Search

Investigating how infants first establish relationships between words is a necessary step towards understanding how an interconnected network of semantic relationships develops in the adult lexical-semantic system. Stimuli selection for these child studies is critical since words must be both familiar and highly imageable. However, there has been a reliance on adult word association norms to inform stimuli selection in English infant studies to date, as no resource currently exists for child-specific word associations. We present three experiments that explore the strength of word–word relationships in 3-year-olds. Experiment 1 collected children's word associations (WA) (N = 150; female = 84, L1 = British English) and compared them to adult associative norms (Moss & Older, 1996; Nelson et al., 2004 (Behavior Research Methods, Instruments, & Computers, 36(3), 402–407)). Experiment 2 replicated WAs from Experiment 1 in an online adaptation of the task (N = 24: 13 female, L1 = British English). Both experiments indicated a high proportion of child-specific WAs not represented in adult norms (Moss & Older, 1996; Nelson et al., 2004 (Behavior Research Methods, Instruments, & Computers, 36(3), 402–407)). Experiment 3 tested noun–noun WAs from these responses in an online semantic priming study (N = 40: 19 female, L1 = British English) and found that association type modulated priming (F(2.57, 100.1) = 13.13, p <. 0001, generalized η2 =.19). This research presents a resource of child-specific imageable noun–noun word pair stimuli suitable for testing young children in word recognition and semantic priming studies. [ABSTRACT FROM AUTHOR]

How words are associated within the linguistic environment conveys semantic content; however, different contexts induce different linguistic patterns. For instance, it is well known that adults speak differently to children than to other adults. We present results from a new word association study in which adult participants were instructed to produce either unconstrained or child-oriented responses to each cue, where cues included 672 nouns, verbs, adjectives, and other word forms from the McArthur–Bates Communicative Development Inventory (CDI; Fenson et al., 2006). Child-oriented responses consisted of higher frequency words with fewer letters, earlier ages of acquisition, and higher contextual diversity. Furthermore, the correlations among the responses generated for each pair of cues differed between unconstrained (adult-oriented) and child-oriented responses, suggesting that child-oriented associations imply different semantic structure. A comparison of growth models guided by a semantic network structure revealed that child-oriented associations are more predictive of early lexical growth. Additionally, relative to a growth model based on a corpus of naturalistic child-directed speech, the child-oriented associations explain added unique variance to lexical growth. Thus, these new child-oriented word association norms provide novel insight into the semantic context of young children and early lexical development. [ABSTRACT FROM AUTHOR]

Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms. [ABSTRACT FROM AUTHOR]

Word ratings on affective dimensions are an important tool in psycholinguistic research. Traditionally, they are obtained by asking participants to rate words on each dimension, a time-consuming procedure. As such, there has been some interest in computationally generating norms, by extrapolating words' affective ratings using their semantic similarity to words for which these values are already known. So far, most attempts have derived similarity from word co-occurrence in text corpora. In the current paper, we obtain similarity from word association data. We use these similarity ratings to predict the valence, arousal, and dominance of 14,000 Dutch words with the help of two extrapolation methods: Orientation towards Paradigm Words and k-Nearest Neighbors. The resulting estimates show very high correlations with human ratings when using Orientation towards Paradigm Words, and even higher correlations when using k-Nearest Neighbors. We discuss possible theoretical accounts of our results and compare our findings with previous attempts at computationally generating affective norms. [ABSTRACT FROM AUTHOR]

In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing. [ABSTRACT FROM AUTHOR]