Preconditioned cues have no value

  1. Melissa Sharpe  Is a corresponding author
  2. Hannah Batchelor
  3. Geoffrey Schoenbaum  Is a corresponding author
  1. NIDA Intramural Research Program, United States


Sensory preconditioning has been used to implicate midbrain dopamine in model-based learning, contradicting the view that dopamine transients reflect model-free value. However, it has been suggested that model-free value might accrue directly to the preconditioned cue through mediated learning. Here, building on previous work (Sadacca et al., 2016), we address this question by testing whether a preconditioned cue will support conditioned reinforcement in rats. We found that while both directly conditioned and second-order conditioned cues supported robust conditioned reinforcement, a preconditioned cue did not. These data show that the preconditioned cue in our procedure does not directly accrue model-free value and further suggest that the cue may not necessarily access value even indirectly in a model-based manner. If so, then phasic response of dopamine neurons to cues in this setting cannot be described as signaling errors in predicting value.

Article and author information

Author details

  1. Melissa Sharpe

    NIDA Intramural Research Program, Baltimore, United States
    For correspondence
    Competing interests
    No competing interests declared.
  2. Hannah Batchelor

    NIDA Intramural Research Program, Baltimore, United States
    Competing interests
    No competing interests declared.
  3. Geoffrey Schoenbaum

    NIDA Intramural Research Program, Baltimore, United States
    For correspondence
    Competing interests
    Geoffrey Schoenbaum, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8180-0701


NIDA-IRP (zia-da000587)

  • Geoffrey Schoenbaum

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


Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#15-CNRB-108) of the NIDA-IRP. The protocol was approved by the ACUC at the IRP (Permit Number: A4149-01). Every effort was made to minimize suffering.

Reviewing Editor

  1. Timothy E Behrens, University of Oxford,, United Kingdom

Publication history

  1. Received: May 5, 2017
  2. Accepted: September 18, 2017
  3. Accepted Manuscript published: September 19, 2017 (version 1)
  4. Version of Record published: September 28, 2017 (version 2)


This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.


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  1. Melissa Sharpe
  2. Hannah Batchelor
  3. Geoffrey Schoenbaum
Preconditioned cues have no value
eLife 6:e28362.
  1. Further reading

Further reading

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    Midbrain dopamine neurons have been proposed to signal reward prediction errors as defined in temporal difference (TD) learning algorithms. While these models have been extremely powerful in interpreting dopamine activity, they typically do not use value derived through inference in computing errors. This is important because much real world behavior – and thus many opportunities for error-driven learning – is based on such predictions. Here, we show that error-signaling rat dopamine neurons respond to the inferred, model-based value of cues that have not been paired with reward and do so in the same framework as they track the putative cached value of cues previously paired with reward. This suggests that dopamine neurons access a wider variety of information than contemplated by standard TD models and that, while their firing conforms to predictions of TD models in some cases, they may not be restricted to signaling errors from TD predictions.

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