title: Detecting gender before you know it: How implementation intentions control early gender categorization.
abstract: Gender categorization is highly automatic. Studies measuring ERPs during the presentation of male and female faces in a categorization task showed that this categorization is extremely quick (around 130ms, indicated by the N170). We tested whether this automatic process can be controlled by goal intentions and implementation intentions. First, we replicated the N170 modulation on gender-incongruent faces as reported in previous research. This effect was only observed in a task in which faces had to be categorized according to gender, but not in a task that required responding to a visual feature added to the face stimuli (the color of a dot) while gender was irrelevant. Second, it turned out that the N170 modulation on gender-incongruent faces was altered if a goal intention was set that aimed at controlling a gender bias. We interpret this finding as an indicator of nonconscious goal pursuit. The N170 modulation was completely absent when this goal intention was furnished with an implementation intention. In contrast, intentions did not alter brain activity at a later time window (P300), which is associated with more complex and rather conscious processes. In line with previous research, the P300 was modulated by gender incongruency even if individuals were strongly involved in another task, demonstrating the automaticity of gender detection. We interpret our findings as evidence that automatic gender categorization that occurs at a very early processing stage can be effectively controlled by intentions.
authors.affiliation: Department of Economics, University of Cologne, Albertus-Magnus-Platz, D-50923 Köln, Germany. Electronic address: sabine.huegelschaefer@uni-koeln.de.
authors.forename: Sabine
authors.initials: S
authors.lastname: Hügelschäfer
authors.affiliation: Department of Political and Social Sciences, Zeppelin University, Am Seemooser Horn 20, D-88045 Friedrichshafen, Germany. Electronic address: alexander.jaudas@zu.de.
authors.forename: Alexander
authors.initials: A
authors.lastname: Jaudas
authors.affiliation: Department of Political and Social Sciences, Zeppelin University, Am Seemooser Horn 20, D-88045 Friedrichshafen, Germany. Electronic address: anja.achtziger@zu.de.
authors.forename: Anja
authors.initials: A
authors.lastname: Achtziger
keywords: Automaticity; Control; ERPs; Gender categorization; Goals; Implementation intentions