September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Inverted encoding models reconstruct the model response, not the stimulus
Author Affiliations
  • Justin L. Gardner
    Stanford University
    Speaker:
  • Taosheng Liu
    Michigan State University
Journal of Vision September 2019, Vol.19, 6b. doi:https://doi.org/10.1167/19.10.6b
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      Justin L. Gardner, Taosheng Liu; Inverted encoding models reconstruct the model response, not the stimulus. Journal of Vision 2019;19(10):6b. doi: https://doi.org/10.1167/19.10.6b.

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

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Abstract

Life used to be simpler for sensory neuroscientists. Some measurement of neural activity, be it single-unit activity or increase in BOLD response, was measured against systematic variation of a stimulus and the resulting tuning functions presented and interpreted. But as the field discovered signal in the pattern of responses across voxels in a BOLD measurement or dynamic structure hidden within the activity of a population of neurons, computational techniques to extract features not easily discernible from raw measurement increasingly began to intervene between measurement and data presentation and interpretation. I will discuss one particular technique, the inverted encoding model, and how it extracts model responses rather than stimulus representations and what challenges that makes for interpretation of results.

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