  Advances in statistical learning theory have resulted in a multitude of
different designs of learning machines. But which ones are implemented by
brains and other biological information processors? We analyze how various
abstract Bayesian learners perform on different data and argue that it is
difficult to determine which learning-theoretic computation is performed by a
particular organism using just its performance in learning a stationary target
(learning curve). Basing on the fluctuation-dissipation relation in statistical
physics, we then discuss a different experimental setup that might be able to
solve the problem.
