September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Representational confusion: the possible consequence of demeaning your data
Author Affiliations
  • Fernando Ramírez
    Bernstein Center for Computational Neuroscience Berlin, Charité – Universitätsmedizin Berlin, Germany
    Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin, Berlin, Germany
  • Carsten Allefeld
    Bernstein Center for Computational Neuroscience Berlin, Charité – Universitätsmedizin Berlin, Germany
    Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin, Berlin, Germany
  • John-Dylan Haynes
    Bernstein Center for Computational Neuroscience Berlin, Charité – Universitätsmedizin Berlin, Germany
    Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin, Berlin, Germany
Journal of Vision August 2017, Vol.17, 270. doi:10.1167/17.10.270
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      Fernando Ramírez, Carsten Allefeld, John-Dylan Haynes; Representational confusion: the possible consequence of demeaning your data. Journal of Vision 2017;17(10):270. doi: 10.1167/17.10.270.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The increased sensitivity afforded by multivariate pattern analysis methods (MVPA) has led to their widespread application in neuroscience. Recently, similarity-based multivariate methods seeking not only to detect information regarding a dimension of interest, say, an object's rotational angle, but to describe the underlying representational structure, have flourished under the name of Representational Similarity Analysis (RSA). However, data pre-processing steps implemented before conducting RSA can significantly change the correlation (and covariance) structure of the data, hence possibly leading to representational confusion—i.e., concluding that brain area X encodes information according to representational scheme A, and not B, when the opposite is true. Here, we demonstrate with computer simulations and empirical fMRI-data that time series demeaning (including z-scoring) can lead to representational confusion. Further, we expose a complex interaction between the effects of data demeaning and how the brain happens to encode information—usually the question under study—hence incurring a form of circularity. These findings should foster reflection on implicit assumptions bearing on the interpretation of MVPA and RSA, and awareness of the possible impact of data demeaning on inferences regarding representational structure and neural coding

Meeting abstract presented at VSS 2017

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