>>Deception detection is the ability to discriminate lies AND truths. If you think about it just
guessing is 50% accuracy (or chance accuracy). The veracity effect says that truth accuracy
is typically higher than lie accuracy. That's because of something called the truth bias,
which means we typically expect truth. And so when we're just guessing we have 50/50
chance but we guess truth more than lies. But there are four possible decisions that
could be made: žTruth could be seen as truth— which would
be truth accuracy. A lie could be seen as a lie—which is lie
accuracy. žA lie could be seen as the truth—which
is a false positive. It means the liar passes the test but they're getting away with a lie.
Or a truth could be seen as a lie—that's false negative. It means truth-tellers are
wrongly accused. Some people call it the Othello error from the Shakespearean character who
thought everybody was lying.
So, how good are we at detecting deception? The best way to understand accuracy rates
is to look at well-conducted meta-analyses. What's a meta-analysis? It's a study of existing
data that combines results from a variety of studies and looks for a patterns of results
across multiple studies. There have been hundreds of studies on human detection accuracy done
by scholars over the past 40 years. Some show very high rates of deception accuracy, up
to about 90%, but some show very low rates, around 30%. A meta-analysis can look for overall
patterns and different factors that might account for the variations. In deception,
the biggest factor that accounts for the variation is sample size. When a large sample is used,
the results are closer to what the overall pattern of results is than when a small sample
is used. Bigger samples make your data more reliable.
There are several meta-analyses out there but the best known one was done by Bond and
DePaulo in 2006. They looked at the deception accuracy reported in 206 studies and found
the average detection accuracy is 54%. Think about that. You could flip a coin and be right
50% of the time, so it means we are performing only 4% better than chance. These samples
are generally people who have no special training on detecting deception, but we'll look at
the accuracy rates of professionals in a moment. The Bond & DePaulo study also revealed a pretty
clear truth bias: 47% of lies were correctly detected while 62% of truths were correctly
detected. If people are guessing, they are more likely to guess truth than lie.
Professional samples don't do much better. A review by Vrij and Mann in 2005 across 14
different studies of police officers, judges, intelligence analysts, and other experts shows
th- showed that their accuracy ranged from 40% to 73% and that in several of the studies,
they had a higher lie accuracy rate than their truth accuracy rate, which suggests they might
have a lie bias. If you think about the goals of a police investigation, they are trying
to determine who committed a crime after it occurred. It's very important that they correctly
judge lies from truths and not arrest or convict the wrong person. As the English jurist Blackstone
once said, it's better for ten guilty persons to escape than one innocent suffer. This is
the basis for the presumption of innocence in our legal system. It would favor the truth
bias and the false positive error. However, if you are trying to prevent a future bad
act like a terrorist attack, the intelligence community would favor a lie bias, that it
is preferable to falsely accuse an innocent person than allow ten terrorists to carry
out their plots. I want you to think about that. Which do you think are more harmful?
False positives or false negatives, and why?
If we're so bad at deception detection, is there any hope for us? Can we be trained to
see the truths and lies correctly? Training thus far has had pretty pitiful results. Training
programs have been developed for law enforcement and other important groups by a variety of
sources but have only been moderately successful and the long-term effects of the training
are questionable, at best. Vrij reports in his summary of 18 different training studies
that the average detection accuracy improves less than 5% after training from 53.4% for
untrained samples to 57.66% for trained samples. Now remember, 50% is chance, so you could
get that just by flipping a coin. In a clever study comparing a valid training program to
a bogus training that gave people no valuable information at all, Tim levine and his colleagues
increased accuracy the same amount, no more than about 5% in either one over people who
got no training at all. The bogus training probably did the same thing the valid training
did; it made people more vigilant and careful about observing behavior and that's about
it. Further, Driskell reports in his recent meta-analysis of 16 studies testing the effects
of training on nearly 3000 trainees that training can have a moderate effect overall but that
the type of training can affect the results. Training that combines information about the
correct relevant cues (such as tension, the logic of the answers, and vocal fluency) with
an ability to practice their skills, and with feedback on accuracy was the most effective.
Driskell's meta-analysis also found that training was more effective for naïve trainees than
people with training experience in deception detection. In fact, he says at least one study
suggests that training may make police officers worse after the training than before. Driskell
speculates that ineffective stereotypes have been taught to professionals and make it more
difficult to undo their misconceptions than the vague misperceptions of laypeople. Several
studies support this idea, suggesting that experienced professionals are more confident
in their judgments than laypeople but are no more accurate. The troubling effect of
this fact is that professionals might not believe they need further training. Although
few studies have examined the impact of training on long-term changes in the behavior of professionals,
these results suggest that without repeated training, police officers and others like
them may simply fall back into their old bad habits relatively quickly. In order to be
effective, training must be scientifically credible, must engage the trainees, and must
be ongoing for long periods of time to avoid the pitfalls of other training systems. In
this module we've talked about the accuracy rates of deception detection and we've discovered
that on average we're no more than 54% accurate when trying to distinguish truths from lies.
Can training make us more accurate? It can but it needs to be good training based on
scientifically validated studies and that gives good feedback to the people being trained.
There's hope for us, but we still have more to learn.