Your Friends Can Tell When You Fake Laugh at Their Jokes

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While your poker face may be good at fooling others, your poker laugh probably doesn't hold up under scrutiny, according to two recent studies.

study in the Evolution and Human Behaviorhighlighted by Time, looked at people's ability to discern real laughter from fake laughs. In the experiment, UCLA associate professor of communications Greg Bryant compiled 18 spontaneous laughs from a genuine conversation between college roommates. Bryant then asked other co-eds to laugh on command, and pulled 18 of these laughs. He then presented audio recordings of all of these guffaws and chuckles to participants to test their fake laugh signals. They correctly identified the fake laughs as fake 63 percent of the time. That number signals that your fake laugh works less often than not.

"Quite a few fake laughs sound pretty good, but listeners seem to pay attention to certain acoustic features that are really hard to fake," said Bryant, who jokingly calls himself the "Laughter Guy."

While that experiment showed humans are decent at rooting out the phonies, a study that was broken down in The New York Times last week wasn't as approving of human lie-detecting. In that experiment, researchers plunged participants' arms in ice water and recorded their facial expressions of pain. Then, they dipped those same participants arms in warm water and asked them to fake an expression of pain. Human observers of the two videos couldn't tell the difference any better than a random guess (i.e. 50 percent.) "We have a fair amount of evidence to show that humans are paying attention to the wrong cues," lead study author Marian Bartlett told the Times.

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Even after extensive training and feedback on which grimaces were fake and which were real, humans only improved to about 55 percent accuracy. Take the test yourself and you'll see: it's really difficult.

But not so for robots. The researchers from UC-San Diego wrote software that was able to detect these fake pain expressions with 85 percent accuracy. The software was able to identify minute muscular differences in facial expressions of pain; the kind that went undetected by human observation. The Times explains some of those differences.

When the person was experiencing real pain, for instance, the length of time the mouth was open varied; when the person faked pain, the time the mouth opened was regular and consistent. Other combinations of muscle movements were the furrowing between eyebrows, the tightening of the orbital muscles around the eyes, and the deepening of the furrows on either side of the nose.

Not that knowing this information will help all that much. Most of these movements were so swift they were missed by the human eye, the Times notes. Our ears are less easily fooled.

This article is from the archive of our partner The Wire.