1. Neuroscience
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Model-based fMRI reveals dissimilarity processes underlying base rate neglect

  1. Sean R O'Bryan  Is a corresponding author
  2. Darrell A Worthy
  3. Evan J Livesey
  4. Tyler Davis
  1. Texas Tech University, United States
  2. Texas A&M University, United States
  3. University of Sydney, Australia
Research Article
  • Cited 7
  • Views 959
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Cite this article as: eLife 2018;7:e36395 doi: 10.7554/eLife.36395

Abstract

Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have either posited that it arises from associative similarity-based mechanisms or dissimilarity-based processes that may depend upon higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.

Data availability

Source data and scripts used to create all figures and tables (e.g., R code, PyMVPA scripts, statistical maps for the model-based fMRI analysis) are posted to a publicly available online repository (Open Science Framework: https://osf.io/atbz7/). Raw fMRI data for the study organized according to Brain Imaging Data Structure (BIDS) guidelines are available at https://openneuro.org/datasets/ds001302.

Article and author information

Author details

  1. Sean R O'Bryan

    Department of Psychological Sciences, Texas Tech University, Lubbock, United States
    For correspondence
    sean.r.obryan@ttu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0562-8211
  2. Darrell A Worthy

    Department of Psychology, Texas A&M University, College Station, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Evan J Livesey

    School of Psychology, University of Sydney, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
  4. Tyler Davis

    Department of Psychological Sciences, Texas Tech University, Lubbock, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

Texas Tech University

  • Tyler Davis

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

Ethics

Human subjects: Subjects provided written informed consent before taking part in the study, and all procedures involving human subjects were approved by the Texas Tech University Institutional Review Board.

Reviewing Editor

  1. Timothy Verstynen, Carnegie Mellon University, United States

Publication history

  1. Received: March 5, 2018
  2. Accepted: August 1, 2018
  3. Accepted Manuscript published: August 3, 2018 (version 1)
  4. Version of Record published: August 24, 2018 (version 2)

Copyright

© 2018, O'Bryan 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.

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