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Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI

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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11700))

Abstract

Decoding and encoding models are popular multivariate approaches used to study representations in functional neuroimaging data. Encoding approaches seek to predict brain activation patterns using aspects of the stimuli as features. Decoding models, in contrast, utilize measured brain responses as features to make predictions about experimental manipulations or behavior. Both approaches have typically included linear classification components. Ideally, decoding and encoding models could be used for the dual purpose of prediction and neuroscientific knowledge gain. However, even within a linear framework, interpretation can be difficult. Encoding models suffer from feature fallacy; multiple combinations of features derived from a stimulus may describe measured brain responses equally well. Interpreting linear decoding models also requires great care, particularly when informative predictor variables (e.g., fMRI voxels) are present in great quantity (redundant) and correlated with noise measurements. In certain cases, noise channels may be assigned a stronger weight than channels that contain relevant information. Although corrections for this problem exist, there are certain noise sources - common to functional neuroimaging recordings - that may complicate corrective approaches, even after regularization is applied. Here, we review potential pitfalls for making inferences based on encoding and decoding hypothesis testing, and suggest a form of feature fallacy also extends to the decoding framework.

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Authors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Pamela K. Douglas

  2. University of California, Los Angeles, CA, USA

    Pamela K. Douglas & Ariana Anderson

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  1. Pamela K. Douglas
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  2. Ariana Anderson
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Correspondence to Pamela K. Douglas .

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Editors and Affiliations

  1. Fraunhofer Heinrich Hertz Institute, Berlin, Germany

    Wojciech Samek

  2. Technische Universität Berlin, Berlin, Germany

    Grégoire Montavon

  3. University of Oxford, Oxford, UK

    Andrea Vedaldi

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Lars Kai Hansen

  5. Sekretariat MAR 4-1, Technical University of Berlin, Berlin, Berlin, Germany

    Klaus-Robert Müller

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Douglas, P.K., Anderson, A. (2019). Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L., Müller, KR. (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science(), vol 11700. Springer, Cham. https://doi.org/10.1007/978-3-030-28954-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-28954-6_20

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