Deception and Deception Detection
I have been researching deception and deception detection for more than 20 years now, and have recently made some major breakthroughs, both theoretically and empirically. It is my hope that the research summarized here will truly be transformative. This page lists some of my key ideas and findings.
Truth Default Theory (TDT) • is my new theory of deception. As the name of theory implies, the key idea is that when humans communicate with other humans, we tend to operate on a default presumption that what the other person says is basically honest. The presumption of honesty is highly adaptive. It enables efficient communication, and this presumption of honesty makes sense because most communication is honest most of the time. However, the presumption of honesty makes humans vulnerable to occasional deceit. There are, of course, times and situations when people abandon this presumption of honesty, and the theory describes when people are expected to suspect a lie or conclude that a lies was told, and the conditions under which people make truth and lie judgments correctly and incorrectly. There is more on TDT here.
Truth-Bias • is the tendency to actively believe or passively presume that another person’s communication is honest independent of actual honesty. The term was originated by McCornack & Parks (1986). Empirically, truth-bias is the ratio of messages judged as honest to the total number of messages judged. In my research, truth bias is always greater than .50. Implications of truth bias include the veracity effect and the Park-Levine probability model. A citation for truth-bias and further documentation of truth-bias and its implications are available in Levine et al. (1999).
Prevalence of Deception • Lying may be less common than reported in the literature. Most people lie infrequently compared to honest communication, and the distribution of lies told is highly skewed. Most lies are told by a few prolific liars making the average lies-per-day misleading and not reflective of most people. See:
Serota, K. B., and Levine, T. R. (2014). A few prolific liars: Variation in the prevalence of lying. Journal of Language and Social Psychology.
Levine, T. R., Serota, K. B., Carey, F, & Messer, D. (2013). Teenagers lie a lot: A further investigation into the prevalence of lying. Communication Research Reports, 30, 211-220.
Probing effect • The probing effect refers to the findings that mere questioning enhances truth-bias but not accuracy. That is, a person who is questioned is more likely to be believed than an identical answer absent the questioning. It is a totally cool and counterintuitive finding that is quite robust. The catch is that the probing effect holds for the simple act of hearing someone questioned; alternatively strategic questioning can enhance accuracy.
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Levine, T. R., & McCornack, S. A. (1996a). A critical analysis of the behavioral adaptation explanation of the probing effect. Human Communication Research, 22, 575-589.
Levine, T. R., & McCornack, S. A. (1996b). Can behavioral adaption explain the probing effect? Human Communication Research, 22, 603-612.
Veracity Effect • The veracity effect refers to the empirical finding that accuracy is higher for truths than lies; that is truth accuracy > lie accuracy. In other words, the veracity of message judged affects the accuracy of judgment about the message. The veracity effect stems from truth bias and it is very robust. Two major implications of the veracity effect are that (a) averaging across truths and lies can be misleading because it obscures the reality that accuracy is different depending on the veracity of the message judged, and (b) that therefore the truth-lie base-rate is an important determinant of overall accuracy and conclusions about accuracy do not generalize across base-rates. Hee Sun Park came up with the idea behind the veracity effect as an MA student at Univ. of Hawaii.
Park-Levine Probability Model • The model predicts overall accuracy in deception detection experiments based on the truth-lie base-rate. Because people are usually truth-biased, honest messages yield higher accuracy than lies (i.e., the veracity effect), and the proportion of truths and lies affects accuracy. So long are people are truth-biased, as the proportion of messages that is honest increases, so does average detection accuracy. This relationship is linear and predicted as the accuracy for truths times the proportion of messages that are true plus the accuracy for lies times the proportion of messages that are lies.
Levine, T. R., Kim, R. K., Park, H.S., & Hughes, M. (2006). Deception detection accuracy is a predictable linear function of message veracity base-rate: A formal test of Park and Levine’s probability model.
Levine, T. R., Clare, D. D., Green, T., Serota, K. B. & Park, H. S. (2014). The effects of truth-lie base rate on interactive deception detection accuracy. Human Communication Research.
Explaining 54% accuracy • Meta-analysis shows that the across-study average accuracy is just under 54%. This is statistically better than chance, but in an absolute sense, not much better than chance. Further, findings are normally and tightly distributed around this mean. I have long wondered why this is case. Why are people only slightly better than chance? Why aren’t people better or worse? Why are the findings so stable? I now believe there are two answers: A few transparent liars and sender demeanor.
A Few Transparent Liars • The reason that accuracy in typical deception detection experiments is slightly above chance is that some small proportion of the population are really bad liars who usually give themselves away. The reason accuracy is not higher is that most people are pretty good liars and that honest demeanor is uncorrelated with actual honesty for most people.
