  In several papers published in Biological Cybernetics in the 1980s and 1990s,
Kawato and colleagues proposed computational models explaining how internal
models are acquired in the cerebellum. These models were later supported by
neurophysiological experiments using monkeys and neuroimaging experiments
involving humans. These early studies influenced neuroscience from basic,
sensory-motor control to higher cognitive functions. One of the most perplexing
enigmas related to internal models is to understand the neural mechanisms that
enable animals to learn large-dimensional problems with so few trials.
Consciousness and metacognition -- the ability to monitor one's own thoughts,
may be part of the solution to this enigma. Based on literature reviews of the
past 20 years, here we propose a computational neuroscience model of
metacognition. The model comprises a modular hierarchical
reinforcement-learning architecture of parallel and layered, generative-inverse
model pairs. In the prefrontal cortex, a distributed executive network called
the "cognitive reality monitoring network" (CRMN) orchestrates conscious
involvement of generative-inverse model pairs in perception and action. Based
on mismatches between computations by generative and inverse models, as well as
reward prediction errors, CRMN computes a "responsibility signal" that gates
selection and learning of pairs in perception, action, and reinforcement
learning. A high responsibility signal is given to the pairs that best capture
the external world, that are competent in movements (small mismatch), and that
are capable of reinforcement learning (small reward prediction error). CRMN
selects pairs with higher responsibility signals as objects of metacognition,
and consciousness is determined by the entropy of responsibility signals across
all pairs.
