Loss Aversion Correlates With the Propensity to Deploy Model-Based Control.

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ID: 96684
2019
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Abstract
Reward-based decision making is thought to be driven by at least two different types of decision systems: a simple stimulus-response cache-based system which embodies the common-sense notion of "habit," for which model-free reinforcement learning serves as a computational substrate, and a more deliberate, prospective, model-based planning system. Previous work has shown that loss aversion, a well-studied measure of how much more on average individuals weigh losses relative to gains during decision making, is reduced when participants take all possible decisions and outcomes into account including future ones, relative to when they myopically focus on the current decision. Model-based control offers a putative mechanism for implementing such foresight. Using a well-powered data set ( = 117) in which participants completed two different tasks designed to measure each of the two quantities of interest, and four models of choice data for these tasks, we found consistent evidence of a relationship between loss aversion and model-based control but in the direction opposite to that expected based on previous work: loss aversion had a positive relationship with model-based control. We did not find evidence for a relationship between either decision system and risk aversion, a related aspect of subjective utility.
Reference Key
solway2019lossfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Solway, Alec;Lohrenz, Terry;Montague, P Read;
Journal Frontiers in neuroscience
Year 2019
DOI
10.3389/fnins.2019.00915
URL
Keywords
planning reinforcement learning neuroeconomics model-based loss aversion subjective utility

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