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Figure 1: An example for a phonological network based on the words a specific child knows at a specific age. The connections between the words exist when the Levenshtein distance between two words is smaller or equal .25.

The Development of Early Phonological Networks

Judith Kalinowski, Laura Hansel, Michaela Vystrčilová, Alexander Ecker, and Nivedita Mani

The influence of phonological similarity between words on the development of early vocabulary has caught the interest of language learning research in recent years. In order to investigate the influence of already learned words on the acquisition of new and phonologically similar words, the vocabulary of individual children must be analyzed over time, as each child learns in a different way. Previous research has used networks to represent children's vocabulary. However, these are based on data averaged over many children (Fourtassi et al., 2020; Siew & Vitevitch, 2020). Laing (2022) used data from individual children in her analysis, but only had a small data set. Because of the different data and the different phonological distances used in the previous studies, the results of the analyses differ: Fourtassi et al. (2020) showed that words that have many phonologically similar words in the children's linguistic environment are more likely to be learned earlier than words that have hardly any similar words in the environment (EXT). Laing (2022) found that previously learned words that are phonologically similar to many other previously learned words attract similar new words. Consequently, children would learn these first (INT). However, Siew & Vitevitch (2020) found that both INT and EXT are statistically significant predictors of word learning, but also argued that INT has a greater impact on phonological network growth. In the present work, we address the problem regarding different (non-longitudinal) data and phonological distances by using a large longitudinal data set of 1,565 Norwegian children, comparing three different phonological distances, and applying them to our model. This allows us to investigate whether early lexicons grow in an INT- or EXT-like manner and whether different phonological distances lead to different results.

Find the preregistration to this project on the Open Science Framework: https://osf.io/kd95f/registrations.

The code can be accessed via GitHub: https://github.com/JudithKalinowski/LongPhon.

Figure 2: Exemplary objects of two different categories which will be used in the experiment.

Form-Meaning Systematicity in
Early Referent Selection

Judith Kalinowski, Ming Yean Sia, Janna Leismann, and Nivedita Mani

How do systematic word-meaning and word-sound mappings, that is, words which are not only similar in meaning but also in sound (e.g., cat and rat), impact word acquisition? Similarity in either word form or word meaning is shown to boost word learning (Altvater-Mackensen & Mani, 2013; Borovsky et al., 2016; Laing, 2022; Newman et al., 2008). However, regarding systematic word-form and word-meaning mappings, previous research findings are contradictory: On the one hand, Fourtassi et al. (2021) suggest that overlap at either the visuo-perceptual or phonological level in word-object mappings impedes novel word learning. On the other hand, Monaghan et al. (2011) suggest that systematic form-meaning relations aid lexical acquisition by constraining potential word meanings. The current research project addresses this incongruity by investigating whether children expect similar sounding words to refer to similar looking objects. Using a preferential looking paradigm, children are first trained with two novel word-object associations. After that, in the retention test trials, we test whether children have learned these novel word-object associations. Critically, in the leveraging test trials, we examine whether children will associate a super-novel label which sounds similar to the trained label to a super-novel object which looks similar to the referent of the trained label. We expect children to look preferentially at the perceptually similar object when asked for a phonologically similar label. These results would suggest that similarity in word-form and word-meaning aids word learning. In addition, we have differentiation test trials to check whether children are able to distinguish between two similar-sounding words and two similar-looking objects.

The preregistration to this project will be uploaded to the Open Science Framework.

The code will be made accessible via GitHub.