An improved method for evaluating inverted encoding models

Abstract

Here we present a more interpretable and versatile approach for reconstructing the contents of perception, attention, and memory from neuroimaging data. Our enhanced inverted encoding model (eIEM) incorporates theoretical and methodological improvements including proper accounting of population-level tuning functions and a trial-by-trial prediction error-based metric where reconstruction quality is measured in meaningful units. Added functionality and improved flexibility is further gained via eIEMs novel goodness-of-fit feature: for trial-by-trial reconstructions, goodness-of-fits are obtained independently (non-circularly) to prediction error and can be applied to any IEM procedure or decoding metric, resulting in improved reconstruction quality and brain-behavior correlations, and more creative applications. We validate eIEM from methodological principles, simulated neuroimaging datasets, and three pre-existing fMRI datasets spanning perception, attention, and working memory. Notably, eIEM is easy to apply and broadly accessible - our Python package (https://pypi.org/project/inverted-encoding) implements eIEM in one line of code - and is easily modifiable to compare performance metrics and/or scale up to more complex models.