The growth and form of knowledge networks by kinesthetic curiosity
This work addresses the problem of understanding and simulating curiosity in AI and psychology, offering a novel interdisciplinary approach that expands existing psychological taxonomies.
The authors tackled the challenge of computationally modeling curiosity, which lacks specific goals or external rewards, by integrating network science, statistical physics, and philosophy to develop a kinesthetic model that frames curiosity as searching movements in information space.
Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious.