In this project MoReSpace, we will investigate the extent to which the transfer of learning is responsible for the development of a "self", and hypothesize that a conflict-driven attention model plays a major role. In the first part of our project, we investigate the transfer of previously learned action-effect associations to new unexpected environmental dynamics. Here, we put a strong focus on cognitive plausibility and motivate our model with psychological phenomena such as "haptic neglect". The phenomenon occurs, for example, when the computer mouse is inverted and the mouse pointer is directed in the opposite direction in each case. In such scenarios, psychologists have found reduced perception of the haptic and proprioceptive senses. Our hypothesis is that this is due to a conflict-driven attention mechanism that improves the ability to deal with such new dynamics. We will evaluate our model on a physical robot, and we will theoretically substantiate it with our collaboration partners from psychology. In the second part of the project, we will focus on imitation learning. Our hypothesis is that the attention model captures some psychological properties that are important for the human ability to change perspective and to imitate. We hypothesize that this will lead to novel methods of imitation learning for robots. We expect these methods to lead to significant improvements in the learning performance. We will evaluate this empirically and reproducibly.

Embodied systems are subject to their sensorimotor constrains, which shape the way these systems can perceive and act on the external world. Corresponding distinctions in the world constitute the Umwelt of such systems, a notion that has been introduced by Uexküll for the first time in 1926 who also studied it in the context of various animals. In order to control beneficial behavior, embodied systems have to structure some aspects of their Umwelt in terms of an internal model. We distinguish various degrees of this structure, which is, for particular conditions, correlated with conscious experience and a self-representation. Tononi's integrated information theory of consciousness can provide indicators for this. The aim of the project is to extend and develop information-theoretic methods that will allow us to use the integrated information theory also in the context of embodied systems. In particular, the project will reveal the requirements for the emergence of a self-representation through the interaction of the system with the world. The methods will be demonstrated and exemplified in terms of robotics experiments.

In this project, we will investigate how far the transfer of learning is responsible for the development of a “self”. We will, therefore, present a computational ideomotor approach, and hypothesize that the transfer is possible due to a hierarchical structure of action-effect associations, such that training a specific narrow low-level task indirectly trains higher cognitive skills that are involved in other low-level tasks. For example, manipulating objects and balancing are two low-level tasks that both involve the cognitive skill of mental rotation. Consequently, according to our hypothesis, the mental rotation skill will benefit from balance training, which in turn also triggers an improvement of the learning of the grasping task. We will address our hypothesis by implementing a computational and neurocognitively plausible neural network architecture evaluated on a physical humanoid robot. Our expected contribution is a functional neurocognitively plausible deep reinforcement neural network model for ideomotor transfer learning that is verifiable on a reproducible robotic platform.