Individual’s preferences, learning ability, passion, and perseverance influence which available learning challenges he will choose, for how long he will persist, what emotions will be experienced while working on those challenges and what utility will be gained from these activities. In our approach to this interdisciplinary problem, we build a bridge between time-allocation models developed within utility theory and empirical emotional experience and learning models from psychology by developing a novel task-based time allocation model. As parameters of the model are highly dynamic, we use Monte Carlo simulations to investigate the phase space of observed emotional states with respect to aforementioned individual’s traits.