Toward explaining face perception of pain: an Artificial Neural Network Fuzzy Case-Based Reasoning Approach

Manual Facial Action Coding studies (FACS) have discovered a fuzzy facial expression that is both specific and sensitive to pain. However, the limitations of manual pain coding sit uneasily beside the increasingly higher standards required of medical care. These limitations include training time and effort, technological requirements and human subjective factors. To surmount these challenges, in the last decade and a half, devices embedded with artificial neural networks (ANNs) have been used in researching pain through facial expression (‘face perception of pain’). Using neural-network theory, this chapter argues that ANN approaches to face perception of pain be viewed as the problem of using and acquiring representational pain-face spaces. In place of categorical definitions and application rules invoking them, face perception of pain is plausibly organized around ‘fuzzy’ cases such that human observers, like ANNs, judge a pain face based on their recognition that one face is more or less similar to other faces whose results are remembered and assessed (‘fuzzy case based reasoning’). A study conducted by one of the authors implementing a fuzzy case-based reasoning system integrated with an ANN (FCBR-ANN) produced more than 90% accuracy in pain perception. Face perception of pain using an FCBR-ANN may be a real-time alternative to manual coding of pain by human observers, and may prove clinically useful.