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Microexpressions Differentiate Truths from Lies about Future Malicious Intent

Finally! The first scientific evidence that microexpressions are a Key to Deception Detection!

While there has been a general consensus that microexpressions play a significant role in deception detection for decades, in reality there had never been a research study published in a peer-reviewed, scientific journal that documented that claim.

Until now.

New and exciting evidence comes from Humintell’s own Drs. David Matsumoto and Hyisung Hwang in a recently published paper in Frontiers in Psychology. In their study, they sought to determine whether microexpressions could reliably indicate deception in a mock crime experiment. Ultimately, they found that microexpressions served as a helpful guide both in detecting deceit and also in evaluating future misconduct.

In actuality, previous studies did try to document the effect of microexpressions as deception indicators. But past research did not assess microexpressions effectively. An experiment was conducted featuring a mock crime. Here, participants were told to either lie or tell the truth during a simulated interview. Both the prescreening interview and the actual experiment were modeled as closely as possible on real-world law enforcement procedures.

Because past research has found that microexpressions are universal culturally, participants included both U.S. born European-Americans and Chinese immigrants. Throughout the interviews, each participant was filmed and their expressions closely analyzed.

After performing these mock interviews, facial behaviors were hand coded by experts to determine whether microexpressions were present. Emotions were then grouped as either negative, such as fear and anger, or positive, such as happiness.

It turned out that liars and truthtellers had starkly different expressions manifestations, with liars showing markedly more negative microexpressions. Not only does this help show that negative microexpressions can be used to determine deception, but the average duration of these microexpressions was relatively constant as between 0.4 and 0.5 seconds.

This study, then, not only provided the first scientific evidence that microexpressions can help detect deception, but it also helped foster further research in looking critically at what constitutes a microexpression.

And it may be a good time for you to participate and learn how to detect deception yourself!

READ THE FULL ABSTRACT AND DOWNLOAD THE FULL ARTICLE

Facial Age and Recognition

Sometimes it is very important to evaluate age based on someone’s face alone, but this can be quite tricky.

This is actually a surprisingly pressing issue as age is relevant in all sorts of commercial, social, or political contexts. While it seems intuitive that we should be able to recognize people’s ages pretty easily, this has been challenged, if not refuted, by emerging research. For instance, in a series of experiments by Dr. Colin Clifford and his team, it appears that people tend to be incredibly bad at accurately judging age.

Not only is it often awkward or inconvenient to not be able to judge someone’s age, but age also undergirds a great deal of social evaluations. Group identification, emotional evaluations, and other assorted judgments are heavily determined by our perceptions of age, as should not be surprising to most readers.

Given the importance of age evaluations in social interactions, Dr. Clifford’s team attempted to expose experimental participants to a massive database of passport photographs, tasking them with estimating the age of the person.

The experimental design was relatively simple; though employing some complicated measures to avoid sampling biases and other confounding factors. Essentially, each of the 84 participants was asked to identify the age of almost 400 participants, ranging greatly in age and across gender.

Overall, they detected certain systemically incorrect estimations across the participants. Namely, young faces tended to be seen as older, while older faces tended to be seen as younger. This was actually in line with some previous research that found that age estimations tend to skew to middle aged faces.

Importantly, perceptions of age tended to by heavily impacted by the face most recently seen. This can take the form of bias related to gender, attractiveness, or facial expression. While this research sought to control for that, Dr. Clifford did find that, without controls, such dependency would have significant impacts on evaluations.

One interesting aspect of this path dependency is the impact that previous assessments of age have. After coding multiple faces as being young, participants were more likely to gauge subsequent pictures as younger than they were. This is particularly notable given that that is the opposite of the expected and normal bias of skewing towards middle aged assessments.

Dr. Clifford’s work not only helps demonstrate the challenges of accurately gauging age but also helps shed light on how we can be influenced by seeing other faces. For instance, a bartender who is used to seeing older faces might be more likely to overestimate a younger customer’s age.

While you may not be much better at age estimation than the average participant, it is helpful to keep these considerations in mind. Knowing a person’s age can be a helpful tool in correctly reading their emotions, but it can also help us gauge whether that person is a threat in various social situations.