Contemplation vs. intuition: a reinforcement learning perspective

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Cho, IK (Author) Other; Rubinchik, A (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: EURO JOURNAL ON DECISION PROCESSES

Vol. 5

Pages: 141-167

ISSN: 2193-9438

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-017-3SE

DOI: 10.1007/s40070-017-0068-x

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27