Previous studies demonstrated that statistical properties of adult generated free associates predict the order of early noun learning. We investigate an explanation for this phenomenon that we call the associative structure of language: early word learning may be driven in part by contextual diversity in the learning environment, with contextual diversity in caregiver speech correlating with the cue-target structure in adult free association norms. To test this, we examined the co-occurrence of words in caregiver speech from the CHILDES database and found that a word’s contextual diversity—the number of unique word types a word co-occurs with in caregiver speech—predicted the order of early word learning and was highly correlated with the number of unique associative cues for a given target word in adult free association norms. The associative structure of language was further supported by an analysis of the longitudinal development of early semantic networks (from 16 to 30 months) using contextual co-occurrence. This analysis supported two growth processes: The lure of the associates, in which the earliest learned words have more connections with known words, and preferential acquisition, in which the earliest learned words are the most contextually diverse in the learning environment. We further discuss the impact of word class (nouns, verbs, etc.) on these results.

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 acquisition—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 proportion to their connections with known words. We tested these alternative principles using longitudinal analyses of developing networks of 130 nouns children learn prior to the age of 30 months. We tested both networks with links between 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 operationalized by connectedness to words typically acquired by the age of 30 months.

The shared-features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the overlap of words normatively acquired by children prior to 2 ½ years of age and perceptual and conceptual (functional) features acquired from adult feature generation norms. The resulting networks have small-world structure, indicative of a high degree of feature overlap in local clusters. However, perceptual features—due to their abundance and redundancy—generate networks more robust to feature omissions, while conceptual features are more discriminating and, per feature, offer more categorical information than perceptual features. Using a network specific cluster identification algorithm (the clique percolation method) we also show that shared features among these early learned nouns create higher-order groupings common to adult taxonomic designations. Again, perceptual and conceptual features play distinct roles among different categories, typically with perceptual features being more inclusive and conceptual features being more exclusive of category memberships. The results offer new and testable hypotheses about the role of shared features in human category knowledge.

This article reports the structure of associations among 101 common verbs and body parts. The verbs are those typically learned by children learning English prior to 3 years of age. In a free association task, 50 adults were asked to provide the single body part that came to mind when they thought of each verb. Analyses reveal highly systematic and structured patterns of associations that are also related to the normative age of acquisition of the verbs showing a progression from verbs associated with actions by the mouth, to verbs strongly associated with actions by hand and arm, to verbs not so strongly associated with any one body part. The results have implications for proposals about embodied verb meaning and also for processes of early verb learning.

This study investigated neural activation patterns during verb processing in children, using fMRI (functional Magnetic Resonance Imaging). Preschool children (aged 4-6) passively listened to lists of verbs and adjectives while neural activation was measured. Findings indicated that verbs were processed differently than adjectives, as the verbs recruited motor systems in the frontal cortex during auditory perception, but the adjectives did not. Further evidence suggested that different types of verbs activated different regions in the motor cortex. The results demonstrate that the motor system is recruited during verb perception in the developing brain, reflecting the embodied nature of language learning and processing.