AUTHOR=Guerra Ernesto , Bernotat Jasmin , Carvacho Héctor , Bohner Gerd TITLE=Ladies First: Gender Stereotypes Drive Anticipatory Eye-Movements During Incremental Sentence Interpretation JOURNAL=Frontiers in Psychology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.589429 DOI=10.3389/fpsyg.2021.589429 ISSN=1664-1078 ABSTRACT=

Immediate contextual information and world knowledge allow comprehenders to anticipate incoming language in real time. The cognitive mechanisms that underlie such behavior are, however, still only partially understood. We examined the novel idea that gender attitudes may influence how people make predictions during sentence processing. To this end, we conducted an eye-tracking experiment where participants listened to passive-voice sentences expressing gender-stereotypical actions (e.g., “The wood is being painted by the florist”) while observing displays containing both female and male characters representing gender-stereotypical professions (e.g., florists, soldiers). In addition, we assessed participants’ explicit gender-related attitudes to explore whether they might predict potential effects of gender-stereotypical information on anticipatory eye movements. The observed gaze pattern reflected that participants used gendered information to predict who was agent of the action. These effects were larger for female- vs. male-stereotypical contextual information but were not related to participants’ gender-related attitudes. Our results showed that predictive language processing can be moderated by gender stereotypes, and that anticipation is stronger for female (vs. male) depicted characters. Further research should test the direct relation between gender-stereotypical sentence processing and implicit gender attitudes. These findings contribute to both social psychology and psycholinguistics research, as they extend our understanding of stereotype processing in multimodal contexts and regarding the role of attitudes (on top of world knowledge) in language prediction.