Lessons from Pinocchio: Cues to deception may be highly exaggerated

Abstract

Deception researchers widely acknowledge that cues to deception -observable behaviors that may differ between truthful and deceptive messagestend to be weak. Nevertheless, several deception cues have been reported with unusually large effect sizes, and some researchers have advocated the use of such cues as tools for detecting deceit and assessing credibility in practical contexts. Examining data from empirical deception cue research and using a series of Monte Carlo simulations, I demonstrate that many estimated effect sizes of deception cues may be greatly inflated by publication bias, small numbers of estimates, and low power. Indeed, simulations indicate the informational value of the present deception literature is quite low, such that it is not possible to determine whether any given effect is real or a false positive. I warn against the hazards of relying on potentially illusory cues to deception and offer some recommendations for improving the state of the science of deception. A preprint of this document is available at https://osf.io/xt8fq/."That Marionette," continued the Talking Cricket, "is a rascal of the worst kind." (Collodi, 1883) In an effort to temper our hopes of catching lies with unerring accuracy, deception researchers often say there is no "Pinocchio's nose" (e.g., Frank, Menasco, & O'Sullivan, 2008;Hartwig & Bond, 2011;Vrij, 2006Vrij, , 2004. That is, there is no behavior that perfectly discriminates between truthful and deceptive messages. For all the talk about Pinocchio's nose, there are other more urgent lessons from the story of Pinocchio pertinent to deception research.In his adventures, Pinocchio causes mischief and mayhem, and among his many foibles is the ease with which he is tempted to do what is easy and immediately satisfying but ultimately dangerous, rather than what is difficult and responsible. * timothy.luke@psy.gu.se. For their helpful comments, suggestions, and inspiration, I am grateful to Fabi Alceste, Karl Ask, Charlie Bond, Will Crozier, Bella DePaulo, Emelie Ernberg, Pär Anders Granhag, Maria Hartwig, Lorraine Hope, Emily Joseph, Louise Jupe, Erik Mac Giolla, Patty Sanchez, Rebecca Willén, and three anonymous reviewers, as well as the many supportive others who provided encouragement and tolerated my ravings and rantings.

Speech cues to deception in bilinguals

“…One additional consideration in interpreting our results relates to the effect sizes of deception data. Effect sizes of cues to deception in the literature are known to be overestimated due to publication bias and low power (Luke, 2019). In line with this consideration, we have presented the findings for all of the acoustic measures we examined, despite many of them not differentiating truth and lies.…”

Embedding lies into truthful stories does not affect their quality

“…Moreover, good lie detectors report a higher reliance on verbal cues when making credibility judgments, while poor lie detectors tend to rely primarily on nonverbal cues (Mann, Vrij, & Bull, ). Specifically, the most consistent finding in the verbal deception literature is that truthful statements contain more details than deceptive ones (e.g., Amado, Arce, Fariña, & Vilarino, ; DePaulo et al, ; Luke, ; Oberlander et al, ). A recent meta‐analysis estimated this effect at d = 0.55 (Amado et al, ), while additional meta‐analytical findings support the usefulness of temporal, visual, and auditory details for differentiating truthful from false accounts (Masip, Sporer, Garrido, & Herrero, ).…”

Statistical Significance Filtering Overestimates Effects and Impedes Falsification: A Critique of Endsley (2019)

“…At a general level, misestimation (typically overestimation) of effects can also occur due to publication bias which is known as the file drawer problem (Rosenthal, 1979). Researchers, reviewers, and journals favor publishing papers with mostly or even all significant (i.e., typically p < .05) results, and rejecting papers with non-significant results; hence, many published effect sizes likely reflect overestimates (Kühberger et al, 2014;Luke, 2019). Publication bias is also a form of significance filtering, obscuring judgments on the practical significance of an effect and often hindering attempts to replicate and extend previous work if published effect sizes do not reflect true effect sizes.…”