Intrinsically motivated information-seeking, also called curiositydriven exploration, is widely believed to be a key ingredient for autonomous learning in the real world. Such forms of spontaneous exploration have been studied in multiple independent lines of computational research, producing a diverse range of algorithmic models that capture different aspects of these processes. These algorithms resolve some of the limitations of neurocognitive theories by formally describing computational functions and algorithmic implementations of intrinsically motivated learning. Moreover, they reveal a high diversity of effective forms of intrinsically motivated information-seeking that can be characterized along different mechanistic and functional dimensions. This chapter aims at reviewing different classes of algorithms and highlighting several important dimensions of variation among them. Identifying these dimensions provides means for structuring a comprehensive taxonomy of approaches. We believe this exercise to be useful in working towards a general computational account of information-seeking. Such an account should facilitate the proposition of new hypotheses about informationseeking in humans and complement the existing psychological theory of curiosity.