1. Computational and Systems Biology
  2. Neuroscience
Download icon

Statistical context dictates the relationship between feedback-related EEG signals and learning

  1. Matthew R Nassar  Is a corresponding author
  2. Rasmus Bruckner
  3. Michael J Frank
  1. Brown University, United States
  2. Freie Universität Berlin, Germany
Research Article
  • Cited 12
  • Views 2,108
  • Annotations
Cite this article as: eLife 2019;8:e46975 doi: 10.7554/eLife.46975

Abstract

Learning should be adjusted according to the surprise associated with observed outcomes but calibrated according to statistical context. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily to speed learning. In contrast, when uninformative outliers are expected to occur occasionally, surprising outcomes should be less influential. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but less learning in a context where surprise was indicative of a one-off outlier (oddball). Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.

Data availability

All analysis code has been made available on GitHub (https://github.com/learning-memory-and-decision-lab/NassarBrucknerFrank_eLife_2019.git). All behavioral and EEG data has been made available on Dryad (doi:10.5061/dryad.570pf8n).

The following data sets were generated

Article and author information

Author details

  1. Matthew R Nassar

    Robert J and Nancy D Carney Institute for Brain Science, Brown University, Providence, United States
    For correspondence
    mattnassar@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5397-535X
  2. Rasmus Bruckner

    Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3033-6299
  3. Michael J Frank

    Robert J and Nancy D Carney Institute for Brain Science, Brown University, Providence, United States
    Competing interests
    Michael J Frank, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8451-0523

Funding

National Institute of Mental Health (F32MH102009)

  • Matthew R Nassar

National Institute on Aging (K99AG054732)

  • Matthew R Nassar

National Institute of Mental Health (R01 MH080066-01)

  • Michael J Frank

National Science Foundation (1460604)

  • Michael J Frank

German Academic Exchange Service London (Promos travel grant)

  • Rasmus Bruckner

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

Ethics

Human subjects: Informed consent was obtained from each participant in the study and all procedures were performed in accordance with the Declaration of Helsinki. All procedures were approved by the Brown University Institutional Review Board (Brown University Federal Wide Assurance #00004460).

Reviewing Editor

  1. Tobias H Donner, University Medical Center Hamburg-Eppendorf, Germany

Publication history

  1. Received: March 19, 2019
  2. Accepted: August 12, 2019
  3. Accepted Manuscript published: August 21, 2019 (version 1)
  4. Version of Record published: August 30, 2019 (version 2)

Copyright

© 2019, Nassar 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

  • 2,108
    Page views
  • 282
    Downloads
  • 12
    Citations

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

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    Christopher P Mancuso et al.
    Research Article Updated

    Environmental disturbances have long been theorized to play a significant role in shaping the diversity and composition of ecosystems. However, an inability to specify the characteristics of a disturbance experimentally has produced an inconsistent picture of diversity-disturbance relationships (DDRs). Here, using a high-throughput programmable culture system, we subjected a soil-derived bacterial community to dilution disturbance profiles with different intensities (mean dilution rates), applied either constantly or with fluctuations of different frequencies. We observed an unexpected U-shaped relationship between community diversity and disturbance intensity in the absence of fluctuations. Adding fluctuations increased community diversity and erased the U-shape. All our results are well-captured by a Monod consumer resource model, which also explains how U-shaped DDRs emerge via a novel ‘niche flip’ mechanism. Broadly, our combined experimental and modeling framework demonstrates how distinct features of an environmental disturbance can interact in complex ways to govern ecosystem assembly and offers strategies for reshaping the composition of microbiomes.

    1. Computational and Systems Biology
    Michael S Lauer, Deepshikha Roychowdhury
    Research Article Updated

    Previous reports have described worsening inequalities of National Institutes of Health (NIH) funding. We analyzed Research Project Grant data through the end of Fiscal Year 2020, confirming worsening inequalities beginning at the time of the NIH budget doubling (1998–2003), while finding that trends in recent years have reversed for both investigators and institutions, but only to a modest degree. We also find that career-stage trends have stabilized, with equivalent proportions of early-, mid-, and late-career investigators funded from 2017 to 2020. The fraction of women among funded PIs continues to increase, but they are still not at parity. Analyses of funding inequalities show that inequalities for investigators, and to a lesser degree for institutions, have consistently been greater within groups (i.e. within groups by career stage, gender, race, and degree) than between groups.