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.

Reviewing Editor

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

Ethics

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

Version 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

  • 3,338
    views
  • 762
    downloads
  • 73
    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. 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

Share this article

https://doi.org/10.7554/eLife.15192

Further reading

    1. Neuroscience
    Kenta Abe, Yuki Kambe ... Tatsuo Sato
    Research Article

    Midbrain dopamine neurons impact neural processing in the prefrontal cortex (PFC) through mesocortical projections. However, the signals conveyed by dopamine projections to the PFC remain unclear, particularly at the single-axon level. Here, we investigated dopaminergic axonal activity in the medial PFC (mPFC) during reward and aversive processing. By optimizing microprism-mediated two-photon calcium imaging of dopamine axon terminals, we found diverse activity in dopamine axons responsive to both reward and aversive stimuli. Some axons exhibited a preference for reward, while others favored aversive stimuli, and there was a strong bias for the latter at the population level. Long-term longitudinal imaging revealed that the preference was maintained in reward- and aversive-preferring axons throughout classical conditioning in which rewarding and aversive stimuli were paired with preceding auditory cues. However, as mice learned to discriminate reward or aversive cues, a cue activity preference gradually developed only in aversive-preferring axons. We inferred the trial-by-trial cue discrimination based on machine learning using anticipatory licking or facial expressions, and found that successful discrimination was accompanied by sharper selectivity for the aversive cue in aversive-preferring axons. Our findings indicate that a group of mesocortical dopamine axons encodes aversive-related signals, which are modulated by both classical conditioning across days and trial-by-trial discrimination within a day.

    1. Neuroscience
    Baiwei Liu, Zampeta-Sofia Alexopoulou, Freek van Ede
    Research Article

    Working memory enables us to bridge past sensory information to upcoming future behaviour. Accordingly, by its very nature, working memory is concerned with two components: the past and the future. Yet, in conventional laboratory tasks, these two components are often conflated, such as when sensory information in working memory is encoded and tested at the same location. We developed a task in which we dissociated the past (encoded location) and future (to-be-tested location) attributes of visual contents in working memory. This enabled us to independently track the utilisation of past and future memory attributes through gaze, as observed during mnemonic selection. Our results reveal the joint consideration of past and future locations. This was prevalent even at the single-trial level of individual saccades that were jointly biased to the past and future. This uncovers the rich nature of working memory representations, whereby both past and future memory attributes are retained and can be accessed together when memory contents become relevant for behaviour.