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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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A resource-rational model of human processing of recursive linguistic structure
A major goal of psycholinguistic theory is to account for the cognitive constraints limiting the speed and ease of language comprehension and production. Wide-ranging evidence demonstrates a key role for linguistic expectations: A word’s predictability, as measured by the information-theoretic quantity of surprisal, is a major determinant of processing difficulty. But surprisal, under standard theories, fails to predict the difficulty profile of an important class of linguistic patterns: the nested hierarchical structures made possible by recursion in human language. These nested structures are better accounted for by psycholinguistic theories of constrained working memory capacity. However, progress on theory unifying expectation-based and memory-based accounts has been limited. Here we present a unified theory of a rational trade-off between precision of memory representations with ease of prediction, a scaled-up computational implementation using contemporary machine learning methods, and experimental evidence in support of the theory’s distinctive predictions. We show that the theory makes nuanced and distinctive predictions for difficulty patterns in nested recursive structures predicted by neither expectation-based nor memory-based theories alone. These predictions are confirmed 1) in two language comprehension experiments in English, and 2) in sentence completions in English, Spanish, and German. More generally, our framework offers computationally explicit theory and methods for understanding how memory constraints and prediction interact in human language comprehension and production.
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- Award ID(s):
- 2121074
- PAR ID:
- 10376126
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 43
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2122602119
