Q-values. Although they can give us a clearer picture of how that specific action is contributing to the agent reaching the maximal goal, it will be extremely sensitive to the fast changes of the competitive dynamics of the game, as illustrated in the example plotted in Figure \[fig:readingsExample\] (b).

To address that and in order to obtain a grounded representation of the agent’s own internal state, we propose the implementation of a Growing-When-Required Network (GWR) [@Marsland2002] to generate prototypical representations of the impact of the measured confidences. The GWR was used recently to address personalization on emotion expression perception [@barros2017self; @barros2019personalized], and in several continuous learning tasks [@Parisi2017; @Parisi2018]. It is a self-organizing network that creates neurons that represents a series of input data and it can be trained online to approximate to the perceived stimuli.

Before feeding it to the GWR, we transform the calculated confidences into a pleasure/arousal (PA) scale [@costa1996mood], which represents a perceived experience two dimensions: pleasing/unpleasing and excited/calm. The PA model has been used as a standard way of representing intrinsic states in virtual agents [@pena2011representing; @zhang2016modeling; @shvo2019towards], and will allow an easy understanding our *Moody framework* representations, and improve its applicability in different scenarios.

