Feedback generation is an important component of humanhuman communication. Humans can choose to signal support, understanding, agreement or also sceptiscism by means of feedback tokens. Many studies have focused on the timing of feedback behaviours. In the current study, however, we keep the timing constant and instead focus on the lexical form and prosody of feedback tokens as well as their sequential patterns. For this we crowdsourced participant's feedback behaviour in identical interactional contexts in order to model a virtual agent that is able to provide feedback as an attentive/supportive as well as attentive/sceptical listener. The resulting models were realised in a robot which was evaluated by third-party observers.