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Processing of Fear and Anger Facial Expressions: the Role of Spatial Frequency Comfort, W. E., Wang, M., Benton, C. P., & Zana, Y. (2013). Processing of fear and anger facial expressions: the role of spatial frequency. Frontiers in Psychology, 4, 213. https://doi.org/10.3389/fpsyg.2013.00213 Publisher's PDF, also known as Version of record Link to published version (if available): 10.3389/fpsyg.2013.00213 Link to publication record in Explore Bristol Research PDF-document University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ ORIGINAL RESEARCH ARTICLE published: 26 April 2013 doi: 10.3389/fpsyg.2013.00213 Processing of fear and anger facial expressions: the role of spatial frequency William E. Comfort 1*, Meng Wang 2, Christopher P.Benton3 andYossi Zana1 1 Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo André, Brazil 2 Institute of Neuroscience, Guangzhou Medical University, Guangzhou, China 3 School of Experimental Psychology, University of Bristol, Bristol, UK Edited by: Spatial frequency (SF) components encode a portion of the affective value expressed in Linda Isaac, Palo Alto VA and Stanford face images.The aim of this study was to estimate the relative weight of specific frequency University, USA spectrum bandwidth on the discrimination of anger and fear facial expressions.The general Reviewed by: Linda Isaac, Palo Alto VA and Stanford paradigm was a classification of the expression of faces morphed at varying proportions University, USA between anger and fear images in which SF adaptation and SF subtraction are expected Charles Alain Collin, University of to shift classification of facial emotion. A series of three experiments was conducted. In Ottawa, Canada Experiment 1 subjects classified morphed face images that were unfiltered or filtered to *Correspondence: remove either low (<8 cycles/face), middle (12–28 cycles/face), or high (>32 cycles/face) William E. Comfort, Centro de Matemática, Computação e SF components. In Experiment 2 subjects were adapted to unfiltered or filtered prototyp- Cognição, Universidade Federal do ical (non-morphed) fear face images and subsequently classified morphed face images. In ABC, Rua Santa Adélia 166, Santo Experiment 3 subjects were adapted to unfiltered or filtered prototypical fear face images André, São Paulo 09.210-170, Brazil. with the phase component randomized before classifying morphed face images. Remov- e-mail: [email protected] ing mid frequency components from the target images shifted classification toward fear. The same shift was observed under adaptation condition to unfiltered and low- and middle- range filtered fear images. However, when the phase spectrum of the same adaptation stimuli was randomized, no adaptation effect was observed. These results suggest that medium SF components support the perception of fear more than anger at both low and high level of processing. They also suggest that the effect at high-level processing stage is related more to high-level featural and/or configural information than to the low-level frequency spectrum. Keywords: spatial frequency, face adaptation, fear, anger, facial expressions, face processing INTRODUCTION documented (Costen et al., 1996; Näsänen, 1999; Goffaux et al., Face perception includes the integration of complex visual input 2003; Rotshtein et al., 2007; Gao and Bentin, 2011), there is less across a spectrum of varying spatial frequencies (SFs) (Sowden information about how our neural representation of a facial emo- and Schyns, 2006). Different SFs are transmitted to higher cortical tion is related to specific SF ranges. Studies which have focused regions through separate neurological channels broadly related to on this issue (Vuilleumier et al., 2003; Deruelle and Fagot, 2004; the magnocellular and parvocellular pathways originating in the Aguado et al., 2010; Kumar and Srinivasan, 2011) have examined retina. High spatial frequency (HSF) information is processed pri- the effect of high and low SF on the perception of emotional marily through the ventral visual processing stream from V1 and expressions using removal of specific SF components from the encodes the fine-grained texture of objects such as faces. In con- target stimuli. For example, Kumar and Srinivasan (2011) showed trast, low spatial frequency (LSF) information is encoded through that low (<8 cycles/face) and high-frequencies (>32 cycles/face) faster, more direct networks in subcortical and early visual areas are more critical for the representation of happy and sad expres- and communicates rapid, coarse signals concerning the configura- sions, respectively. Aguado et al. (2010) compared reaction time to tion or spatial relationship