Inverted encoding models estimate sensible channel responses for sensible models

In a commentary published in eNeuro, Gardner & Liu (2019) discuss the role of model specification in interpreting the output of complex models of neural data. As a case study, they suggest that one variant of such analyses, the inverted encoding model (IEM) analysis framework, should not be used to assay properties of \"stimulus representations\" because the ability to apply linear transformations at various stages of the analysis procedure renders results arbitrary. As we discuss, the specification of all models is arbitrary to the extent that an experimenter makes choices based on current knowledge of the model system. However, the results derived from any given model, such as the reconstructed channel response profiles obtained from an IEM analysis, are uniquely defined and are arbitrary only in the sense that changes in the model can predictably change results. Moreover, with knowledge of the model used for IEM analyses, the results remain informative as comparisons between reconstructed channel response profiles across task conditions using a fixed encoding model - the most common use of the IEM technique - can generally capture changes in population-level representation magnitude across linear transformations. Thus, changes in the magnitude of the response profiles across conditions are preserved, even across unprincipled linear transforms. IEM-based channel response profiles should therefore not be considered arbitrary when the model is clearly specified and guided by our best understanding of neural population representations in the brain regions being analyzed. Intuitions derived from this case study are important to consider when interpreting results from all model-based analyses, which are similarly contingent upon the specification of the models used.