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Gender bias in morphological inferences

Published online by Cambridge University Press:  20 December 2021

Sara Finley Open the ORCID record for Sara Finley [Opens in a new window] ,
Saara Charania ,
Tiarra Lewis ,
Barbara Millward  and
Stella Wang
Sara Finley*
Affiliation:
Pacific Lutheran University
Saara Charania
Affiliation:
Pacific Lutheran University
Tiarra Lewis
Affiliation:
Pacific Lutheran University
Barbara Millward
Affiliation:
Pacific Lutheran University
Stella Wang
Affiliation:
Pacific Lutheran University
*
*Corresponding author. Email: [email protected]
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Abstract

The present study explores how language learners apply gender stereotypes in learning a novel language with grammatical gender. Adult, English-speaking participants were exposed to picture–sound pairs from a miniature language. Each picture was of a matched gendered professional (e.g., male tennis player, female tennis player) with a nonsense form [CVCV-go/gu]. Participants were exposed to 32 picture–sound pairs (16 male, and 16 female, all matched) five times in a randomized order. Following training, participants were given a two-alternative forced-choice test with novel picture–word pairs. Participants were presented with a novel picture paired with two words (e.g., [befegu vs. befebo]) and were asked to choose which word most likely portrayed the meaning conveyed by the picture. These novel items contained gender-matched professions (e.g., male and female chemist), neutral items (office supplies), stereotypically female items (makeup), and stereotypically male items (tools). Participants assigned the appropriate gender to the novel professions, and assigned gender in line with the stereotyped objects at a rate significantly greater than chance (but not for neutral items). These results support the hypothesis that learning a language with a binary grammatical gender might be influenced by gender stereotypes.

Keywords

artificial language learningmorphologynoun classesgender stereotypes
Type
Article
Information
Language and Cognition , Volume 14 , Issue 1 , March 2022 , pp. 47 - 60
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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