Figure 2. Modelling of learning and stress.

(a) Hierarchical Gaussian Filter model²¹. Beliefs are represented in probability distributions organized in a hierarchy, with the speed of updating at each level influenced by the estimate at the level above. This allows learning to occur more quickly in volatile environments. Each level is Gaussian, characterized by a mean (μ) and a variance (σ), which corresponds to uncertainty. These representations unfold over time, with the model furnishing an estimate at each level, for each trial. We take these dynamic representations of uncertainty from this model (b) and use them to predict stress responses (c) using linear modelling (d). The resultant regression coefficients (β_1−n) quantify the influence of each form of uncertainty on that stress measure. Note that X_1−n are the regressors in the linear model, which include but are not limited to uncertainty trajectories; we also include terms such as number of shocks, and nuisance variables such as gaze co-ordinates.