Choose, rate or squeeze: Comparison of economic value functions elicited by different behavioral tasks

Alizée Lopez-Persem, Lionel Rigoux, Sacha Bourgeois-Gironde, Jean Daunizeau, Mathias Pessiglione

Research output: Contribution to journalArticlepeer-review

Abstract

A standard view in neuroeconomics is that to make a choice, an agent first assigns subjective values to available options, and then compares them to select the best. In choice tasks, these cardinal values are typically inferred from the preference expressed by subjects between options presented in pairs. Alternatively, cardinal values can be directly elicited by asking subjects to place a cursor on an analog scale (rating task) or to exert a force on a power grip (effort task). These tasks can vary in many respects: they can notably be more or less costly and consequential. Here, we compared the value functions elicited by choice, rating and effort tasks on options composed of two monetary amounts: one for the subject (gain) and one for a charity (donation). Bayesian model selection showed that despite important differences between the three tasks, they all elicited a same value function, with similar weighting of gain and donation, but variable concavity. Moreover, value functions elicited by the different tasks could predict choices with equivalent accuracy. Our finding therefore suggests that comparable value functions can account for various motivated behaviors, beyond economic choice. Nevertheless, we report slight differences in the computational efficiency of parameter estimation that may guide the design of future studies.

Original languageEnglish
Article numbere1005848
JournalPLoS Computational Biology
Volume13
Issue number11
DOIs
StatePublished - Nov 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 Lopez-Persem et al.

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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