Modeling Joint Improvisation between Human and Virtual Players in the Mirror Game

Joint improvisation is observed to emerge spontaneously among humans performing joint action tasks, and has been associated with high levels of movement synchrony and enhanced sense of social bonding. Exploring the underlying cognitive and neural mechanisms behind the emergence of joint improvisation is an open research challenge. This paper investigates the emergence of jointly improvised movements between two participants in the mirror game, a paradigmatic joint task example. A theoretical model based on observations and analysis of experimental data is proposed to capture the main features of their interaction. A set of experiments is carried out to test and validate the model ability to reproduce the experimental observations. Then, the model is used to drive a computer avatar able to improvise joint motion with a human participant in real time. Finally, a convergence analysis of the proposed model is carried out to confirm its ability to reproduce the emergence of joint movement between the participants

Kinematic characteristics of motion in the mirror game

We present the analysis of data collected in the mirror game setting. In our set-up two players are asked to mirror each other movements (with or without a designated leader). First, we study kinematic characteristics of motion of individual players, and next we investigate how they are affected by interactions between the players. Results of the presented analysis will be used to inform the design of interactive virtual players with kinematics based on the similarity principle.Engineering and Physical Sciences Research CouncilEuropean Project AlterEgo FP7 ICT 2.9 - Cognitive Sciences and Robotic

Unravelling socio-motor biomarkers in schizophrenia

We present novel, low-cost and non-invasive potential diagnostic biomarkers of schizophrenia. They are based on the ‘mirror-game’, a coordination task in which two partners are asked to mimic each other’s hand movements. In particular, we use the patient’s solo movement, recorded in the absence of a partner, and motion recorded during interaction with an artificial agent, a computer avatar or a humanoid robot. In order to discriminate between the patients and controls, we employ statistical learning techniques, which we apply to nonverbal synchrony and neuromotor features derived from the participants’ movement data. The proposed classifier has 93% accuracy and 100% specificity. Our results provide evidence that statistical learning techniques, nonverbal movement coordination and neuromotor characteristics could form the foundation of decision support tools aiding clinicians in cases of diagnostic uncertainty

Effects of Facial Emotions on Social-motor Coordination in Schizophrenia

Schizophrenia patients are known to be impaired in their ability to process social information and to engage in social interactions. To understand better social cognition in schizophrenia, we investigate the links between these impairments. In this paper, we focus primarily on the influence of social feedback, such as facial emotions, on motor coordination during joint action. To investigate and quantify this influence, we exploited systematically-controlled social and nonsocial feedback provided by a humanoid robot. Humanoid robotics technology offers interactive designs and can precisely control the properties of the feedback provided during the interaction. In this work, a joint-action task with a robot is performed to investigate how social cognition is affected by cognitive capabilities and symptomatology. Results show that positive social feedback has a facilitatory effect on social-motor coordination in the control participants compared to nonsocial positive feedback. This facilitation effect is not present in schizophrenia patients, whose social-motor coordination is similar in social and nonsocial feedback conditions. This result is strongly correlated with performances in the Trail Making Test (TMT), which highlights the link between cognitive deficits and social-motor coordination in schizophrenia

ELECTRE TRI 2.0a : Methodological guide and user's documentation

ou

Learning value functions in interactive evolutionary multiobjective optimization

This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that attempts to learn a value function capturing the users’ true preferences. At regular intervals, the user is asked to rank a single pair of solutions. This information is used to update the algorithm’s internal valuefunction model, and the model is used in subsequent generationsto rank solutions incomparable according to dominance. This speeds up evolution toward the region of the Pareto front that is most desirable to the user. We take into account the most general additive value function as a preference model and we empirically compare different ways to identify the value function that seems to be the most representative with respect to the given preference information, different types of user preferences, and different ways to use the learned value function in the MOEA. Results on a number of different scenarios suggest that the proposed algorithm works well over a range of benchmark problems and types of user preferences

Interactive evolutionary multiobjective optimization using robust ordinal regression

This paper proposes the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), a combination of an evolutionary multiobjective optimization method, NSGA-II, and an interactive multiobjective optimization method, GRIP. In the course of NEMO, the decision maker is able to introduce preference information in a holistic way, by simply comparing some pairs of solutions and specifying which solution is preferred, or comparing intensities of preferences between pairs of solutions. From this information, the set of all compatible value functions is derived using GRIP, and a properly modified version of NSGA-II is then used to search for a representative set of all Pareto-optimal solutions compatible with this set of derived value functions. As we show, this allows to focus the search on the region most preferred by the decision maker, and thereby speeds up convergence