Emotion perception is mediated by spatial frequency content.

Abstract

Spatial frequencies have been shown to play an important role in face identification, but very few studies have investigated the role of spatial frequency content in identifying different emotions. In the present study we investigated the role of spatial frequency in identifying happy and sad facial expressions. Two experiments were conducted to investigate (a) the role of specific spatial frequency content in emotion identification, and (b) hemispherical asymmetry in emotion identification. Given the links between global processing, happy emotions, and low frequencies, we hypothesized that low spatial frequencies would be important for identifying the happy expression. Correspondingly, we also hypothesized that high spatial frequencies would be important in identifying the sad expression given the links between local processing, sad emotions, and high spatial frequencies. As expected we found that the identification of happy expression was dependent on low spatial frequencies and the identification of sad expression was dependent on high spatial frequencies. There was a hemispheric asymmetry with the identification of sad expression, especially in the right hemisphere, possibly mediated by high spatial frequency content. Results indicate the importance of spatial frequency content in the identification of happy and sad emotional expressions and point to the mechanisms involved in emotion identification.

The Vividness of Happiness in Dynamic Facial Displays of Emotion

“…Follow-up analyses did not reveal any other factors or interactions that compromised the conclusion that happy expressions were better identified than angry expressions. The happy advantage is consistent with previous research using static images of anger and happiness [4], as well as findings using happy and sad faces [15]. The results of Experiment 1 raise questions about the studies that claim superiority of detection of negative emotional expressions especially given the more ecological and dynamic nature of the present stimuli.…”

“…Recently we (DK & NS) have shown that the removal of low spatial frequency information significantly decreased the speed at which static happy expressions were identified [15]. In contrast, filtering out low frequency information with a High Pass Filter (HPF) benefits the detectibility of negative (sad) expressions relative to happy expressions.…”

The Vividness of the Happy Face

“…Results from our final two studies suggested that this benefit is conveyed by multiple processing routes. The first replicated findings that even unfocused images of happy faces are more discriminable (Kumar & Srinivasan, 2011). This may suggest recruitment of the magnocellular pathway, which moves low-spatial-frequency information-such as blurred images-very quickly into the system.…”

Emotion recognition (sometimes) depends on horizontal orientations

“…Indeed, prior reports suggest that not all emotion categories are equally dependent on the same spatial frequencies or orientations. Happy and sad emotion recognition appear to be supported by low (<8 cycles per face) and high (>32 cycles per face) spatial frequencies respectively (Kumar & Srinivasan, 2011). Yu, Chai, & Chung (2011) measured performance on the categorization of four facial expressions (Anger, Fear, Happiness, and Sadness) using multiple orientation filters (i.e., -60°, -30°, 0°, 30°, 60°, 90°) and concluded that horizontal information is critical for the recognition of most emotions with the exception of fear expressions.…”