  A new prior is proposed for learning representations of high-level concepts
of the kind we manipulate with language. This prior can be combined with other
priors in order to help disentangling abstract factors from each other. It is
inspired by cognitive neuroscience theories of consciousness, seen as a
bottleneck through which just a few elements, after having been selected by
attention from a broader pool, are then broadcast and condition further
processing, both in perception and decision-making. The set of recently
selected elements one becomes aware of is seen as forming a low-dimensional
conscious state. This conscious state is combining the few concepts
constituting a conscious thought, i.e., what one is immediately conscious of at
a particular moment. We claim that this architectural and
information-processing constraint corresponds to assumptions about the joint
distribution between high-level concepts. To the extent that these assumptions
are generally true (and the form of natural language seems consistent with
them), they can form a useful prior for representation learning. A
low-dimensional thought or conscious state is analogous to a sentence: it
involves only a few variables and yet can make a statement with very high
probability of being true. This is consistent with a joint distribution (over
high-level concepts) which has the form of a sparse factor graph, i.e., where
the dependencies captured by each factor of the factor graph involve only very
few variables while creating a strong dip in the overall energy function. The
consciousness prior also makes it natural to map conscious states to natural
language utterances or to express classical AI knowledge in a form similar to
facts and rules, albeit capturing uncertainty as well as efficient search
mechanisms implemented by attention mechanisms.
