Telling the difference between deceiving and truth telling: An experiment in a public space

The behavioral experiment presented in this paper investigated deception tasks (both concealment and lying) undertaken in a public space. The degree of risk of deception detection and the demands of self-regulation when deceiving were manipulated. The results showed a significant interaction effect between veracity and risk of deception detection, emerged for the body movement of “hand(s) in pocket(s)”. The incidence of “hand(s) in pocket(s)” was found to increase from truth telling to deceiving conditions when the risk of deception detection was higher, and to decrease from truth telling to deceiving conditions when the risk was lower. Higher risk of deception detection was also found in magnifying the “overall negative and controlled impression” displayed by both deceivers and truth tellers, compared to the lower risk of detection condition. We also discussed the possible effects of risk of deception detection and depletion of self-regulation, on deception behavior. Further studies and the connection between this study and the research community of computer vision and multimodel interaction is also discussed

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe