TY - JOUR T1 - The importance of considering model choices when interpreting results in computational neuroimaging JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0196-19.2019 SP - ENEURO.0196-19.2019 AU - Thomas C. Sprague AU - Geoffrey M. Boynton AU - John T. Serences Y1 - 2019/11/26 UR - http://www.eneuro.org/content/early/2019/11/26/ENEURO.0196-19.2019.abstract N2 - Model-based analyses open exciting opportunities for understanding neural information processing. In a commentary published in eNeuro, Gardner & Liu (2019) discuss the role of model specification in interpreting results derived from complex models of neural data. As a case study, they suggest that one such analysis, the inverted encoding model (IEM), 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”. Here, we argue that 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. 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.Significance Statement Gardner & Liu (2019) point out that linear models can provide equally good fits to data across a class of linear transforms applied during analysis. They suggest this is particularly problematic for one analysis method – the inverted encoding model – that uses activation patterns to estimate responses in modeled information channels, as this renders results arbitrary. Instead, we argue results are not arbitrary when considered in the context of a well-motivated model. Of course, changing model properties can change results, but this applies to all model-based analyses, regardless of inversion. Changing properties of models to recover desired results without disclosure is always ill-advised. When used properly, especially to compare population-level response profiles across conditions, these approaches remain useful tools. ER -