Sender Honest Demeanor • There are large individual differences in believability. Some people come off as honest. Other people are doubted more often. These differences in how honest different people seem to be are a function of a combination of 11 different behaviors and impressions that function together. Honest demeanor has little to do with actual honesty, and this explains poor accuracy in deception detection experiments.
Levine, T. R., Serota, K. B. Shulman, H., Clare, D. D., Park, H. S., Shaw, A. S., Shim, J. C., & Lee, J. H. (2011). Sender demeanor: Individual differences in sender believability have a powerful impact on deception detection judgments. Human Communication Research, 37, 377-403.
How People Really Detect Lies • Outside the deception lab in everyday life, most lies are detected after-the-fact based on either confessions or the discovery of some evidence showing that what was said was false. Very few lies are detected in real time based only on the passive observation of sender nonverbal behavior.
Content in Context • Understanding communication requires listening to what is said and taking that in context. Knowing about the context in which the communication occurs can help detect lies.
Question Effects • Question effects involves asking the right questions to yield diagnostically useful information that improves deception detection accuracy.
Levine, T. R., Blair, J. P., & Clare, D. D. (2014). Diagnostic Utility: Experimental Demonstrations and Replications of Powerful Question Effects in High Stakes Deception Detection. Human Communication Research.
Expert Interrogation • Expertise in deception is highly context dependent and involves knowing how to prompt diagnostically useful information rather than detection by passive observation of nonverbal communication.
Research currently under review for publication.
Lie to Me Experiment •
Bogus Training Placebo Experiment •
Levine, T. R., Feeley, T., McCornack, S. A., Harms, C., & Hughes, M. (2005). Testing the effects of nonverbal training on deception detection accuracy with the inclusion of a bogus train control group. Submitted to Western Journal of Communication, 69, 203-218.
Suspicion and Deception Detection Accuracy •
Reactions to Discovered Deception •
McCornack, S. A., & Levine, T. R. (1990a). When lies are discovered: Emotional and relational outcomes of discovered deception. Communication Monographs, 57 119-138.
Levine, T. R., McCornack, S. A., & Avery, P.B. (1992). Sex differences in emotional reactions to discovered deception. Communication Quarterly, 40, 289-296.
Research on Information Manipulation Theory •
McCornack, S. A., Levine, T. R., Torres, H. I., Solowczuk, K. A., & Campbell, D. M. (1992). When the alteration of information is viewed as deception: An empirical test of information manipulation theory. Communication Monographs, 59, 17-29.
Levine, T. R. (1998). Modeling the Psychometric Properties of Information Manipulation Ratings. Communication Research Reports, 15, 218-225.
Yeung, L. N. T., Levine, T. R., & Nishiyama, K. (1999). Information Manipulation Theory and Perceptions of Deception in Hong Kong. Communication Reports, 12, 1-11.
Levine, T. R. (2001). Dichotomous and continuous views of deception: A reexamination of deception ratings in information manipulation theory. Communication Research Reports, 18, 230-240.
Levine, T. R., Asada, K. J., Massi, L. L. (2003). The relative impact of violation type and lie severity on judgments of message deceptiveness, Communication Research Reports, 20, 208-218.
Other Deception Research
Levine, T. R., Anders, L. N., Banas, J., Baum, K. L., Endo, K., Hu, A. D. S., & Wong, N. C. H. (2000). Norms, expectations, and deception: A norm violation model of veracity judgments. Communication Monographs, 67, 123-137.
Levine, T. R., Kim, R. K., & Blair, J. P. (2010). (In)accuracy at detecting true and false confessions and denials: An initial test of a projected motive model of veracity judgments. Human Communication Research, 36, 81-101.
Park, E., Levine, T. R., Harms, C., & Ferrerra, M. (2002). Group and individual accuracy in deception detection. Communication Research Reports, 19, 99-106.
Levine, T. R., Asada, K. J. K., & Park, H. S. (2006). The lying chicken and the gaze avoidant egg: Eye contact, deception, and causal order. Southern Communication Journal, 71, 401-11.
Levine, T. R., Shaw, A., & Shulman, H. (2010a). Assessing deception detection accuracy with dichotomous truth-lie judgments and continuous scaling: Are people really more accurate when honesty is scaled? Communication Research Reports, 27, 112-122.
I acknowledge the contributions of my valued co-authors Steve McCornack, Hee Sun Park, Pete Blair, Kim Serota, David Clare, and Rachel Kim. I have been fortunate to team up with exceptionally bright and insightful collaborators who are also dear friends. Funding was provided by the National Science Foundation and the US Dept. of Justice.