Social Engineering Blogs

Over the past month, we have emphasized the expression and feeling of pain, but it is also apparent that pain and the seven basic emotions are closely intertwined.

It is specifically the connections between pain and anger that were explored in a 2008 study in the Journal of Behavioral Medicine. In that study, Dr. Jennifer Graham and a team of researchers looked at the role of anger management interventions in helping those who suffer from chronic pain. This involved an experimental analysis of over one hundred pain patients who sought to grapple with the struggles of a chronic illness.

Their primary hypothesis that anger management can contribute to both emotional and also pain-related management makes sense given the common effects of chronic illness. Those struck with such a pain condition often face the prospect of losing their job and even their social support systems. This can cause and perpetuate a feeling of anger and unfairness.

Moreover, the search for effective interventions for chronic pain is a necessary and important goal. Many people who face chronic pain struggle to find sympathy amongst medical professionals, and past research indicates a marked increase in depression and anger following the onset of pain, which is not particularly surprising.

Building off this research, Dr. Graham and her team studied a group of 102 volunteers, all of whom had recently attended a pain center and experienced pain for at least the past six months, though on average, they had felt pain for around 3 years. After a series of assessments and intake interviews, each patient was randomly assigned to either the treatment or control group.

In this context, the treatment group was asked to complete a pair of writing tasks dedicated to expressing and grappling with feelings of anger, while the control group was given more emotionally neutral writing tasks. After completion of these tasks over 2.5 weeks, the subjects were interviewed again.

These interviews were then compared to see if volunteers reported differing levels of pain, personal control, and depression. The treatment group was found to have significantly reduced levels of depression and feelings of personal control after undergoing the writing process.

While unfortunately these efforts did not reduce pain, the connection between expressions of anger and improved mental health for those involved is striking. This suggests not only a benefit from expressing one’s emotions but also helps elucidate the complex ties between feelings of depression, anger, and pain.

It is important to be aware of people’s struggles with pain, which is why Humintell has dedicated this month’s blogs to Pain Awareness Month, and the role of anger in chronic pain shouldn’t be understated. As our past blog indicated, even medical professionals are terrible at recognizing genuine from faked pain, resulting in many sufferers of chronic pain struggling at being understood.

By learning to better understand people’s emotions, be it anger, sadness, or pain, we can act more compassionately towards those in their lives who do feel chronic pain. In the meantime, check out some of the past blogs for this month here and here.

We know that humans are terrible at distinguishing real pain from faked pain, but could your smartphone just do that for you?

Building off of last week’s blog, it seems that some researchers have sought to implement the insight that computers are better at detecting genuine pain than doctors. In an exciting new study, the University of Pittsburgh’s Dr. Jeffrey Cohn sought to explore the potential of implementing machine learning techniques into an accessible software to read people’s subtle pain expressions.

Especially given current political trends and concerns as to the over prescription of opiates, doctors are under increased pressure to distinguish people who actually need medication for pain from those who are just faking in. This is helpful for doctors but also for patients as well. In fact, many patients, especially those who suffer from chronic pain, struggle to convince doctors that their pain is genuine.

If Dr. Cohn is right, however, future doctors can just pull out a smartphone and take a brief video of the patient’s face and expressions. With the app under development, a computer algorithm can match patients’ facial expressions to past video templates of people who suffer from genuine pain.

This algorithm was trained by analyzing a series of videos of people’s faces while attempting to complete manual tasks despite a shoulder injury. It tracked their winces and grimaces, creating a database of what facial expressions are most reliably caused by their feeling of pain.

Last week, we emphasized the factors that indicate deception, such as a lowered brow and raised cheeks, and Dr. Cohn’s work emphasizes that genuine pain is indicated by movement around the nose and mouth.

While these tips might help us think we can detect pain effectively, we have to recognize how fallible human efforts to detect genuine pain are, even for doctors. This fits into what we have long said about how difficult many forms of deception detection and microexpression reading are for those who are not trained in them.

However, the notion of using an app to read expressions is an exciting one, both for its practical benefits but also for the intellectual potential of driving forward our understanding of expression recognition. There is no reason why this sort of app cannot be used for other emotions.

In fact, last year we wrote about an app that used artificial intelligence to recognize our emotions. In that case, it was used to create emojis that could be sent online while still accurately representing our real facial expressions. With facial recognition technology like that, combined with a machine learning database similar to what Dr. Cohn used, the potential to use technology to enhance emotional recognition is exciting indeed.

There’s a balance, of course, in deciding how much we want to trust technology without learning these skills ourselves. While trained medical professionals may struggle at detecting genuine pain, that does not mean that training specifically in expression reading cannot help. Our time-tested and proven training programs are great evidence of your potential to learn how to do this on your own, especially because even a trained machine isn’t right all the time!

Sometimes it may be necessary to learn how to distinguish real pain expressions from deceptive ones. Not only does this aid in deception detection, but it can also help further our understanding of the pain expression, itself!

In order to address this question, a team of researchers from the University of California, San Diego, and the University of Toronto ran two experiments trying to see if observers could reliably distinguish faked pain from real pain, including one that implemented this analysis with computer learning software.

Initially, the treatment conditions had to be determined. This involved recruiting an admittedly small number of 26 participants who were asked to be recorded under three conditions. In addition to a baseline where no pain expression was solicited, they were also induced to present a genuine or faked pain expression.

This was stimulated by having each participant submerge their arm in water, either at a lukewarm or freezing cold temperature. When the water was lukewarm, they were asked to act as though it was cold and painful, while the freezing cold water reliably generated the desired expression of genuine pain. These images were evaluated first by a new set of 170 volunteers but also by an automated system which tried to analyze facial expressions without human interference.

In the first of these, the new volunteers were exposed to video clips of the genuine and fake pain expressions and asked to distinguish them. Interestingly, their success was almost entirely random, at about 52 percent accuracy. Notably, they had no prior experience making these distinctions.

While this first study indicates that people without training can’t seem to distinguish real pain from deception, they also cite previous research finding that even clinicians do not perform much better.

However, machine algorithms may make up for our human failures in this regard. Each clip was broken down into a set of discrete frames, allowing a computer to look at individual stills extracted from the videos. These were then paired with the previously discussed FACS software which codes images based on a preexisting series of templates representing typical facial expressions, in order to see if the software could also distinguish between faked and genuine pain expressions.

While the software was able to reliably distinguish with an accuracy of over 70 percent, what is more interesting is exactly what components of the facial expression were most useful in doing so. For instance, faked expressions relied on a lowered brow which was not present in genuine instances of pain. Similarly, raised cheeks and fearful brows were also reliable indicators of deception.

This study shows us a couple of pretty interesting things. First, human efforts alone in distinguishing real and fake expressions were prohibitively inaccurate, at least for untrained participants. Second, computer methods were much more accurate. This is perhaps the most interesting finding, given the potential to feed any videotaped facial expression into this software to determine whether the subject is lying.

Importantly, this blog is written as part of Pain Awareness Month. While many people, especially those with chronic illnesses, are frequently accused of faking their pain, this is not an effort to exacerbate that. Instead, it shows how unreliably doctors determine whether patients are telling the truth, often to the patients’ detriment, and it helps show the way forward to a more reliable approach.

Moreover, we have much to learn in how to accurately detect deception. There is no reason that similar approaches cannot be applied to fear or sadness, and while you might be inaccurate untrained, there is a reason we offer a robust training program in deception detection!