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This study examines whether children acquiring Tseltal (Mayan) demonstrate a noun bias – an overrepresentation of nouns in their early vocabularies. Nouns, specifically concrete and animate nouns, are argued to universally predominate in children’s early vocabularies because their referents are naturally available as bounded concepts to which linguistic labels can be mapped. This early advantage for noun learning has been documented using multiple methods and across a diverse collection of language populations. However, past evidence bearing on a noun bias in Tseltal learners has been mixed. Tseltal grammatical features and child–caregiver interactional patterns dampen the salience of nouns and heighten the salience of verbs, leading to the prediction of a diminished noun bias and perhaps even an early predominance of verbs. We here analyze the use of noun and verb stems in children’s spontaneous speech from egocentric daylong recordings of 29 Tseltal learners between 0;9 and 4;4. We find weak to no evidence for a noun bias using two separate analytical approaches on the same data; one analysis yields a preliminary suggestion of a flipped outcome (i.e. a verb bias). We discuss the implications of these findings for broader theories of learning bias in early lexical development.
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Availability-Based Production Predicts Speakers’ Real-time Choices of Mandarin Classifiers
Speakers often face choices as to how to structure their intended message into an utterance. Here we investigate the influence of contextual predictability on the encoding of linguistic content manifested by speaker choice in a classifier language, Mandarin Chinese. In Mandarin, modifying a noun with a numeral obligatorily requires the use of a classifier. While different nouns are compatible with different SPECIFIC classifiers, there is a GENERAL classifier that can be used with most nouns. When the upcoming noun is less predictable, using a more specific classifier would reduce the noun’s surprisal, potentially facilitating comprehension (predicted to be preferred under Uniform Information Density, Levy & Jaeger, 2007), but the specific classifier may be dispreferred from a production standpoint if the general classifier is more easily available (predicted by Availability-Based Production; Bock, 1987; Ferreira & Dell, 2000). Here we report a picture-naming experiment confirming two distinctive predictions made by Availability-Based Production.
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- Award ID(s):
- 1844723
- PAR ID:
- 10130644
- Date Published:
- Journal Name:
- Proceedings of the Annual Conference of the Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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