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Related Experiment Video

Updated: Oct 19, 2025

Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

A Neurocomputational Model for Intrinsic Reward.

Benjamin Chew^1,2, Bastien Blain^3,2, Raymond J Dolan^1,2

¹Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, United Kingdom.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|September 21, 2021

Summary

We developed a computational tool to measure the affective value of experiences, integrating behavior and neural data. This "computational hedonometer" quantifies intrinsic and extrinsic rewards, offering a novel approach beyond traditional economic indicators.

Keywords:

affect mood reward risky decision making value

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Last Updated: Oct 19, 2025

Studying Food Reward and Motivation in Humans

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Published on: March 19, 2014

A Conflict Model of Reward-seeking Behavior in Male Rats

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Published on: February 20, 2019

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

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Published on: May 3, 2012

Area of Science:

Neuroscience
Economics
Computational Psychology

Background:

Traditional economic indicators like GDP fail to capture the full spectrum of human values and non-market activities impacting well-being.
There is a need for novel tools to measure the affective value of intrinsically rewarding experiences, which are often overlooked by standard economic approaches.

Purpose of the Study:

To introduce and validate a computational tool for measuring the affective value of experiences.
To investigate the neural correlates of intrinsic and extrinsic rewards using functional magnetic resonance imaging (fMRI).
To model the affective dynamics influenced by both intrinsic and extrinsic rewards.

Main Methods:

Developed a computational tool to quantify the affective value of experiences.
Employed fMRI to measure neural activity in human subjects during a reinforcement learning task.
Incorporated subjective affective state ratings and analyzed neural activity in relation to intrinsic and extrinsic rewards.

Main Results:

Both intrinsic and extrinsic rewards significantly influenced affective dynamics, as captured by the computational model.
Individuals valuing intrinsic rewards more showed greater ventromedial prefrontal cortex (vmPFC) activity for these rewards.
The computational model successfully indexed the subjective value of intrinsic relative to extrinsic rewards.

Conclusions:

The developed computational tool, a "computational hedonometer," effectively quantifies the affective value of experiences by integrating behavioral and neural data.
Intrinsic rewards, despite lacking instrumental value, demonstrably influence affective state and neural activity (vmPFC).
This approach offers a promising method for assessing the value of non-market activities and subjective experiences beyond traditional economic metrics.