The similar growth dynamics observed in phonological networks of first and second language users may indicate a universal cognitive principle underlying word learning.Д¬ognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. Results mirror findings by Siew and Vitevitch and demonstrate that preferential attachment is the main network growth algorithm driving lexical learning at early second-language proficiency stages, while inverse preferential attachment prevails at more advanced proficiency stages.

The present study was designed as a replication of Siew and Vitevitch's (2020a) study 'An investigation of network growth principles in the phonological language network' with data of English-as-a-second-language learners.

Their findings were confirmed for various languages, they fit with assumptions of cognitive efficiency in lexical memory and 2 retrieval, and thus are intriguing for second language research as well. They identified a unique developmental trajectory in network growth, with high-density neighborhoods becoming enriched through growth at early acquisition stages (the 'preferential attachment' mechanism) but low-density neighborhoods gaining new neighbors at advanced acquisition stages (termed 'inverse preferential attachment'). A recent study by Siew and Vitevitch (2020a) investigated word form lexica and their growth in children acquiring English and Dutch as first languages from a network perspective.

1 hop design