Abstract

Faces drive our social interactions. A vast literature suggests an interaction between gender and emotional face perception, with studies using different methodologies demonstrating that the gender of a face can affect how emotions are processed. However, how different is our perception of affective male and female faces? Furthermore, how does our current affective state when viewing faces influence our perceptual biases? We presented participants with a series of faces morphed along an emotional continuum from happy to angry. Participants judged each face morph as either happy or angry. We determined each participant’s unique emotional ‘neutral’ point, defined as the face morph judged to be perceived equally happy and angry, separately for male and female faces. We also assessed how current state affect influenced these perceptual neutral points. Our results indicate that, for both male and female participants, the emotional neutral point for male faces is perceptually biased to be happier than for female faces. This bias suggests that more happiness is required to perceive a male face as emotionally neutral, i.e., we are biased to perceive a male face as more negative. Interestingly, we also find that perceptual biases in perceiving female faces are correlated with current mood, such that positive state affect correlates with perceiving female faces as happier, while we find no significant correlation between negative state affect and the perception of facial emotion. Furthermore, we find reaction time biases, with slower responses for angry male faces compared to angry female faces.

Highlights

  • When navigating the social world, humans rely on the information conveyed in faces

  • The influence of state affect in biasing emotional processing has been evaluated in clinical populations, and ignored, for the most part, in the non-clinical cohorts, which are the focus of the current study. It is beyond the scope of this paper to provide an overview of the influence of state affect on emotional processing in clinical populations, we provide a few relevant examples of findings in clinical populations with depression, a mood disorder characterized by negative mood and/or irritability along with impaired functioning (DSM-5; American Psychiatric Association, 2013)

  • Our study had three main aims: (1) to quantify perceptual biases in judging emotional information as a function of the gender of a face, (2) to determine the influence of current state affect on perceptual biases by correlating baseline biases in perceiving happy and angry male and female faces with measures of current positive and negative state affect and (3) to quantify biases in reaction time in judging emotional information as a function of face gender

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Summary

INTRODUCTION

When navigating the social world, humans rely on the information conveyed in faces. Faces help us identify people we know vs. people we do not and determine whom we can safely approach and whom we should avoid (McArthur and Baron, 1983). Despite much previous work demonstrating biases in reaction time or the allocation of attentional or memory resources in maintaining prioritized processing for angry-male and happy-female associations, we know of no studies to date that have directly quantified perceptual differences between emotional male and female faces. Our study had three main aims: (1) to quantify perceptual biases in judging emotional information as a function of the gender of a face, (2) to determine the influence of current state affect on perceptual biases by correlating baseline biases in perceiving happy and angry male and female faces with measures of current positive and negative state affect and (3) to quantify biases in reaction time in judging emotional information as a function of face gender. We remained ambivalent regarding reaction time differences: given that our task was not a speeded reaction time task, even though participants had a limited time to respond, our experimental paradigm might not be sensitive enough to quantify the expected reaction time differences

Participants
31–65 Female Male
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