Phonological network fluency identifies phonological restructuring through mental search

Abstract

We investigated network principles underlying mental search through a novel phonological verbal fluency task. Post exclusion, 95 native-language Mandarin speakers produced as many items that differed by a single lexical tone as possible within one minute. Their verbal productions were assessed according to several novel graded fluency measures, and network science measures that accounted for the structure, cohesion and interconnectedness of lexical items. A multivariate regression analysis of our participants’ language backgrounds included their mono- or multi-lingual status, English proficiency, and fluency in other Chinese languages/dialects. Higher English proficiency predicted lower error rates and greater interconnectedness, while higher fluency in other Chinese languages/dialects revealed lower successive similarity and lower network coherence. This inverse relationship between English and other Chinese languages/dialects provides evidence of the restructuring of the phonological mental lexicon.

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

“…Empirical and theoretical research in the cognitive sciences identified these mental representations of knowledge as components of a way more complex system called mental lexicon, a repository of knowledge apt at information acquisition, processing, and use [21]. The recent adoption of network science tools has shown how the large-scale, associative structure of word knowledge in the mental lexicon is highly informative of a wide variety of cognitive processes such as lexical processing [19,22,23,24], learning and cognitive development [25,26,27], text structuring and writing styles [28,29,30,31], creativity [32,33], and expertise levels in specific domains [34,5]. Analogously, forma mentis networks act as approximated reconstructions on the mental constructs built by individuals in their associative mental lexicon, representing their perceptions of the outer world [16,17].…”

Database of word-level statistics for Mandarin Chinese (DoWLS-MAN)

“…(Brennan et al 2013). These results, which suggest that the phonological mental lexicon undergoes a restructuring for first language (L1) speakers of languages that use alphabets, help to understand why Mandarin second language (L2) speakers of English would similarly show evidence of segmentation in speech production tasks (Neergaard, Luo, and Huang 2019;Verdonschot et al 2013). found evidence to support the influence of orthography on phonology within a verbal fluency task in which participants produced as many phonological neighbors to a given Mandarin monosyllable as possible within 1 minute.…”

“…Phonological neighborhood measures have long been shown to influence lexical processing. While research on the effects of phonological neighbors has primarily taken place with English and Spanish speakers (For a review, see Vitevitch & Luce, 2016), a recent focus has looked to Mandarin (Neergaard, Britton, et al 2019;Neergaard, Luo, et al 2019;Wiener and Turnbull 2016).…”

“…Analyses of phonological networks that go beyond the word-level, referred to as the network's topology, have been illustrative of the role that segmentation plays in speech processing. Topological features extracted from phonological networks have been used to analyze both participant-level verbal productions (Neergaard, Luo, et al 2019), and whole-vocabularies (Arbesman, Strogatz, and Vitevitch 2010b, 2010aBrown et al 2018;Dautriche et al 2017;Shoemark et al 2016;Siew 2013;Siew and Vitevitch 2019;Stella et al 2018;Stella and Brede 2015;Turnbull and Peperkamp 2016;Vitevitch 2008). constructed the sixteen Mandarin phonological networks seen in Table 1.…”

Comparing the semantic networks of children with cochlear implants and children with typical hearing: Effects of length of language access.

“…strength of association between words, based on for example their semantic relatedness, is represented by the edge. Different tasks have been used to estimate the networks included in the mental lexicon, for example association tasks (Kenett, Anaki, & Faust, 2014), phonological neighbor tasks (Neergaard, Luo, & Huang, 2019), and semantic verbal fluency tasks (Borodkin et al, 2016b;Kenett, Beaty, Silvia, Anaki, & Faust, 2016;Kenett et al, 2013). In semantic verbal fluency tasks participants are asked to name as many words as possible from a specific category (for example animals) within a time limit (often one minute).…”