Analyses of adult semantic networks suggest a learning mechanism involving preferential attachment: A word is more likely to enter the lexicon the more connected the known words to which it is related. We introduce and test two alternative growth principles: preferential ac- quisition—words enter the lexicon not because they are related to well-connected words, but because they connect well to other words in the learning environment—and the lure of the associates—new words are favored in propor- tion to their connections with known words. We tested these alternative principles using longitudinal analyses of de- veloping networks of 130 nouns children learn prior to the age of 30 months. We tested both networks with links be- tween words represented by features and networks with links represented by associations. The feature networks did not predict age of acquisition using any growth model. The associative networks grew by preferential acquisition, with the best model incorporating word frequency, number of phonological neighbors, and connectedness of the new word to words in the learning environment, as opera- tionalized by connectedness to words typically acquired by the age of 30 months.