EEG Measures of Facial Expression Recognition

The purpose of this study was to explore the role of the human mirror neuron system (hMNS) in the accurate identification of emotional facial expressions. Electroencephalography (EEG) was used to record Mu wave activity while participants preformed a series of video-matching tasks, in which they discriminated between facial stimuli by either emotional expression or model identity. A polygonmatching task was used as a baseline measure for mirror neuron activity. Mu Suppression Indices were calculated and compared between the identity-matching and emotion-matching conditions. Mu suppression was significantly increased in the emotion-matching condition, suggesting that the mirror neuron system is engaged to a greater extent during emotional facial expression processing than general face processing. Future research should further investigate this engagement of the hMNS, especially in relation to peripheral feedback mechanisms that might also be involved in recognizing emotional facial expressions. FACIAL EXPRESSION RECOGNITION 3 EEG Measures of Facial Expression Recognition Our lives are rich with emotional information. A mother embraces her child as he cries about his swollen and scraped knee. A basketball player glares into the eyes of his opponent, preparing for the opening tip of the game. Companies use emotions to help sell their products, while politicians manipulate them to persuade crowds. Emotions even penetrate the cyber realm, frequenting our emails and messages as emoticons. As a social species, the ability to process and understand these emotional signals is central to our daily function. Without it, we would be incapable of deciphering the intentions or motivations of those around us. Various strategies are used to communicate our emotions to each other. Sometimes they are voluntary—involving the vocalization of our thoughts and feelings. Other times they are subconscious—occurring through minute vocal, facial, or postural changes. All of this emotional information is constantly being detected and interpreted, with much of it processed instantly on a subconscious level. Facial expressions serve as one important route through which we gain emotional information. Without this superficial cue, obtaining implicit knowledge about the internal state to other individuals would become increasingly more challenging. Although facial expressions involve highly complex and varied muscular movements, most people are able to agree upon and distinguish between a vast spectrum of expression, doing so with a high degree of accuracy as to their corresponding emotion. Exactly how people are able to perform such as task has been the central question of many psychological studies over the past couple of years, as well as what type of cognitive or neurological mechanisms allow for this ability to be so swift and automatic for the general population. FACIAL EXPRESSION RECOGNITION 4 Several theories have been proposed regarding the mechanisms involved in facial expression recognition. Some of the leading hypotheses stem from the theory of embodied cognition, which asserts that people gain implicit knowledge of other minds through the simulation of other’s motor actions, making associations with that motor action and their our own internal states (Perry, Troje, & Bentin, 2010). Evidence for this theory has been presented through research investigating both peripheral and central neural mechanisms. Facial mimicry, when an observed facial expression is subconsciously reproduced using one’s own muscles, is one peripheral phenomenon that has been studied to help understand this process. People both overtly and covertly mimic facial expressions of those around them (Dimberg, Thunberg, & Elmhead, 2002). Theorists argue that this mimicry is a type of simulation technique, and is required for the ability to correctly identify emotions from the faces of other people. Niedenral et al. (2001) and Oberman et al. (2007) reported that participants preformed worse at identifying the emotions portrayed in pictures of facial expressions when they were unable to move their face. Similarly, individuals with autism spectrum disorder (ASD)— who do not participate in facial mimicry at all—show severe deficits in their ability identify emotions from facial expressions (McIntosh, Reichmann-Decker, Winkielman, & Wilbarger, 2006). These reports, along with a large body of supporting literature, serve as compelling evidence that facial mimicry plays a critical role in the recognition of emotional facial expressions. One central system that has also been implicated in the recognition and understanding of emotions is the mirror neuron system (MNS). Mirror neurons are neurons that are activated when performing motor actions (e.g., reaching for a ball), as FACIAL EXPRESSION RECOGNITION 5 well as when observing another person perform the same motor action. Thus, the name “mirror neuron” refers to the literal mirroring of an observed behavior as if the observer preformed it themselves. Similar to bodily movements, facial expressions involve very complex and specific muscle actions, and recent studies have investigated the role of mirror neurons in recognizing facial expressions. These studies have found that when mirror neuron activity is inhibited, participants are unable to effectively discriminate between different types of facial expressions (Pitcher, Garrido, Walsh, & Duchaine, 2008). Although many studies have reported compelling evidence implicating various roles of mirror neurons, many suffer from important methodological limitations. Stimulus control is often a limitation of these studies, as a result of the complex tasks designed to test the unique properties on the mirror neuron system (Muthukumaraswamy, Johnson, & McNair, 2004; Oberman, Pineda, & Ramachandran, 2007; Pitcher et al., 2008). Because of this, it is not clear whether the findings of these studies can be attributed to the suggested internal “mirroring” process, or rather a change in the stimulus presented. Thus, this study not only seeks to investigate the role of the human mirror neuron system in facial expression recognition, but also to determine whether mirror neuron activity reflects the internal task of identifying emotional facial expressions, or is simply a response to facial expressions as an external stimulus. To examine the role of the hMNS in facial expression recognition, we measured mirror neuron activity while participants preformed a video-matching task testing their ability to distinguish between various emotional facial expressions. Mu wave activity (8-13 Hz), an indirect measure for mirror neuron activity (Gastaut & Bert, 1954), was measured FACIAL EXPRESSION RECOGNITION 6 using electroencephalography (EEG). According to the findings of previous similar studies, we predict that there will be a higher degree of Mu suppression during the emotion-matching task, as compared to the identity-matching task.