Punishment insensitivity emerges from impaired contingency detection, not aversion insensitivity or reward dominance

Abstract

Our behaviour is shaped by its consequences – we seek rewards and avoid harm. It has been reported that individuals vary markedly in their avoidance of detrimental consequences, i.e. in their sensitivity to punishment. The underpinnings of this variability are poorly understood; they may be driven by differences in aversion sensitivity, motivation for reward, and/or instrumental control. We examined these hypotheses by applying several analysis strategies to the behaviour of rats (n = 48; 18 female) trained in a conditioned punishment task that permitted concurrent assessment of punishment, reward-seeking, and Pavlovian fear. We show that punishment insensitivity is a unique phenotype, unrelated to differences in reward-seeking and Pavlovian fear, and due to a failure of instrumental control. Subjects insensitive to punishment are afraid of aversive events, they are simply unable to change their behaviour to avoid them.

Data availability

All data generated or analysed during this study are included in the manuscript in Figure 1. Source data files, for all figures, have been provided.

Article and author information

Author details

  1. Philip Jean-Richard-dit-Bressel

    School of Psychology, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0898-8987
  2. Cassandra Ma

    School of Psychology, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  3. Laura A Bradfield

    School of Psychology, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3921-0745
  4. Simon Killcross

    School of Psychology, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Gavan P McNally

    School of Psychology, University of New South Wales, Sydney, Australia
    For correspondence
    g.mcnally@unsw.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9061-6463

Funding

Australian Research Council (DP190100482)

  • Gavan P McNally

Australian Research Council (DP170100075)

  • Gavan P McNally

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

Ethics

Animal experimentation: All procedures were approved by the UNSW Animal Ethics Committee (AEC) (ACEC16/160B) and in accordance with the code set out by the National Health and Medical Research Council (NHMRC) for the treatment of animals in research.

Copyright

© 2019, Jean-Richard-dit-Bressel et al.

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.

Metrics

  • 2,727
    views
  • 298
    downloads
  • 42
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Philip Jean-Richard-dit-Bressel
  2. Cassandra Ma
  3. Laura A Bradfield
  4. Simon Killcross
  5. Gavan P McNally
(2019)
Punishment insensitivity emerges from impaired contingency detection, not aversion insensitivity or reward dominance
eLife 8:e52765.
https://doi.org/10.7554/eLife.52765

Share this article

https://doi.org/10.7554/eLife.52765

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Anna Cattani, Don B Arnold ... Nancy Kopell
    Research Article

    The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3–6 Hz), high theta (~6–12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

    1. Neuroscience
    Maëliss Jallais, Marco Palombo
    Research Article

    This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.