Sébastien Brault

Balancing Deceit and Disguise: How to successfully fool the defender in a 1 vs. 1 situation in rugby

Suddenly changing direction requires a whole body reorientation strategy. In sporting duels such as an attacker vs. a defender in rugby, successful body orientation/reorientation strategies are essential for successful performance. The aim of this study is to examine which biomechanical factors, while taking into account biomechanical constraints, are used by an attacker in a 1 vs. 1 duel in rugby. More specifically we wanted to examine how an attacker tries to deceive the defender yet disguise his intentions by comparing effective deceptive movements (DM(+)), ineffective deceptive movements (DM(-)), and non-deceptive movements (NDM). Eight French amateur expert rugby union players were asked to perform DMs and NDMs in a real 1 vs. 1 duel. For each type of movement (DM(+), DM(-), NDM) different relevant orientation/reorientation parameters, medio-lateral displacement of the center of mass (COM), foot, head, upper trunk, and lower trunk yaw; and upper trunk roll were analyzed and compared. Results showed that COM displacement and lower trunk yaw were minimized during DMs while foot displacement along with head and upper trunk yaw were exaggerated during DMs (DM(+) and DM(-)). This would suggest that the player is using exaggerated body-related information to consciously deceive the defender into thinking he will run in a given direction while minimizing other postural control parameters to disguise a sudden change in posture necessary to modify final running direction. Further analysis of the efficacy of deceptive movements showed how the disguise and deceit strategies needed to be carefully balanced to successfully fool the defender.

Detecting deception in movement: the case of the side-step in rugby

Although coordinated patterns of body movement can be used to communicate action intention, they can also be used to deceive. Often known as deceptive movements, these unpredictable patterns of body movement can give a competitive advantage to an attacker when trying to outwit a defender. In this particular study, we immersed novice and expert rugby players in an interactive virtual rugby environment to understand how the dynamics of deceptive body movement influence a defending player’s decisions about how and when to act. When asked to judge final running direction, expert players who were found to tune into prospective tau-based information specified in the dynamics of ‘honest’ movement signals (Centre of Mass), performed significantly better than novices who tuned into the dynamics of ‘deceptive’ movement signals (upper trunk yaw and out-foot placement) (p<.001). These findings were further corroborated in a second experiment where players were able to move as if to intercept or ‘tackle’ the virtual attacker. An analysis of action responses showed that experts waited significantly longer before initiating movement (p<.001). By waiting longer and picking up more information that would inform about future running direction these experts made significantly fewer errors (p<.05). In this paper we not only present a mathematical model that describes how deception in body-based movement is detected, but we also show how perceptual expertise is manifested in action expertise. We conclude that being able to tune into the ‘honest’ information specifying true running action intention gives a strong competitive advantage.

Computational Deception and Noncooperation

Many applications require knowledge about how to deceive, including those related to safety, security, and warfare. Speech and text analysis can help detect deception, as can cameras, microphones, physiological sensors, and intelligent software. Models of deception and noncooperation can make a virtual or mixed-reality training environment more realistic, improve immersion, and thus make it more suitable for training military or security personnel. Robots might need to operate in physical and nontraining environments where they must perform military activity, including misleading the enemy. The contributions to this installment of Trends &amp;#x0026; Controversies present state-of-the-art research approaches to the analysis and generation of noncooperative and deceptive behavior in virtual humans, agents, and robots; the analysis of multiparty interaction in the context of deceptive behavior; and methods to detect misleading information in texts and computer-mediated communication. Articles include: &#x0022;Computational Deception and Noncooperation,&#x0022; by Anton Nijholt; &#x0022;Robots that Need to Mislead: Biologically-Inspired Machine Deception,&#x0022; by Ronald C. Arkin; &#x0022;Deception in Sports Using Immersive Environments,&#x0022; by S&amp;#x00E9;bastien Brault, Richard Kulpa, Franck Multon, and Benoit Bideau; &#x0022;Non-Cooperative and Deceptive Virtual Agents,&#x0022; by David Traum; &#x0022;Deception Detection in Multiparty Contexts,&#x0022;by Hayley Hung; &#x0022;Deception Detection, Human Reasoning, and Deception Intent,&#x0022; by Eugene Santos Jr., Deqing Li, and Fei Yu; and &#x0022;Automatic Deception Detection in Computer-Mediated Communication,&#x0022; by Lina Zhou and Dongsong Zhang.We discuss the importance of modelling deceptive and noncooperative behavior in computer sytems such as intelligent agents, robots and serious game environments for traing and simulation. This short survey is an introduction to the next six contributions to this installment of Trends & Controversies, we find state-of-the-art research approaches to the analysis and generation of noncooperative and deceptive behavior in virtual humans, agents, and robots; the analysis of multiparty interaction in the context of deceptive behavior; and methods to detect misleading information in texts and computer-mediated communication.

Displacements in Virtual Reality for Sports Performance Analysis

In real situations, analyzing the contribution of different parameters on sports performance is a difficult task. In a duel for example, an athlete needs to anticipate his opponent’s actions to win. To evaluate the relationship between perception and action in such a duel, the parameters used to anticipate the opponent’s action must then be determined. Only a fully standardized and controllable environment such as virtual reality can allow this analysis. Nevertheless, movement is inherent in sports and only a system providing a complete freedom of movements of the immersed subject (including displacements) would allow the study of the link between visual information uptake and action, that is related to performance. Two case studies are described to illustrate such use of virtual reality to better understand sports performance. Finally, we discuss how the introduction of new displacement devices can extend the range of applications in sports.