Contemplation vs. intuition: a reinforcement learning perspective

In-Koo Cho, Anna Rubinchik

Research output: Contribution to journalArticlepeer-review

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 elements 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 laid-back wise-guy, 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) enhance contemplative decisions (made by the cognitive unit) for an alert agent, i.e., the one who identifies novel problems frequently enough.

Original languageEnglish (US)
Pages (from-to)141-167
Number of pages27
JournalEURO Journal on Decision Processes
Volume5
Issue number1-4
DOIs
StatePublished - Nov 1 2017

Keywords

  • 35B40
  • 60G99
  • 91C99
  • Adaptive learning
  • Executive control
  • Inhibition
  • Stochastic approximation
  • The two-system decision-making

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Statistics and Probability
  • Business, Management and Accounting (miscellaneous)
  • Computational Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Contemplation vs. intuition: a reinforcement learning perspective'. Together they form a unique fingerprint.

Cite this