Advancing Lie Detection by Inducing Cognitive Load on Liars: A Review of Relevant Theories and Techniques Guided by Lessons from Polygraph-Based Approaches

Jeffrey J. Walczyk, Frank P. Igou, Alexa P. Dixon, Talar Tcholakian

This article critically reviews techniques and theories relevant to the emerging field of "lie detection by inducing cognitive load selectively on liars." To help these techniques benefit from past mistakes, we start with a summary of the polygraph-based Controlled Question Technique (CQT) and the major criticisms of it made by the National Research Council (2003), including that it not based on a validated theory and administration procedures have not been standardized. Lessons from the more successful Guilty Knowledge Test are also considered. The critical review that follows starts with the presentation of models and theories offering insights for cognitive lie detection that can undergird theoretically load-inducing approaches. This is followed by evaluation of specific research-based, load-inducing proposals, especially for their susceptibility to rehearsal and other countermeasures. To help organize these proposals and suggest new direction for innovation and refinement, a theoretical taxonomy is presented based on the type of cognitive load induced in examinees (intrinsic or extraneous) and how open-ended the responses to test items are. Finally, four recommendations are proffered that can help researchers and practitioners to avert the corresponding mistakes with the CQT and yield new, valid cognitive lie detection technologies.