  Dual-process theories play a central role in both psychology and
neuroscience, figuring prominently in fields ranging from executive control to
reward-based learning to judgment and decision making. In each of these
domains, two mechanisms appear to operate concurrently, one relatively high in
computational complexity, the other relatively simple. Why is neural
information processing organized in this way? We propose an answer to this
question based on the notion of compression. The key insight is that
dual-process structure can enhance adaptive behavior by allowing an agent to
minimize the description length of its own behavior. We apply a single model
based on this observation to findings from research on executive control,
reward-based learning, and judgment and decision making, showing that seemingly
diverse dual-process phenomena can be understood as domain-specific
consequences of a single underlying set of computational principles.
