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Published online: 28 November 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract
People learn language from their social environment. As individuals differ in their social networks, they might be exposed to input with different lexical distributions, and these might influence their linguistic representations and lexical choices. In this article we test the relation between linguistic performance and 3 social network properties that should influence input variability, namely, network size, network heterogeneity, and network density. In particular, we examine how these social network properties influence lexical prediction, lexical access, and lexical use. To do so, in Study 1, participants predicted how people of different ages would name pictures, and in Study 2 participants named the pictures themselves. In both studies, we examined how participants' social network properties related to their performance. In Study 3, we ran simulations on norms we collected to see how age variability in one's network influences the distribution of different names in the input. In all studies, network age heterogeneity influenced performance leading to better prediction, faster response times for difficult-to-name items, and less entropy in input distribution. These results suggest that individual differences in social network properties can influence linguistic behavior. Specifically, they show that having a more heterogeneous network is associated with better performance. These results also show that the same factors influence lexical prediction and lexical production, suggesting the two might be related.
Keywords Social networks . Variability . Lexical access . Prediction . Language production
Languages allow the same events to be described in myriad ways. Some manners of description are more common than others. For example, the same piece of furniture can be referred to as either a dresser or a chest of drawers, but the former is more common in American English (Yoon et al., 2004). Occasionally, a term's frequency depends on the characteristics of the speaker. That is, in some cases, lexical choice among parallel terms does not reflect shades of meaning of the referent, but it does, on the other hand, reflect a characteristic of the speaker. For example, bicycle and bike can refer to the same object, but the former is more likely to be used by older adults than by younger ones (Yoon et...