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Recognized but harder to integrate: An eye-tracking study of French gender-fair forms during reading

Published online by Cambridge University Press:  06 March 2026

Julia Tibblin*
Affiliation:
Centre for Languages and Literature, Lund University, Sweden
Pascal Gygax
Affiliation:
Department of Psychology, University of Fribourg, Switzerland
Joost van de Weijer
Affiliation:
Lund University Humanities Lab, Lund University, Sweden
Jonas Granfeldt
Affiliation:
Centre for Languages and Literature, Lund University, Sweden
*
Corresponding author: Julia Tibblin; Email: julia.tibblin@rom.lu.se
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Abstract

Over the last few decades, linguistic gender-fair forms have become increasingly used by individuals and official institutions. In the French-speaking sphere, this has led to heated discussions among politicians and other stakeholders, some of whom claim that these forms render texts illegible and inaccessible to the general public. However, the processing of gender-fair forms in reading has been the topic of a few empirical studies. In the present paper, we add to this small body of research by reporting results from a pre-registered eye-tracking study where 58 native French-speakers read short texts which included a masculine form (voisins), complete double form (voisines et voisins), or contracted double form (voisin·es). Consistent with previous findings, the complete double forms were not more costly to process. In contrast, contracted double forms led to increased processing costs in intermediate and late stages of processing, but had no effect on the early stages of processing. Our data also indicate that the processing of contracted double forms becomes easier over time, and that it is facilitated by positive attitudes towards gender-fair language. These findings provide important insights that enlighten the current debate and should therefore be considered in the elaboration of official guidelines regarding gender-fair language.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is used to distribute the re-used or adapted article and the original article is properly cited. The written permission of Cambridge University Press or the rights holder(s) must be obtained prior to any commercial use.
Copyright
© The Author(s), 2026. Published by Cambridge University Press
Figure 0

Figure 1. Overview of the research design with example NPs in each condition. Dashed lines indicate the direct comparisons within each NP structure, whereas the solid lines indicate the indirect comparisons between NP structures, i.e., the comparisons of interest.

Figure 1

Table 1. All analyzed, local eye-movement measures, the cognitive process they are hypothesized to reflect (see Boston et al., 2008; Conklin et al., 2018), and their definition

Figure 2

Table 2. Mean (standard deviation) first fixation duration (FFD), gaze duration (GD), regression-path duration (RPD), total fixation duration (TFD), and actual percentage of regressions out of all fixations on the critical word (reg-in). Values are in milliseconds except for Reg-in

Figure 3

Figure 2. Total fixation durations (ms) (A) and probability of regression-in (B) by NP structure and Condition as predicted by the best model of fit. The vertical lines indicate the 95% confidence interval.

Figure 4

Figure 3. Predicted regression-path durations by Trial number, grouped by Condition and NP structure. The vertical lines indicate the 95% confidence interval.

Figure 5

Figure 4. Predicted gaze durations by Attitudes towards gender-fair language, grouped by Condition and NP structure. The vertical lines indicate the 95% confidence interval.

Figure 6

Table 3. Models of best fit for regression-path durations, total fixation durations, and regressions-in. Asterisks indicate p-values (* = <0.05, ** = <0.01, *** = <0.001). Note that the output for regressions-in varies since it is a generalized linear mixed-effects model and not a linear mixed-effects model. The number of observations also differs since in the first two models each data point represents one trial, whereas in the third each data point represents one fixation on the critical NP

Figure 7

Table 4. Model of best fit for gaze durations. Asterisks indicate p-values (* = <0.05, ** = <0.01, *** = <0.001)