Eye pupils mirror information divergence in approximate inference

Pupil dilation is considered to track the arousal state linked to a wide range of cognitive processes. A recent article suggested the potential to unify findings in pupillometry studies based on an information theory framework and Bayesian methods. However, Bayesian methods become computationally intractable in many realistic situations. Thus, the present study examined whether pupil responses reflect the amount of information quantified in approximate inference, a practical method in a complex environment. We measured the pupil diameters of 27 healthy adults instructed to predict each subsequent number to be presented in a series, and to update their predictions at several discrete change points when an outcome generation criterion changed. Individual differences in task performance and pupil response were modeled by a variational Bayes method, which quantified prediction uncertainty and change point probability as Kullback-Leibler divergence (D_KL) and Shannon's surprise (SS). This model-based approach revealed that covariance between trial-wise pupil dilation and trial-wise D_KL varies depending on prediction accuracy. Further, SS was sensitive to several discrete change points. These findings suggest that the pupil-linked arousal system reflects information divergence during approximate inference in a dynamic environment.