How Relying on a Single Cue Helps to Detect Lies: A Student Author Perspective

Humans are poor at detecting lies. Previous studies had shown that humans were only 54% accurate in detecting lies (Bond & DePaulo, 2006). This is just like flipping a coin to decide on lie detection.
How Relying on a Single Cue Helps to Detect Lies: A Student Author Perspective

Considering the practical utility of detecting lies in the legal field, there is the development of several training programs that train security personnel on several cues as many as 92 cues (Weinberger, 2010). The success of such training is still questionable  (Hauch et al., 2016). What if the secret to detecting lies does not lie in adding more cues to the existing ones, but rather, in reducing them? Like a jigsaw puzzle, different students worked on different aspects of the current project trying to piece together the individual parts to form a single picture. Their mission? To answer one of the most pressing questions in the field of deception detection: How can people reliably tell lie from truth? Specifically, does relying on just one cue help to accurately detect lies? The heuristic approach to lie detection stipulates that, by relying on just the best cues, one makes truth-lie judgments better than relying on many cues. Inspired by the work of others (Gigerenzer & Todd, 1999; Luke, 2018; Street & Richardson, 2015) the Lie Lab of the University of Amsterdam together with researchers from Maastricht University and Tilburg University set out to show that relying on a single cue could be a promising way to detect deception.

Verschuere et al. (2023) started as a bachelor thesis project by Chu-Chien Lin and Sara Huismann. We showed that by using a single diagnostic cue (i.e. detailedness or verifiability), one can detect deception far above chance. This effect proved to be stable across student versus non-student participants, Dutch versus German speakers, and written statements versus interview transcripts (Studies 1 to 4). The benefit of this is that, rather than spending time training judges on the elaborate coding scheme, this approach requires a simple rating scale and saves time. As Chu-Chien, one of the student authors noted, “…this piece of research was a unique hands-on opportunity to contribute to every step of the research process and an invaluable experience for my professional development. The steep learning curve of critically appraising the current methods of lie detection and testing a simpler yet more robust approach allowed me to put my understanding from lectures into practice and to the test.” Leonie also highlighted that, as illustrated in our science comic (see Figure 1), “science does not always have to be about complex models and complexity does not always mean we understand human functioning and psychological processes better but sometimes investigating simple heuristics makes more sense because they are even more useful to use in everyday life.”

In study 5 we found that it did not matter whether participants knew their rating of cues was used for lie detection or not, the heuristic was still effective.

Having firmly established the basic phenomenon, we dared to explore initial steps toward real-life applications. We did this in another bachelor thesis project (Study 6), where we found that interviewers were able to distinguish between lie and true statements by relying on a single cue making an overall accurate judgment of 79%. According to Leonie who worked on the project “ We set up the lab experiment in a way that we aimed to make it as realistic. We send our participants around campus to do a task, and later some of them were instructed to lie. As an experimenter, we did not know who was lying as well but we obviously knew about the heuristic. Thus, it was fun for us (students) to guess amongst ourselves who was lying and who was not.”.

Study 7 found that it is not just about using any single cue but using a single diagnostic cue that helps in distinguishing statements. Obed explained that “I searched through books and meta-analyses to identify the diagnostic and less diagnostic cues in the deception literature. We had to build consensus about the definition of the diagnosticity of a cue and devise objective ways of measuring certain variables (such as using text analysis software).

Finally, Studies 8 and 9 showed that simple cues performed better than using multiple cues.

            There are a lot of useful lessons that can be taken from this study. For Thierry van Goor his interest in the subject understudy “was beyond educational. Most importantly, I would have never believed I would have even the smallest (emphasis on small) part in an actual publication.  I find it incredible, and I am incredibly proud. For me, this thesis really taught me what it means to conduct your research project. Others can learn what passion and knowledge can really achieve.” For Obed “research is not just about searching for literature and collecting data to test hypotheses but it needs a strong team to critically come into agreement and produce quality work.” Lastly Chu-Chien noted that “the importance of remaining critical of the extant literature and the (aggregated) value of individual items within measures used in practice. As such, I would highly encourage others to continue challenging deception research given the novelty of the theories in the field.”

Science comic illustrating how simple rules of thumb can facilitate deception detection.

Figure 1. Science comic illustrating how simple rules of thumb can facilitate deception detection. By Jan Cleijne (art) and Bruno Verschuere (text).

What is the way forward?

We believe that the heuristic approach sets a new phase of deception detection. Researchers and practitioners can also begin to explore the approach in real-life situations to help see its usefulness. There is now a growing interest in using AI for lie detection. However, the authors note that the heuristic presents some advantages over the use of AI because current AI methods often lack the transparency in how lie-truth statements are classified and that is needed in real-life decision-making. It will also be important for researchers to compare whether the heuristic approach will outperform AI in terms of accuracy in the classification of lie and truth statements. With all these said, a note of caution is warranted. Considering the high error rate of the approach, it requires careful consideration of how the heuristic approach can help practitioners make better judgements. With so many remaining questions, we hope other students too will enjoy studying how and when simple rules of thumb can help to find the truth.


Bond, C. F., & DePaulo, B. M. (2006). Accuracy of Deception Judgments. Personality and Social Psychology Review, 10(3), 214–234.

Gigerenzer, G., & Todd, P. M. (1999). Simple heuristics that make us smart. Oxford University Press, USA.

Hauch, V., Sporer, S. L., Michael, S. W., & Meissner, C. A. (2016). Does training improve the detection of deception? A meta-analysis. Communication Research, 43(3), 283–343.

Luke, T. J. (2018). Lessons from Pinocchio: Cues to deception may be highly exaggerated [Preprint]. Open Science Framework.

Street, C. N., & Richardson, D. C. (2015). The focal account: Indirect lie detection need not access unconscious, implicit knowledge. Journal of Experimental Psychology: Applied, 21(4), 342.

Verschuere, B., Lin, C.-C., Huismann, S., Kleinberg, B., Willemse, M., Mei, E. C. J., Goor, T. van, Löwy, L. H. S., Appiah, O. K., & Meijer, E. (2023). The use-the-best heuristic facilitates deception detection. Nature Human Behaviour.

Weinberger, S. (2010). Airport security: Intent to deceive? Nature, 465(7297), 412–416.

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