Frontiers in Neurorobotics · 2007 · 789 citations · 36 references
Intrinsic motivation drives spontaneous exploration and curiosity, is central to open‑ended cognitive development, and has attracted growing interest from developmental robotics. The paper aims to synthesize existing intrinsic‑motivation approaches, critique their operationalization, and establish a formal typology for systematic computational study. The typology, grounded in existing computational models and enriched with novel conceptualizations, provides a structured framework for operationalizing intrinsic motivation. The authors contend that this computational typology will open new research avenues in psychology and developmental robotics.
Intrinsic motivation, the causal mechanism for spontaneous exploration and curiosity, is a central concept in developmental psychology. It has been argued to be a crucial mechanism for open-ended cognitive development in humans, and as such has gathered a growing interest from developmental roboticists in the recent years. The goal of this paper is threefold. First, it provides a synthesis of the different approaches of intrinsic motivation in psychology. Second, by interpreting these approaches in a computational reinforcement learning framework, we argue that they are not operational and even sometimes inconsistent. Third, we set the ground for a systematic operational study of intrinsic motivation by presenting a formal typology of possible computational approaches. This typology is partly based on existing computational models, but also presents new ways of conceptualizing intrinsic motivation. We argue that this kind of computational typology might be useful for opening new avenues for research both in psychology and developmental robotics.
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Flow: the psychology of optimal experience
Choice Reviews Online · 1990 · 12.7K citations
Cognitive Science, Behavioral Decision Making, Optimal Experience +5