between facial features (for an overview, classification of happy and anger faces and found an advantage of see Ruiz-Soler and Beltran, 2006). Subsequent outputs from these LSF (<12 cycles/degree) over HSF (>3 cycle/degree). Vuilleumier pathways typically converge in higher-level regions, including the et al. (2003), on the other hand, found no differences between amygdala, fusiform gyrus, and orbitofrontal cortex (Vuilleumier reaction time and accuracy for discrimination of fear over neutral et al., 2003; Bar et al., 2006; Rotshtein et al., 2007). Previous studies expression for low (2–8 cycles/face) and middle (8–16 cycles/face) have suggested that information from high and low SF bandwidths SF band-pass filtered images. Similarly, Deruelle and Fagot (2004) may be effectively integrated at these sites, producing a neural rep- found no differences in adult subjects between discrimination resentation of the face to guide subsequent recognition (Eger et al., of low (<12) and high-pass (>36) filtered faces with a smiling 2004; Gauthier et al., 2005; Rotshtein et al., 2007). While the inte- or grimacing expression (participants aged below nine showed a gration of facial identity through SF information has been well bias toward high-pass filtered images). Although important, none www.frontiersin.org April 2013 | Volume 4 | Article 213 | 1 Comfort et al. Facial processing of fear and anger of these results advance our current knowledge regarding the SF other views face adaptation as equivalent to a shift in the proto- tuning at different phases of facial expression processing. In this type of a multidimensional “face space,”where the facial attribute study we are aim to discriminate between low and high levels is encoded in terms of its deviation from an average (e.g., Rob- of processing SF content in the representation of facial expres- bins et al., 2007). In the present study the latter continuum-based sions. Potentially, this will reveal further details of how and at what approach was taken. stage differing SF bandwidths are integrated. Visual adaptation, a Given a reference point xi at which a certain morphed face psychophysical method commonly associated with higher-order image is perceived as expressing fear and anger at equal proba- visual processing (Fox and Barton, 2007; Webster and MacLeod, bility (a balance point), manipulation of frequency content and 2011) may provide answers as to how visual SFs are processed at adaptation state is expected to shift that point to one of the two relatively late stages of facial expression analysis. extremes. For example, if low frequency components are used to Visual adaptation, in the context of face processing, takes the encode fear more than anger, removing low SF content from the form of prolonged exposure to a target face which can affect the stimuli will increase the probability of the face image at point xi to subsequent perception of facial attributes and provide valuable be classified as expressing anger. Fear and anger expressions were insights into the pattern of visual coding used in facial processing chosen due to the fact that these form opposites along such a con- (for an overview, see Webster and MacLeod, 2011). In a typical tinuum in the feature dimension (e.g., distance between eyebrows experimental setting, a subject is exposed to a particular face, and eyes, aperture of mouth) and they are negatively correlated in causing faces which are presented afterward to be perceived as terms of their diagnostic information (Smith et al., 2005) and evo- less similar to the adapting face in comparison to a condition lutionary perspective (anger as dominant, fear as submissive; see with no adaptation. Such face adaptation aftereffects are robust Leppänen and Nelson, 2009). Adaptation to either facial expres- across changes in the size, retinal position, and orientation of facial sion is expected to cause the morphed face to appear more similar images and exhibit similar patterns of adaptation across low-level to the opposite expression. As an alternative to methods adopted features such as contrast, color, and SF (Leopold et al., 2001; Wat- by other studies examining adaptation to facial expressions (Hsu son and Clifford, 2003; Yamashita et al., 2005). Adaptation is also and Young, 2004; Fox and Barton, 2007; Juricevic and Webster, specific to facial attributes: aftereffects have been documented for 2012), this design evaluates adaptation aftereffects in the context facial properties such as identity (Leopold et al., 2001, 2005), gen- of ambivalent expressions instead of neutral expressions (Webster der, race, and expression (Webster et al., 2004; Furl et al., 2007). As et al.,
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