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A Toolbox for Representational Similarity Analysis

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  • Hamed Nili
  • Cai Wingfield
  • Alexander Walther
  • Li Su
  • William Marslen-Wilson
  • Nikolaus Kriegeskorte

Abstract

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).

Suggested Citation

  • Hamed Nili & Cai Wingfield & Alexander Walther & Li Su & William Marslen-Wilson & Nikolaus Kriegeskorte, 2014. "A Toolbox for Representational Similarity Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
    • Handle: RePEc:plo:pcbi00:1003553
    DOI: 10.1371/journal.pcbi.1003553
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    1. Johannes Haushofer & Margaret S Livingstone & Nancy Kanwisher, 2008. "Multivariate Patterns in Object-Selective Cortex Dissociate Perceptual and Physical Shape Similarity," PLOS Biology, Public Library of Science, vol. 6(7), pages 1-9, July.
    2. Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
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    4. Cai Wingfield & Li Su & Xunying Liu & Chao Zhang & Phil Woodland & Andrew Thwaites & Elisabeth Fonteneau & William D Marslen-Wilson, 2017. "Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-25, September.
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    7. Alexander J Barnett & Walter Reilly & Halle R Dimsdale-Zucker & Eda Mizrak & Zachariah Reagh & Charan Ranganath, 2021. "Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain," PLOS Biology, Public Library of Science, vol. 19(6), pages 1-34, June.
    8. Máté Aller & Agoston Mihalik & Uta Noppeney, 2022. "Audiovisual adaptation is expressed in spatial and decisional codes," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    9. Jörn Diedrichsen & Nikolaus Kriegeskorte, 2017. "Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.
    10. Ming Bo Cai & Nicolas W Schuck & Jonathan W Pillow & Yael Niv, 2019. "Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-30, May.
    11. Michael F Bonner & Russell A Epstein, 2018. "Computational mechanisms underlying cortical responses to the affordance properties of visual scenes," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-31, April.
    12. Manoj Kumar & Cameron T Ellis & Qihong Lu & Hejia Zhang & Mihai Capotă & Theodore L Willke & Peter J Ramadge & Nicholas B Turk-Browne & Kenneth A Norman, 2020. "BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-12, January.
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    15. Katherine R. Storrs & Barton L. Anderson & Roland W. Fleming, 2021. "Unsupervised learning predicts human perception and misperception of gloss," Nature Human Behaviour, Nature, vol. 5(10), pages 1402-1417, October.

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