Efficient information for recognizing pain in facial expressions

The face as a visual stimulus is a reliable source of information for judging the pain experienced by others. Until now, most studies investigating the facial expression of pain have used a descriptive method (i.e. Facial Action Coding System). However, the facial features that are relevant for the observer in the identification of the expression of pain remain largely unknown despite the strong medical impact that misjudging pain can have on patients' well-being.

Here, we investigated this question by applying the Bubbles method. Fifty healthy volunteers were asked to categorize facial expressions (the six basic emotions, pain and neutrality) displayed in stimuli obtained from a previously validated set and presented for 500 ms each. To determine the critical areas of the face used in this categorization task, the faces were partly masked based on random sampling of regions of the stimuli at different spatial frequency ranges.

Results show that accurate pain discrimination relies mostly on the frown lines and the mouth. Finally, an ideal observer analysis indicated that the use of the frown lines in human observers could not be attributed to the objective 'informativeness' of this area.

Based on a recent study suggesting that this area codes for the affective dimension of pain, we propose that the visual system has evolved to focus primarily on the facial cues that signal the aversiveness of pain, consistent with the social role of facial expressions in the communication of potential threats.