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

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).

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

  • 3,587
    views
  • 503
    downloads
  • 66
    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. Matthew R Nassar
  2. Rasmus Bruckner
  3. Michael J Frank
(2019)
Statistical context dictates the relationship between feedback-related EEG signals and learning
eLife 8:e46975.
https://doi.org/10.7554/eLife.46975

Share this article

https://doi.org/10.7554/eLife.46975

Further reading

    1. Computational and Systems Biology
    2. Microbiology and Infectious Disease
    Gaetan De Waele, Gerben Menschaert, Willem Waegeman
    Research Article

    Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged at https://github.com/gdewael/maldi-nn.