Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala

  1. Lauren Y Atlas  Is a corresponding author
  2. Bradley B Doll
  3. Jian Li
  4. Nathaniel D Daw
  5. Elizabeth A Phelps
  1. National Institutes of Health, United States
  2. New York University, United States
  3. Peking University, China
  4. Princeton University, United States

Abstract

Socially-conveyed rules and instructions strongly shape expectations and emotions. Yet most neuroscientific studies of learning consider reinforcement history alone, irrespective of knowledge acquired through other means. We examined fear conditioning and reversal in humans to test whether instructed knowledge modulates the neural mechanisms of feedback-driven learning. One group was informed about contingencies and reversals. A second group learned only from reinforcement. We combined quantitative models with functional magnetic resonance imaging and found that instructions induced dissociations in the neural systems of aversive learning. Responses in striatum and orbitofrontal cortex updated with instructions and correlated with prefrontal responses to instructions. Amygdala responses were influenced by reinforcement similarly in both groups and did not update with instructions. Results extend work on instructed reward learning and reveal novel dissociations that have not been observed with punishments or rewards. Findings support theories of specialized threat-detection and may have implications for fear maintenance in anxiety.

Article and author information

Author details

  1. Lauren Y Atlas

    National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, United States
    For correspondence
    lauren.atlas@nih.gov
    Competing interests
    The authors declare that no competing interests exist.
  2. Bradley B Doll

    Center for Neural Sciences, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jian Li

    Department of Psychology, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Nathaniel D Daw

    Department of Psychology, Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Elizabeth A Phelps

    Center for Neural Sciences, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

Ethics

Human subjects: Informed consent was obtained from all subjects. Research was approved by New York University's Institutional Review Board.

Reviewing Editor

  1. Timothy EJ Behrens, University College London, United Kingdom

Publication history

  1. Received: February 11, 2016
  2. Accepted: May 8, 2016
  3. Accepted Manuscript published: May 12, 2016 (version 1)
  4. Version of Record published: June 14, 2016 (version 2)

Copyright

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.

Metrics

  • 3,142
    Page views
  • 731
    Downloads
  • 54
    Citations

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

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. Lauren Y Atlas
  2. Bradley B Doll
  3. Jian Li
  4. Nathaniel D Daw
  5. Elizabeth A Phelps
(2016)
Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala
eLife 5:e15192.
https://doi.org/10.7554/eLife.15192
  1. Further reading

Further reading

    1. Neuroscience
    Andrew P Davison, Shailesh Appukuttan
    Insight

    Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.

    1. Neuroscience
    Jonathan Nicholas, Nathaniel D Daw, Daphna Shohamy
    Research Article

    A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.