Abstract
Inverted encoding models (IEMs) have recently become a popular method for investigating neural representations by reconstructing the contents of perception, attention, and memory from neuroimaging data. However, the standard IEM procedure can produce spurious results and interpretation issues. Here we present a novel modification to IEMs (“enhanced inverted encoding modeling,” eIEM) that addresses key issues inherent in the standard IEM procedure, improves the flexibility and interpretability of stimulus reconstructions, and provides trial-by-trial stimulus predictions and goodness-of-fit estimates. Our modifications are 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 trial-by-trial confidence estimates independent of prediction error which can be used to threshold reconstructions and improve neural decoding performance and brain-behavior correlations. We validate the improved utility of eIEM across three fMRI datasets and offer a Python package for easy implementation.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Overall revisions throughout, including renaming our modified IEM procedure to eIEM and discussion of how eIEM can be used for brain-behavior correlations.
a Observed brain activations could be beta weights from general linear model estimation40 or raw BOLD signal from a block or slow-event related design.
