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Bilingual toddlers show increased attention capture by static faces compared to monolinguals

Published online by Cambridge University Press:  20 January 2023

Victoria L Mousley
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, United Kingdom Deafness, Cognition and Language Research Centre, University College London, London, United Kingdom
Mairéad MacSweeney*
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, United Kingdom Deafness, Cognition and Language Research Centre, University College London, London, United Kingdom
Evelyne Mercure
Affiliation:
Birkbeck, University of London, London, United Kingdom Goldsmiths, University of London, London, United Kingdom
*
Address for correspondence: Mairéad MacSweeney Alexandra House UCL Institute of Cognitive Neuroscience 17–19 Queen Square London WC1N 3AZ United Kingdom E-mail: m.macsweeney@ucl.ac.uk
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Abstract

Bilingual infants rely differently than monolinguals on facial information, such as lip patterns, to differentiate their native languages. This may explain, at least in part, why young monolinguals and bilinguals show differences in social attention. For example, in the first year, bilinguals attend faster and more often to static faces over non-faces than do monolinguals (Mercure et al., 2018). However, the developmental trajectories of these differences are unknown. In this pre-registered study, data were collected from 15- to 18-month-old monolinguals (English) and bilinguals (English and another language) to test whether group differences in face-looking behaviour persist into the second year. We predicted that bilinguals would orient more rapidly and more often to static faces than monolinguals. Results supported the first but not the second hypothesis. This suggests that, even into the second year of life, toddlers’ rapid visual orientation to static social stimuli is sensitive to early language experience.

Information

Type
Research 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
Copyright © The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1. Example of face pop-out stimuli.Note. Two examples of face pop-out slides originally designed by Gliga et al. (2009). Each slide contained five areas of interest defined in MATLAB: face, noisy face, car, phone, and bird. The noisy face areas were created by randomising the phase spectra of the face on the slide while maintaining the outer face contour.

Figure 1

Figure 2. Boxplot of significant AOI x Group interaction on the outcome of attention capture (Hypothesis 1).Note. Bilinguals and monolinguals differed in their attention capture by faces and non-faces, such that bilinguals were faster to look to faces and slower to look to non-faces than monolinguals. Bilinguals are represented in white, and monolinguals are represented in grey. Fixation latency is calculated as the time between onset of the face pop-out slide and toddlers’ first fixation to the area of interest (face or non-face). The non-face values were calculated within-participant as an average of toddlers’ fixation latencies to the four non-face areas (i.e., noisy face, car, phone, and bird). The error bars represent standard error.

Figure 2

Table 1. Fixation latency and count measures to each area on face pop-out slides by group.

Figure 3

Figure 3. Boxplot of non-significant AOI x Group interaction on the outcome of attention maintenance (Hypothesis 2).Note. There was a main effect of AOI but not of group. The interaction of AOI x Group was not significant. Bilinguals are represented in white, and monolinguals are represented in grey. Fixation maintenance is calculated as the average number of return fixations to the areas of interest (face or non-face) that each participant made per completed trial. The non-face values were calculated within-participant as an average of toddlers’ fixations to the four non-face areas (i.e., noisy face, car, phone, and bird). The error bars represent standard error.

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