TY - JOUR T1 - An improved method for evaluating inverted encoding models JF - bioRxiv DO - 10.1101/2021.05.22.445245 SP - 2021.05.22.445245 AU - Paul S. Scotti AU - Jiageng Chen AU - Julie D. Golomb Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/05/23/2021.05.22.445245.abstract N2 - Inverted encoding models have recently become popular as a method for decoding stimuli and investigating neural representations. Here we present a novel modification to inverted encoding models that improves the flexibility and interpretability of stimulus reconstructions, addresses some key issues inherent in the standard inverted encoding model procedure, and provides trial-by-trial stimulus predictions and goodness-of-fit estimates. The standard inverted encoding model approach estimates channel responses (or “reconstructions”), which are averaged and aligned across trials and then typically evaluated using a metric such as slope, amplitude, etc. We discuss how this standard procedure can produce spurious results and other interpretation issues. Our modifications are not susceptible to these methodological issues and are further advantageous due to our decoding metric taking into account the choice of population-level tuning functions and employing a prediction error-based metric directly comparable across experiments. Our modifications also allow researchers to obtain trial-by-trial confidence estimates independent of prediction error which can be used to threshold reconstructions and increase statistical power. We validate and demonstrate the improved utility of our modified inverted encoding model procedure across three real fMRI datasets, and additionally offer a Python package for easy implementation of our approach.Competing Interest StatementThe authors have declared no competing interest. ER -