Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning

In any non-deterministic environment, unexpected events can indicate true changes in the world (and require behavioural adaptation) or reflect chance occurrence (and must be discounted). Adaptive behaviour requires distinguishing these possibilities. We investigated how humans achieve this by integrating high-level information from instruction and experience. In a series of EEG experiments, instructions modulated the perceived informativeness of feedback: Participants performed a novel probabilistic reinforcement learning task, receiving instructions about reliability of feedback or volatility of the environment. Importantly, our designs de-confound informativeness from surprise, which typically co-vary. Behavioural results indicate that participants used instructions to adapt their behaviour faster to changes in the environment when instructions indicated that negative feedback was more informative, even if it was simultaneously less surprising. This study is the first to show that neural markers of feedback anticipation (stimulus-preceding negativity) and of feedback processing (feedback-related negativity; FRN) reflect informativeness of unexpected feedback. Meanwhile, changes in P3 amplitude indicated imminent adjustments in behaviour. Collectively, our findings provide new evidence that high-level information interacts with experience-driven learning in a flexible manner, enabling human learners to make informed decisions about whether to persevere or explore new options, a pivotal ability in our complex environment

Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers

The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these techniques pose to safety assurance and indicate opportunities to integrate safety considerations into algorithms "by design". Finally, from a human-centric perspective, we stipulate that, to achieve high levels of safety and ultimately trust, the robotic co-worker must meet the innate expectations of the humans it works with. It is our aim to stimulate a discussion focused on the safety aspects of human-in-the-loop robotics, and to foster multidisciplinary collaboration to address the research challenges identified

Conceptual learning : the priority for higher education

The common sense notion of learning as the all-pervasive acquisition of new behaviour and knowledge, made vivid by experience, is an incomplete characterisation, because it assumes that the learning of behaviour and the learning of knowledge are indistinguishable, and that acquisition constitutes learning without reference to transfer. A psychological level of analysis is used to argue that conceptual learning should have priority in higher education

D3.1 Instructional Designs for Real-time Feedback

The main objective of METALOGUE is to produce a multimodal dialogue system that is able to implement an interactive behaviour that seems natural to users and is flexible enough to exploit the full potential of multimodal interaction. The METALOGUE system will be arranged in the context of educational use-case scenarios, i.e. for training active citizens (Youth Parliament) and call centre employees. This deliverable describes the intended real-time feedback and reflection in-action support to support the training. Real-time feedback informs learners how they perform key skills and enables them to monitor their progress and thus reflect in-action. This deliverable examines the theoretical considerations of reflection in-action, what type of data is available and should be used, the timing and type of real-time feedback and, finally, concludes with an instructional design blueprint giving a global outline of a set of tasks with stepwise increasing complexity and the feedback proposed.The underlying research project is partly funded by the METALOGUE project. METALOGUE is a Seventh Framework Programme collaborative project funded by the European Commission, grant agreement number: 611073 (http://www.metalogue.eu)