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Related Experiment Video

Updated: May 1, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

The problem with value.

John P O'Doherty¹

¹Division of Humanities and Social Sciences, and Computation and Neural Systems Program, California Institute of Technology, United States.

Neuroscience and Biobehavioral Reviews

|April 15, 2014

Summary

This summary is machine-generated.

Distinguishing neural value signals from other brain processes is challenging. This review explores experimental methods to identify true value coding and highlights the insights gained from non-value signals in decision-making research.

Keywords:

Decision-making Learning Neuroeconomics Reward

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Last Updated: May 1, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Area of Science:

Neuroscience
Cognitive Neuroscience
Decision Science

Background:

Neural correlates of value are widely reported across brain regions.
Differentiating true value signals from other neural processes (e.g., outcome identity, attention) remains a significant challenge.

Purpose of the Study:

To review experimental manipulations for testing neural value signals.
To identify challenges in ascertaining the nature of neural responses.
To emphasize the utility of non-value signals in understanding decision-making.

Main Methods:

Review of experimental manipulations designed to isolate value-related neural activity.
Analysis of challenges in interpreting neural responses in the context of decision-making.

Main Results:

Difficulty in definitively identifying neural signals as representing value per se.
Identification of alternative neural coding schemes that can be confused with value signals.
Recognition that non-value signals can offer valuable insights into computational processes.

Conclusions:

Experimental design is crucial for distinguishing value signals from other neural processes.
Non-value related neural signals provide critical information about decision-making computations.
Further research is needed to refine methods for identifying neural correlates of value.