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The effect of lexicalization biases on cross-situational statistical learning of novel verbs

Published online by Cambridge University Press:  25 January 2024

Nathan R. George*
Affiliation:
Derner School of Psychology, Adelphi University, Garden City, NY, USA
Sabina Ciaccio
Affiliation:
Audiology, The City University of New York Graduate Center, New York, NY, USA
Lucia Berry
Affiliation:
Audiology, The City University of New York Graduate Center, New York, NY, USA
Daniel J. Weiss
Affiliation:
The National Academies of Sciences, Engineering, and Medicine, Washington, DC, USA
*
Corresponding author: Nathan R. George; Email: ngeorge@adelphi.edu
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Abstract

Languages vary in the mapping of relational terms onto events. For instance, English motion descriptions favor manner (how something moves) verbs over path (where something move) verbs, whereas those of other languages, like Spanish, show the opposite pattern. While these lexicalization biases are malleable, adopting a novel lexicalization pattern can be slow for second language learners. One potential mechanism for learning non-native verb mappings is cross-situational statistical learning (CSSL). However, the application of CSSL to verbs is limited and does not explicitly examine how lexicalization biases may complicate adults’ ability to resolve the referential uncertainty of multiple referents. We ask English-speaking monolingual adults to learn the mappings of ten verbs via CSSL. Verbs mapped onto either manner or path of motion, with the other event component held constant. Adults in both conditions demonstrated successful learning of novel verbs, with adults learning the manner verbs showing more consistent performance across accepting correct referents and rejecting incorrect ones. Our results are the first to demonstrate adults’ use of CSSL to acquire verb meanings that both align with and cut against native lexicalization biases and suggest a limited influence of lexicalization biases on adults’ learning in idealized CSSL conditions.

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Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press
Figure 0

Table 1. Target paths and manners of motion

Figure 1

Figure 1. Examples of training videos from the manner (jumping jacks; left frame) and path condition (exiting; right frame).

Figure 2

Table 2. Model 1 estimates fixed effects

Figure 3

Figure 2. Performance on test blocks over time. Error bars represent standard error.

Figure 4

Table 3. Model 2 estimates fixed effects

Figure 5

Figure 3. Performance on test trials (marginal means), broken down by trial type. Error bars represent standard error. See Tables 4-6 for comparisons of significance

Figure 6

Table 4. Contrasts exploring differences between referent and distractor test trials by condition

Figure 7

Table 5. Contrasts for effects of test type by condition

Figure 8

Table 6. Contrasts for effects of condition by test type

Figure 9

Table 7. Marginal means