Momentary subjective well-being depends on learning and not reward

  1. Bastien Blain  Is a corresponding author
  2. Robb B Rutledge
  1. University College London, United Kingdom


Subjective well-being or happiness is often associated with wealth. Recent studies suggest that momentary happiness is associated with reward prediction error, the difference between experienced and predicted reward, a key component of adaptive behaviour. We tested subjects in a reinforcement learning task in which reward size and probability were uncorrelated, allowing us to dissociate between the contributions of reward and learning to happiness. Using computational modelling, we found convergent evidence across stable and volatile learning tasks that happiness, like behaviour, is sensitive to learning-relevant variables (i.e., probability prediction error). Unlike behaviour, happiness is not sensitive to learning-irrelevant variables (i.e., reward prediction error). Increasing volatility reduces how many past trials influence behaviour but not happiness. Finally, depressive symptoms reduce happiness more in volatile than stable environments. Our results suggest that how we learn about our world may be more important for how we feel than the rewards we actually receive.

Data availability

Data and code are available online (

Article and author information

Author details

  1. Bastien Blain

    Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7735-6043
  2. Robb B Rutledge

    Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7337-5039


Medical Research Council (MR/N02401X/1)

  • Robb B Rutledge

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.


Human subjects: All subjects gave informed consent and the Research Ethics Committee of University College London approved the study study (Committee approval ID Number: 12673/001).

Reviewing Editor

  1. Daeyeol Lee, Johns Hopkins University, United States

Version history

  1. Received: April 17, 2020
  2. Accepted: November 16, 2020
  3. Accepted Manuscript published: November 17, 2020 (version 1)
  4. Version of Record published: December 22, 2020 (version 2)
  5. Version of Record updated: April 13, 2021 (version 3)


© 2020, Blain & Rutledge

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


  • 5,274
    Page views
  • 637
  • 19

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Bastien Blain
  2. Robb B Rutledge
Momentary subjective well-being depends on learning and not reward
eLife 9:e57977.

Share this article

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Tony Zhang, Matthew Rosenberg ... Markus Meister
    Research Article

    An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Here we propose a neural algorithm that can solve all these problems and operates reliably in diverse and complex environments. At its core, the mechanism makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source. We show how the brain can learn to generate internal “virtual odors” that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

    1. Neuroscience
    Frances Skinner

    Automatic leveraging of information in a hippocampal neuron database to generate mathematical models should help foster interactions between experimental and computational neuroscientists.