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. 2023 Aug 23;18(8):e0289669.
doi: 10.1371/journal.pone.0289669. eCollection 2023.

Assessing the size of non-Māori-speakers' active Māori lexicon

Yoon Mi Oh  1 , Simon Todd  2   3 , Clay Beckner  4 , Jen Hay  3   5 , Jeanette King  3   6
Affiliations

Assessing the size of non-Māori-speakers' active Māori lexicon

Yoon Mi Oh et al. PLoS One. .

Abstract

Most non-Māori-speaking New Zealanders are regularly exposed to Māori throughout their lives without seeming to build any extensive Māori lexicon; at best, they know a small number of words which are frequently used and sometimes borrowed into English. Here, we ask how many Māori words non-Māori-speaking New Zealanders know, in two ways: how many can they identify as real Māori words, and how many can they actively define? We show that non-Māori-speaking New Zealanders can readily identify many more Māori words than they can define, and that the number of words they can reliably define is quite small. This result adds crucial support to the idea presented in earlier work that non-Māori-speaking New Zealanders have implicit form-based (proto-lexical) knowledge of many Māori words, but explicit semantic (lexical) knowledge of few. Building on this distinction, we further ask how different levels of word knowledge modulate effects of phonotactic probability on the accessing of that knowledge, across both tasks and participants. We show that participants' implicit word knowledge leads to effects of phonotactic probability-and related effects of neighbourhood density-in a word/non-word discrimination task, but not in a more explicit task that requires the active definition of words. Similarly, we show that the effects of phonotactic probability on word/non-word discrimination are strong among participants who appear to lack explicit word knowledge, as indicated by their weak discrimination performance, but absent among participants who appear to have explicit word knowledge, as indicated by their strong discrimination performance. Together, these results suggest that phonotactic probability plays its strongest roles in the absence of explicit semantic knowledge.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Non-Māori-speaking New Zealanders’ mean wordhood confidence ratings for real word and non-word stimuli per frequency bin, and their relation to phonotactic score.
(Licensed under Creative Commons Attribution 4.0. https://creativecommons.org/licenses/by/4.0).
Fig 2
Fig 2. Raw data from Experiment 1 (Word Identification task) showing mean wordhood confidence ratings for real word (blue triangles) and non word (black circles) stimuli.
Fig 3
Fig 3. Partial effect plots for predicted ratings.
It shows interactions of participant d-prime with effects of: (a) the interaction of phonotactic score and stimulus type (word or non-word); (b) the presence of a macron; (c) the number of phonological neighbours. Participant d-prime was entered in the statistical model as a continuous variable, but is taken at two fixed values here for illustrative purposes: high (d-prime = 3) and low (d-prime = 1).
Fig 4
Fig 4. Proportion of correct definitions as a function of familiarity, based on the predicted mean ratings from Experiment 1.
Selected words are labeled adjacent to the corresponding point.
See this image and copyright information in PMC

References

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