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.
Highlights
People learn language from their social environment
Previous research shows that reading an article that does not agree with the anticipated following noun leads to an N400 effect whose amplitude is larger the more anticipated the mismatching noun is (e.g., Delong et al, 2005)
Mem Cogn (2017) 45:528–538 influenced by the same properties of our social network; they shed some light on the relation between the representations used for prediction of others’ speech and the representations used for own speech production
Summary
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 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. 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. The goal of this article is to test whether properties of our social network influence our knowledge of word distributions and, our lexical choices and ability to predict others’ lexical choice. Sumner (2011) showed that exposure to multiple tokens from the same speaker leads to better learning than exposure to the same token of the speaker the same number of times
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