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 minimization
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 minimization 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 its advantages for state and
noise estimation, system identification and action selection for informative
path planning.

DOI:https://doi.org/10.3389/fnbot.2022.896229