Deceptive behavior in doping related interviews: The case of Lance Armstrong

    loading  Checking for direct PDF access through Ovid

Abstract

Objectives

The aim of the present study is to investigate the organization of Armstrong's nonverbal behavior in deceptive statements and in those statements in which deception is not proven. The final aim of this study is to show that T-pattern methodology can be a useful tool in research about doping behavior.

Design

In this observational study we focused on Armstrong's micro-expressions (action units, gaze movements, head movements) drawing observational material from different videos excerpts where Armstrong made doping-related statements. A baseline of Armstrong's deceptive behavior was established by selecting three video samples from 2005 in which he fully denied ever having taken performance-enhancing drugs. They were compared to the interview conducted by Oprah Winfrey in January 2013, in which he admitted doping but denied the specific charges of bullying and corruption.

Method

Our approach is based on the detection of statistically significant hierarchical sequences of behaviors in time, called T-patterns (temporal patterns). The algorithm, implemented in Theme software, determines whether apparently arbitrary events sequentially repeat, within a specified time interval, at a rate greater than that expected by chance.

Results

Data analyses allowed identifying distinctive patterns for each of the two conditions. The baseline showed a very limited number of patterns, highlighting low level of complexity and the presence of stereotyped behaviors. In the Oprah video samples, the number and complexity of distinctive patterns was significantly higher, and most of them included gaze shifting behaviors.

Conclusions

T-pattern methodology might be an effective strategy to detect nonverbal features of deception, integrated with more traditional and established practice, in order to improve anti-doping measures and fight this spreading phenomenon.

Related Topics

    loading  Loading Related Articles