTP words) as object labels, while infants with larger vocabularies resisted learning HTP Italian words. These findings suggest that the HTP sequences may be represented as candidate words, and more broadly, that TP statistics are relevant to word learning.

Infant statistical-learning ability is related to real-time language processing.

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests that one way statistical learning ability affects language proficiency is by facilitating real-time language processing. Here we tested whether 15-month-olds' ability to learn sequential statistical structure in artificial language materials is related to their ability to encode and interpret native-language speech. Specifically, we tested their ability to learn sequential structure among syllables (Experiment 1) and words (Experiment 2), as well as their ability to encode familiar English words in sentences. The results suggest that infants' ability to learn sequential structure among syllables is related to their lexical-processing efficiency, providing continuity with findings from children and adults, though effects were modest.

Pro-eating disorder communities on social networking sites: a content analysis.

The purpose of this study was to assess the number of pro-ana groups on social networking sites and to analyze their content. A general inductive approach was used to analyze the content. Two main themes emerged from the content analysis: social support and eating disorder specific content. Themes were similar across all groups; however, a linguistic analysis indicated differences between groups on the two different networking sites. There was an absence of content typically found on Internet sites. Pro-ana groups on social networking sites are focused on social interactions, and lack eating disorder specific content found on Internet sites.