Abstract

Investigating how infants first establish relationships between words is a necessary step towards understanding the qualitative shift children make to an organised and complex interconnected network of semantic relationships which characterises a mature, adult lexical-semantic system. Since little is known about the word-word associations in infants that establish this network of meanings (Arias-Trejo & Plunkett, 2009), this thesis sought to, first, document the word associations (WA)s that young monolingual and bilingual children produce and then compare these to adult WAs. A concurrent aim was to establish a database of child-specific WAs as a resource for future studies. Second, to understand how a network of meaning establishes in different groups during infancy, an online semantic priming paradigm was developed due to the Covid-19 pandemic. The aim was to see how words are organised in the emergent lexical-semantic system by replicating in-lab findings and extending these to explore different infant groups. In parallel, this paradigm was used to validate the WAs found in monolingual and bilingual children. Findings from Chapter 1 revealed that children share some of the WAs that adults exhibit in a mature lexical-semantic system. However, a large number of WAs shared by children were not represented in the WA norms of adults. This could indicate that adult norms under-represent the associations of children, as they might not capture the unique developmental stage and life experience of 3-year-olds. This research presents a resource of child-specific associated word pair stimuli for future studies. Findings from Chapter 2 indicate that lexical-semantic links might be more robust in the lexical-semantic system of a 3-year-old when they capture associative meaning compared to taxonomic meaning. Furthermore, running infant studies online can replicate in-lab findings, though it remains unclear if this is only true of certain paradigms.

Document Type

Thesis

Publication Date

2023-01-01

DOI

10.24382/2766

Download

Share

COinS