  Computational models of visual attention in artificial intelligence and
robotics have been inspired by the concept of a saliency map. These models
account for the mutual information between the (current) visual information and
its estimated causes. However, they fail to consider the circular causality
between perception and action. In other words, they do not consider where to
sample next, given current beliefs. Here, we reclaim salience as an active
inference process that relies on two basic principles: uncertainty minimisation
and rhythmic scheduling. For this, we make a distinction between attention and
salience. Briefly, we associate attention with precision control, i.e., the
confidence with which beliefs can be updated given sampled sensory data, and
salience with uncertainty minimisation that underwrites the selection of future
sensory data. Using this, we propose a new account of attention based on
rhythmic precision-modulation and discuss its potential in robotics, providing
numerical experiments that showcase advantages of precision-modulation for
state and noise estimation, system identification and action selection for
informative path planning.
