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Veracity Effect

The Veracity Effect refers to the observation made by Timothy R. Levine, Hee Sun Park, and Steven McCornack that deception detection accuracy depends on the veracity, that is, the honesty, of the message being evaluated. Research shows that when deception detection accuracy is scored separately for truths and lies, people tend to get truths correct more often than lies. That is, people are probabilistically more likely to be correct in presuming honesty than they are in correctly identifying a lie as a lie. As a consequence, the honesty or dishonesty of a message affects the likelihood of the correctness of a perception of honesty.

The veracity effect results from truth bias, which is the tendency to believe someone more often than not regardless of whether or not a person is actually honest. Truth bias is a very typical finding in deception detection research. Because people tend to believe others, when others are honest people tend to be correct in presuming honesty. Lies, however, are more often mistaken for the truth. This creates the veracity effect.

A coin-flipping analogy helps explain the veracity effect. If someone trying to guess the outcome of a sequence of coin flips guesses heads more often than he or she guesses tails, they are more likely to be right on those occasions when heads is obtained. When tails comes up, however, they tend to guess incorrectly. In this analogy, heads is like judging honest messages and tails is like judging lies. Because of probability and chance, truth-biased people are more likely to be right about truths than lies.

Implications of the Veracity Effect

The veracity effect is very well documented in experimental research on deception detection. In meta-analysis, people are truth biased in more than 70 percent of research studies, and on average, 57 percent of all messages are believed in deception detection experiments. Deception detention accuracy averages 54 percent in terms of correct truth-lie discrimination, but accuracy for truths averages 61 percent compared with 47 percent for lies across prior studies. The veracity effect is the label for this typical difference in truth and lie accuracy.

A primary implication of the veracity effect is that because people are statistically better than 50/50 chance at truth-lie discrimination (54 percent overall), this does not mean that they are above 50 percent at detecting lies per se. Truth-lie discrimination is an average across truths and lies. The 61 percent for truths and 47 percent for lies, when averaged, yields 54 percent (61 + 47 = 108; 108 ÷ 2 = 54). This makes average truth-lie discrimination potentially misleading. Consequently, following the discovery of the veracity effect, many researchers report accuracy for truths and lies separately in addition to reporting average accuracy.

A second important implication of the veracity effect is that the truth-lie base rate matters. The truth-lie base rate refers to the proportion of truths and lies judged in a deception detection task. Most deception detection research uses an equal number of truths and lies. Such research yields the 54 percent accuracy result mentioned previously. However, because accuracy is typically higher for truths than lies, the greater the proportion of truths judged, the higher the accuracy. Conversely, as the number of lies exceeds the number of truths, accuracy decreases.

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