Lying is an activity that every human being practices daily. We could say deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, we can still affirm there has been no such finding as a Pinocchio’s nose. (Vrij, 2008). This work proposes a new approach to deception detection, based on finding significant differences between liars and truth tellers through the analysis of their behavior. This is based on the combination of three factors: cognitive load manipulation, multimodal data collection, and t-pattern analysis. The first one is a technique that has already been used in previous studies on deception (Vrij, Fisher, Mann, & Leal, 2008) to enlarge the differences between lie and truth. The second factor, multimodal approach, has been acknowledged in literature about deception detection and on several studies concerning the understanding of any communicative phenomenon. We believe a methodology such as T-pattern analysis (third factor) could be able to get the best advantages from an approach that combines data coming from multiple signaling systems. In fact, T-pattern analysis is a new methodology for the analysis of behavior (Magnusson, 2000; 2005) that unveil the complex structure at the basis of the organization of human behavior. We conducted two experimental studies. Results showed how T-pattern analysis allowed to find significant differences between truth telling and lying even without operating any kind of cognitive load manipulation. In terms of general impact and anticipated benefits, this project aims at making progress in the state of knowledge about deception detection, with the final goal to propose a useful tool for the improvement of public security and well-being.

(2014). A cognitive approach to deception detection: multimodal recognition of prepared lies. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2014).

A cognitive approach to deception detection: multimodal recognition of prepared lies

DIANA, BARBARA
2014-09-10

Abstract

Lying is an activity that every human being practices daily. We could say deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, we can still affirm there has been no such finding as a Pinocchio’s nose. (Vrij, 2008). This work proposes a new approach to deception detection, based on finding significant differences between liars and truth tellers through the analysis of their behavior. This is based on the combination of three factors: cognitive load manipulation, multimodal data collection, and t-pattern analysis. The first one is a technique that has already been used in previous studies on deception (Vrij, Fisher, Mann, & Leal, 2008) to enlarge the differences between lie and truth. The second factor, multimodal approach, has been acknowledged in literature about deception detection and on several studies concerning the understanding of any communicative phenomenon. We believe a methodology such as T-pattern analysis (third factor) could be able to get the best advantages from an approach that combines data coming from multiple signaling systems. In fact, T-pattern analysis is a new methodology for the analysis of behavior (Magnusson, 2000; 2005) that unveil the complex structure at the basis of the organization of human behavior. We conducted two experimental studies. Results showed how T-pattern analysis allowed to find significant differences between truth telling and lying even without operating any kind of cognitive load manipulation. In terms of general impact and anticipated benefits, this project aims at making progress in the state of knowledge about deception detection, with the final goal to propose a useful tool for the improvement of public security and well-being.
ZURLONI, VALENTINO
Deception; Lie detection; T-pattern; Cognitive load; THEME
M-PSI/01 - PSICOLOGIA GENERALE
English
Scuola di Dottorato in Scienze Umane
SCIENZE DELLA FORMAZIONE E DELLA COMUNICAZIONE - 47R
26
2012/2013
(2014). A cognitive approach to deception detection: multimodal recognition of prepared lies. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2014).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/53250
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