(a) Schematic of linear discriminant contrast analysis (based on [Kriegeskorte et al., 2007]). Within cross-validation folds, data from one imaging run is projected onto the optimal decision boundary derived from other runs, in order to remove inflation by noise in the final distance estimate (obtained by averaging across folds). (b) Multiple regression models detailing how changes in representational (dis)similarity over the course of the task in each ROI relate to overall relative avoidance on generalization trials, and (c) to individual differences in the model parameter governing width of generalization from aversive feedback. Error bars represent standard error. (d) Visualisation of bivariate relationships between change in representational geometry and raw GS avoidance (in primary visual cortex), and (e) between change in representational geometry and individual σA values (in the anterior insula, amygdala, and V1), weighted by individual parameter estimate precision (1/posterior variance). Larger bubble size represents greater precision (and therefore higher regression weight). Light blue shading on structural images illustrates the ROI volumes data were extracted from in each case. CV LDC, leave-one-out cross-validated linear discriminant contrast; a insula, anterior insula; vmPFC, ventromedial prefontal cortex. *p<0.05, **p<0.01.