Anger superiority effect: The importance of dynamic emotional facial expressions

Abstract

A rapid response to a threatening face in a crowd is important to successfully interact in social environments. Visual search tasks have been employed to determine whether there is a processing advantage for detecting an angry face in a crowd, compared to a happy face. The empirical findings supporting the ''anger superiority effect'' (ASE), however, have been criticized on the basis of possible lowlevel visual confounds and because of the limited ecological validity of the stimuli. Moreover, a ''happiness superiority effect'' is usually found with more realistic stimuli. In the present study, we tested the ASE by using dynamic (and static) images of realistic human faces, with validated emotional expressions having similar intensities, after controlling the bottom-up visual saliency and the amount of image motion. In five experiments, we found strong evidence for an ASE when using dynamic displays of facial expressions, but not when the emotions were expressed by static face images.Keywords: Anger superiority effect; Dynamic faces; Face-in-the-crowd effect; Face perception; Emotion.Evolutionary arguments may lead us to expect that threatening faces are detected more efficiently among a crowd of distractor faces than nonthreatening faces. Because facial threat provides a particularly potent social Please address all correspondence to Corrado Caudek, Department of Neuroscience, Psychology, Pharmacology, and Child's Health (NEUROFARBA), University of Florence, Via di San Salvi 12, Complesso di San Salvi, Padiglione 26, 50135 Firenze, Italy. E-mail: corrado.caudek@unifi.it These findings suggest that the ASE might be mediated by mechanisms that are summoned only in part (or not at all) by stimuli lacking the typical dynamic characteristics of facial emotion in natural settings.

Posterror speeding after threat-detection failure.

“…In a previous study, which investigated the ASE under both static and dynamic conditions, we 8 indeed found evidence for an ASE when using dynamic displays of facial expressions, but not when the emotions were expressed by static face images (Ceccarini & Caudek, 2013).…”

“…Like in Experiment 1, the stimulus conditions of Ceccarini and Caudek (2013) are not optimal to study error reactivity after the failure to detect a threat. In fact, dynamic and static displays were randomly intermixed in half of the trials of their experiments, and an ASE was found for the dynamic displays only 3 .…”

“…Stimuli were 3D face models of nine different facial identities showing angry, happy, and neutral facial expressions ( Figure 1, panel B). The stimuli were generated by following the procedure described by Ceccarini and Caudek (2013). (Becker et al, 2011;Purcell, Stewart, & Skov, 1996).…”

“…(Becker et al, 2011;Purcell, Stewart, & Skov, 1996). To address thisissue, following the same procedure described by Ceccarini and Caudek (2013), we modified the nine happy face images with the 3dStudio Max software in order to create 3D face models expressing happiness with a closed mouth. The 3D face models were then transformed in video sequences (30 fps) by means of the Flash CS5 software.…”

“…We measured the amount of image motion in order to insure that it was approximately the same across the three emotional expressions (angry, happy, and neutral faces; see Horstmann & Ansorge, 2009). Following the same procedure described by Ceccarini and Caudek (2013) Performance accuracy. The percentages of errors were 12.8%, 6.7%, and 2.1% for nonthreatening targets, threatening targets, and target-absent trials, respectively.…”