Humans perseverate on punishment avoidance goals in multigoal reinforcement learning

  1. Paul B Sharp  Is a corresponding author
  2. Evan M Russek
  3. Quentin JM Huys
  4. Raymond J Dolan
  5. Eran Eldar
  1. The Hebrew University of Jerusalem, Israel
  2. University College London, United Kingdom

Abstract

Managing multiple goals is essential to adaptation, yet we are only beginning to understand computations by which we navigate the resource-demands entailed in so doing. Here, we sought to elucidate how humans balance reward seeking and punishment avoidance goals, and relate this to variation in its expression within anxious individuals. To do so, we developed a novel multigoal pursuit task that includes trial-specific instructed goals to either pursue reward (without risk of punishment) or avoid punishment (without the opportunity for reward). We constructed a computational model of multigoal pursuit to quantify the degree to which participants could disengage from the pursuit goals when instructed to, as well as devote less model-based resources towards goals that were less abundant. In general, participants (n=192) were less flexible in avoiding punishment than in pursuing reward. Thus, when instructed to pursue reward, participants often persisted in avoiding features that had previously been associated with punishment, even though at decision time these features were unambiguously benign. In a similar vein, participants showed no significant downregulation of avoidance when punishment avoidance goals were less abundant in the task. Importantly, we show preliminary evidence that individuals with chronic worry may have difficulty disengaging from punishment avoidance when instructed to seek reward. Taken together, the findings demonstrate that people avoid punishment less flexibly than they pursue reward. Future studies should test in larger samples whether a difficulty to disengage from punishment avoidance contributes to chronic worry.

Data availability

All data are available in the main text or the supplementary materials. All code and analyses can be found at: github.com/pq1289/multigoal_RL

Article and author information

Author details

  1. Paul B Sharp

    The Hebrew University of Jerusalem, Jerusalem, Israel
    For correspondence
    paul.sharp@mail.huji.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4949-1501
  2. Evan M Russek

    Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Quentin JM Huys

    Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Raymond J Dolan

    The 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:" 0000-0001-9356-761X
  5. Eran Eldar

    The Hebrew University of Jerusalem, Jerusalem, Israel
    Competing interests
    The authors declare that no competing interests exist.

Funding

Fulbright Association (PS00318453)

  • Paul B Sharp

NIH Blueprint for Neuroscience Research (R01MH124092)

  • Eran Eldar

Wellcome Trust (098362/Z/12/Z)

  • Paul B Sharp

2Max Planck UCL Centre for Computational Psychiatry and Ageing Research (Open Access Funding)

  • Paul B Sharp

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

Ethics

Human subjects: Participants gave written informed consent before taking part in the study, which was approved by the university's ethics review board (project ID number 16639/001).

Copyright

© 2022, Sharp 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

  • 2,356
    views
  • 365
    downloads
  • 8
    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. Paul B Sharp
  2. Evan M Russek
  3. Quentin JM Huys
  4. Raymond J Dolan
  5. Eran Eldar
(2022)
Humans perseverate on punishment avoidance goals in multigoal reinforcement learning
eLife 11:e74402.
https://doi.org/10.7554/eLife.74402

Share this article

https://doi.org/10.7554/eLife.74402

Further reading

    1. Neuroscience
    David C Williams, Amanda Chu ... Michael A McDannald
    Research Advance

    Recognizing and responding to threat cues is essential to survival. Freezing is a predominant threat behavior in rats. We have recently shown that a threat cue can organize diverse behaviors beyond freezing, including locomotion (Chu et al., 2024). However, that experimental design was complex, required many sessions, and had rats receive many foot shock presentations. Moreover, the findings were descriptive. Here, we gave female and male Long Evans rats cue light illumination paired or unpaired with foot shock (8 total) in a conditioned suppression setting, using a range of shock intensities (0.15, 0.25, 0.35, or 0.50 mA). We found that conditioned suppression was only observed at higher foot shock intensities (0.35 mA and 0.50 mA). We constructed comprehensive temporal ethograms by scoring 22,272 frames across 12 behavior categories in 200-ms intervals around cue light illumination. The 0.50 mA and 0.35 mA shock-paired visual cues suppressed reward seeking, rearing, and scaling, as well as light-directed rearing and light-directed scaling. The shock-paired visual cue further elicited locomotion and freezing. Linear discriminant analyses showed that ethogram data could accurately classify rats into paired and unpaired groups. Using complete ethogram data produced superior classification compared to behavior subsets, including an Immobility subset featuring freezing. The results demonstrate diverse threat behaviors – in a short and simple procedure – containing sufficient information to distinguish the visual fear conditioning status of individual rats.