With an eye on uncertainty: Modelling pupillary responses to environmental volatility

Abstract

Living creatures must accurately infer the nature of their environments. They do this despite being confronted by stochastic and context sensitive contingencies—and so must constantly update their beliefs regarding their uncertainty about what might come next. In this work, we examine how we deal with uncertainty that evolves over time. This prospective uncertainty (or imprecision) is referred to as volatility and has previously been linked to noradrenergic signals that originate in the locus coeruleus. Using pupillary dilatation as a measure of central noradrenergic signalling, we tested the hypothesis that changes in pupil diameter reflect inferences humans make about environmental volatility. To do so, we collected pupillometry data from participants presented with a stream of numbers. We generated these numbers from a process with varying degrees of volatility. By measuring pupillary dilatation in response to these stimuli—and simulating the inferences made by an ideal Bayesian observer of the same stimuli—we demonstrate that humans update their beliefs about environmental contingencies in a Bayes optimal way. We show this by comparing general linear (convolution) models that formalised competing hypotheses about the causes of pupillary changes. We found greater evidence for models that included Bayes optimal estimates of volatility than those without. We additionally explore the interaction between different causes of pupil dilation and suggest a quantitative approach to characterising a person’s prior beliefs about volatility.

Brain dynamics for confidence-weighted learning

“…Previous studies showed that the precision of predictions modulates reaction times (Vossel et al, 2014), functional magnetic resonance imaging (fMRI) activity in fronto-parietal networks (Vossel et al, 2015), pupil size (Vincent et al, 2019), but with tasks and analyses that leave unclear whether the effect is genuinely about precision, or also about predictability. A similar and related confound exists between expectation and attention, which are often related in practice but distinct in theory (Summerfield and de Lange, 2014).…”

“…Here, we controlled specifically for this correlation by using the residual confidence in our analyses. This is important because many brain signals are associated with predictability (Friedman et al, 2001;Strange et al, 2005;Summerfield and de Lange, 2014;Vossel et al, 2015;Kok et al, 2017;Vincent et al, 2019).…”

From Allostatic Agents to Counterfactual Cognisers: Active Inference, Biological Regulation, and The Origins of Cognition

“…confidence about the hidden causes of sensory states), volatility can be construed as the (inverse) precision over transition probabilities (i.e. confidence about the way hidden states evolve over time; Parr and Friston 2017;Parr et al 2019;Sales et al 2019;Vincent et al 2019). This formulation suggests that volatile environments will tend to generate more surprising outcomes than stable environments, insofar as their states are apt to change in ways that are difficult to anticipate.…”

Perceptual awareness and active inference

“…This makes a strong empirical prediction, in that the time taken for the image to fade should be inversely related to measures of volatility (the imprecision of dynamics) that can be estimated through other experimental paradigms (Mathys et al 2014; Marshall et al 2016; Lawson et al 2017). Given the association between noradrenergic signalling and precision, it may also be possible to estimate these parameters from pupillary data (Koss 1986) on an individual basis (Vincent et al 2019). This additionally predicts that pharmacological manipulation of catecholamine signalling would systematically change the time until an image fades.…”