Figure 2.

Schematic of analysis method. Averaging across voxels within our dlPFC and HPC ROIs, we extracted (1) time-series of single-trial beta-weights for each novel event, (2) time-series of single-trial beta-weights for each target event, and (3) the raw time-series from these regions. In the left panel, we depict schematic, representative data of these 3 measurements for a single subject. For the event-to-baseline analyses (E2B), all 4 beta-weight time-series were smoothed with a 10-trial sliding window and convolved with an hemodynamic response function to generate a predicted baseline signal. For the event-to-event analyses (E2E), the raw signals were centered and convolved with the onset regressors for both the target and novel conditions, resulting in 4 time-series that model event-locked correlations with target regions. Representative schematics of these regressors are depicted in the right panel. Finally, regressors were combined into 2 separate GLM models, separately for novel and target events, to predict the VTA signal (not shown). We removed confounding variables (task events, nuisance regressors, and raw ROI signals) from the VTA time-series before using it as the dependent variable.