F Related work

In this section, I clarify relationships between this project and other word recognition research reported from our lab. In short, our lab has reported results about the two-image and four-image experiments from cross-sectional samples, describing child-level measures that predict performance in these tasks. In contrast, my dissertation 1) focuses on the longitudinal development of word recognition and 2) engages with the fine-grained details of lexical processing.

Law et al. (2016) analyzed data from the four-image experiment in Study 1. This study featured a diverse cross-sectional sample of 60 children, half of whom received the experiment in African American English and half received it in Mainstream American English. The sample ranged in age from 28 to 60 months. The study included data from 23 participants from year 1 of the longitudinal study (i.e., what I refer to as age 3) in order to enrich parts of the sample demographics. For this manuscript, we analyzed how vocabulary and maternal education predicted looking patterns, including relative looks to the semantic and phonological foils, but with conventional polynomial growth curves. The use of generalized additive models is an innovation I developed for my dissertation.

Law and Edwards (2015) analyzed a different version of the mispronunciation experiment on a different sample of children (n = 34, 30–46 months old). This earlier version included both real word and the mispronunciation of the real word in the same block of trial. For example, a child would hear “dog” and “tog” during the same session of the experiment. This design might subtly temper the effect of mispronounced stimuli by allowing the listener to compare the mispronunciation to its correctly produced counterpart. The version of the experiment in Study 2 separates the real words and mispronunciations so that a child never hears a familiar word and its mispronunciation during the same block of trials. With this design, there is no explicit point of comparison for the mispronunciation, and the child has to rely on his or her own lexical representations when processing these words.

Mahr and Edwards (2018) was the manuscript I originally authored for my preliminary examinations. The paper analyzes the same kinds of relations as Weisleder and Fernald (2013) which showed that lexical processing efficiency mediated the effect of language input on future vocabulary size. Specifically, I asked whether word recognition performance on the four-image task of Study 1, vocabulary size, and home language input data from age 3 predicted vocabulary size at age 4. The paper only examined looks to the familiar image from one year of the study, so it did not analyze any lexical competition effects or the development of word recognition within children. In short, we found that receptive vocabulary was more sensitive to variability in lexical processing and home language input than expressive vocabulary.

References

Law, F., II, Mahr, T., Schneeberg, A., & Edwards, J. R. (2016). Vocabulary size and auditory word recognition in preschool children. Applied Psycholinguistics. doi:10.1017/S0142716416000126

Law, F., II, & Edwards, J. R. (2015). Effects of vocabulary size on online lexical processing by preschoolers. Language Learning and Development, 11(4), 331–355. doi:10.1080/15475441.2014.961066

Mahr, T., & Edwards, J. R. (2018). Using language input and lexical processing to predict vocabulary size. Developmental Science, e12685. doi:10.1111/desc.12685

Weisleder, A., & Fernald, A. (2013). Talking to children matters: Early language experience strengthens processing and builds vocabulary. Psychological Science, 24(11), 2143–52. doi:10.1177/0956797613488145