T-Pattern Analysis and Cognitive Load Manipulation to Detect Low-Stake Lies: An Exploratory Study

Abstract

Deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, there has been no definite finding of a univocally “deceptive” signal. This work proposes an approach to deception detection combining cognitive load manipulation and T-pattern methodology with the objective of: (a) testing the efficacy of dual task-procedure in enhancing differences between truth tellers and liars in a low-stakes situation; (b) exploring the efficacy of T-pattern methodology in discriminating truthful reports from deceitful ones in a low-stakes situation; (c) setting the experimental design and procedure for following research. We manipulated cognitive load to enhance differences between truth tellers and liars, because of the low-stakes lies involved in our experiment. We conducted an experimental study with a convenience sample of 40 students. We carried out a first analysis on the behaviors’ frequencies coded through the observation software, using SPSS (22). The aim was to describe shape and characteristics of behavior’s distributions and explore differences between groups. Datasets were then analyzed with Theme 6.0 software which detects repeated patterns (T-patterns) of coded events (non-verbal behaviors) that regularly or irregularly occur within a period of observation. A descriptive analysis on T-pattern frequencies was carried out to explore differences between groups. An in-depth analysis on more complex patterns was performed to get qualitative information on the behavior structure expressed by the participants. Results show that the dual-task procedure enhances differences observed between liars and truth tellers with T-pattern methodology; moreover, T-pattern detection reveals a higher variety and complexity of behavior in truth tellers than in liars. These findings support the combination of cognitive load manipulation and T-pattern methodology for deception detection in low-stakes situations, suggesting the testing of directional hypothesis on a larger probabilistic sample of populationThe authors are gratefully acknowledge the support of two Spanish government projects (Ministerio de Economía y Competitividad): (1) La Actividad Física y el Deporte Como Potenciadores del Estilo de Vida Saludable: Evaluación del Comportamiento Deportivo Desde Metodologías No Intrusivas [Grant No. DEP2015-66069-P, MINECO/FEDER, UE]; (2) Avances Metodológicos y Tecnológicos en el Estudio Observacional del Comportamiento Deportivo [Grant No. PSI2015-71947-REDP, MINECO/FEDER, UE]. In addition, they thank the support of the Generalitat de Catalunya Research Group, GRUP DE RECERCA I INNOVACIÓ EN DISSENYS (GRID). Tecnología i Aplicació Multimedia i Digital als Dissenys Observacionals [Grant No. 2017 SGR 1405]. Lastly, MTA also acknowledge the support of University of Barcelona (Vice-Chancellorship of Doctorate and Research Promotion).Peer Reviewe