  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.
