Figure 4.

(A–D) Schematic of inverted encoding models (IEMs) for reconstructing representations of the stimulus. The response in each voxel is modeled using linear regression to estimate the magnitude of the response in different information channels that correspond to hypothesized response properties of underlying neural populations (here two orientation selective tuning functions are shown, and these would be used as the basis set to model the response of each voxel). Once the weight on each channel is computed using an independent training set of data, novel test data collected in each voxel can be mapped back into the space of the information channels, which effectively forms a reconstruction of the remembered stimulus. Illustrated here is a schematic with just two voxels and a 2-channel encoding model for stimulus orientation. (A) shows the set of stimuli used to train the encoding model, and (B) shows the orientation selective basis functions. (C) The response to each of the 5 oriented stimuli is then measured in each voxel, and used to estimate the response in each information channel (shown in D). As in MVPA, you can have an equal average response to each of the 5 stimuli within the region of interest, but there can still be a pattern of activation across the voxels that carries information about the remembered feature (lower panel, C). (E) Reconstruction of a remembered orientation from areas V1 and V2 during the delay period of a recall STM task. (F) Correlations between the bandwidth (dispersion or inverse of precision) of individual subject orientation reconstructions and their behaviorally assess memory recall performance. All error bars reflect SEM across subjects. Panels A-D from Sprague and Serences, 2015, and panels E-F from Ester et al., 2013. Reprinted with permission from the authors and the original publisher.