  We present a computational and theoretical model of the neural mechanisms
underlying human decision-making. We propose a detailed model of the
interaction between brain regions, under a proposer-predictor-actor-critic
framework. Task-relevant areas of cortex propose a candidate plan using fast,
model-free, parallel constraint-satisfaction computations. Other areas of
cortex and medial temporal lobe can then predict likely outcomes of that plan
in this situation. This step is optional. This prediction-(or model-) based
computation produces better accuracy and generalization, at the expense of
speed. Next, linked regions of basal ganglia act to accept or reject the
proposed plan based on its reward history in similar contexts. Finally the
reward-prediction system acts as a critic to determine the value of the outcome
relative to expectations, and produce dopamine as a training signal for cortex
and basal ganglia. This model gains many constraints from the hypothesis that
the mechanisms of complex human decision-making are closely analogous to those
that have been empirically studied in detail for animal action-selection. We
argue that by operating sequentially and hierarchically, these same mechanisms
are responsible for the most complex human plans and decisions. Finally, we use
the computational model to generate novel hypotheses on causes of human risky
decision-making, and compare this to other theories of human decision-making.
