  The study of the brain's representations of uncertainty is a central topic in
neuroscience. Unlike most quantities of which the neural representation is
studied, uncertainty is a property of an observer's beliefs about the world,
which poses specific methodological challenges. We analyze how the literature
on the neural representations of uncertainty addresses those challenges and
distinguish between "code-driven" and "correlational" approaches. Code-driven
approaches make assumptions about the neural code for representing world states
and the associated uncertainty. By contrast, correlational approaches search
for relationships between uncertainty and neural activity without constraints
on the neural representation of the world state that this uncertainty
accompanies. To compare these two approaches, we apply several criteria for
neural representations: sensitivity, specificity, invariance, functionality.
Our analysis reveals that the two approaches lead to different, but
complementary findings, shaping new research questions and guiding future
experiments.
