As robots become more omnipresent in our society, we are facing the challenge of making them more socially competent. However, in order to safely and meaningfully cooperate with humans, robots must be able to interact in ways that humans find intuitive and understandable. Addressing this challenge, we propose a novel approach for understanding and modelling social behaviour and implementing social coupling in robots. Our approach presents a radical departure from the classical view of social cognition as mind-reading, mentalising or maintaining internal rep-resentations of other agents. This project is based on the view that even complex modes of social interaction are grounded in basic sensorimotor interaction patterns. SensoriMotor Contingencies (SMCs) are known to be highly relevant in cognition. Our key hypothesis is that learning and mastery of action-effect contingencies are also critical to enable effective coupling of agents in social contexts. We use “socSMCs” as a shorthand for such socially rele-vant action-effect contingencies. We will investigate socSMCs in human-human and human-robot social interaction scenarios. The main objectives of the project are to elaborate and investigate the concept of socSMCs in terms of information-theoretic and neurocomputational models, to deploy them in the control of humanoid robots (PR2, REEM-C) for social entrainment with humans, to elucidate the mechanisms for sustaining and exercising socSMCs in the human brain, to study their breakdown in patients with autism spectrum disorders, and to benchmark the socSMCs approach in several demonstrator scenarios. Our long term vision is to realize a new socially competent robot technology grounded in novel insights into mechanisms of functional and dysfunctional social behavior, and to test novel aspects and strategies for human-robot interaction and cooperation that can be applied in a multitude of assistive roles relying on highly compact computational solutions.

The human visual brain can learn and adapt to change, which solves many of the problems posed by an ever-changing visual environment. However, to maintain a consistent overall representation of the visual world, the brain also has to retain previously acquired neuronal mechanisms. The key is to strike a balance between plasticity and stability. Increasing our knowledge about the stability and plasticity of the visual brain has tremendous potential for innovation in health care and high-tech industry: 1) rehabilitation, treatments and detection of disease can be developed and refined based on knowing how the brain changes as a result of visual loss or neural dysfunction; 2) it can inspire the development and implementation of artificial intelligence, such as adaptive automated vision systems. However, our present knowledge of the adaptive capacity of the human brain is incomplete and largely qualitative in nature. This limits translation into significant applications. To overcome this, NextGenVis –Research Network for training the Next Generation of European Visual Neuroscientists – will aim its research and training efforts on teaching young researchers in how to a) acquire new, quantitative knowledge on the adaptive properties of the visual brain in health and disease – with a strong focus on the neurocomputational basis – and b) apply this new knowledge to boost innovation in health care and technology. Our pan-European team of academic, health care and private sector partners is ideally suited to accomplish this as it bundles and focuses unique European expertise and resources in brain imaging, psychology, neurology, ophthalmology and computer science. Importantly, the positive impact of this network will extend beyond the current focus on vision and will last long after the funding period. It will continue to link together a team of highly skilled researchers who will inspire each other to excel in visual neuroscience and its applications.