Contemplation vs. Intuition: A Reinforcement Learning Perspective
Abstract: In a search for a positive model of decision-making
with observable primitives, we rely on the burgeoning literature
in cognitive neuroscience to construct a three-element machine
(agent). Its control unit initiates either impulsive or cognitive element to solve a problem in a stationary Markov environment, the
element “chosen” depends on whether the problem is mundane or
novel, memory of past successes and the strength of inhibition.
Our predictions are based on a stationary asymptotic distribution of the memory, which, depending on the parameters, can
generate different “characters”, e.g., an uptight dimwit, who could
succeed more often with less inhibition, as well as a relaxed wiseguy, who could gain more with a stronger inhibition of impulsive
(intuitive) responses.
As one would expect, stronger inhibition and lower cognitive
costs increase the frequency of decisions made by the cognitive element. More surprisingly, increasing the “carrot” and reducing
the “stick” (being in a more supportive environment) enhances
contemplative decisions (made by the cognitive unit) for an alert
agent, i.e., the one who identifies novel problems frequently enough.
Last Updated Date : 03/01/2016