Dark Control: A Unified Account of Default Mode Function by Control Theory and Reinforcement Learning

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its highest energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an overarching function. Many research streams support an evolutionarily adaptive role in envisioning experience to anticipate the future. The present paper proposes a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. DMN function is recast in mathematical terms of control theory and reinforcement learning based on Markov decision processes. We argue that our formal account of DMN function naturally accommodates as special cases the previously proposed cognitive accounts on (1) predictive coding, (2) semantic associations, and (3) a “sentinel” role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.