Constructing the Mandarin Phonological Network: Novel Syllable Inventory Used to Identify Schematic Segmentation

Abstract EN

The purpose of this study was to construct, measure, and identify a schematic representation of phonological processing in the tonal language Mandarin Chinese through the combination of network science and psycholinguistic tasks.

Two phonological association tasks were performed with native Mandarin speakers to identify an optimal phonological annotation system.

The first task served to compare two existing syllable inventories and to construct a novel system where either performed poorly.

The second task validated the novel syllable inventory.

In both tasks, participants were found to manipulate lexical items at each possible syllable location, but preferring to maintain whole syllables while manipulating lexical tone in their search through the mental lexicon.

The optimal syllable inventory was then used as the basis of a Mandarin phonological network.

Phonological edit distance was used to construct sixteen versions of the same network, which we titled phonological segmentation neighborhoods (PSNs).

The sixteen PSNs were representative of every proposal to date of syllable segmentation.

Syllable segmentation and whether or not lexical tone was treated as a unit both affected the PSNs’ topologies.

Finally, reaction times from the second task were analyzed through a model selection procedure with the goal of identifying which of the sixteen PSNs best accounted for the mental target during the task.

The identification of the tonal complex-vowel segmented PSN (C_V_C_T) was indicative of the stimuli characteristics and the choices participants made while searching through the mental lexicon.

The analysis revealed that participants were inhibited by greater clustering coefficient (interconnectedness of words according to phonological similarity) and facilitated by lexical frequency.

This study illustrates how network science methods add to those of psycholinguistics to give insight into language processing that was not previously attainable.