Rapid Changes in Risk Preferences Originate from Bayesian Inference on Parietal Magnitude Representations

Abstract

AbstractRisk preferences – the willingness to accept greater uncertainty to achieve larger potential rewards – determine many aspects of our lives and are often interpreted as an individual trait that reflects a general ’taste’ for risk. However, this perspective cannot explain why risk preferences can change considerably across contexts and even across repetitions of the identical decisions. Here we provide modelling and neural evidence that contextual shifts and moment-to-moment fluctuations in risk preferences can emerge mechanistically from Bayesian inference on noisy magnitude representations in parietal cortex. Our participants underwent fMRI while choosing between safe and risky options that were either held in working memory or present on the screen. Risky options that were held in working memory were less likely to be chosen (risk aversion) when they had large payoffs but more likely to be chosen (risk-seeking) when they had small payoffs. These counterintuitive effects are mechanistically explained by a computational model of the Bayesian inference underlying the perception of the payoff magnitudes: Options kept in working memory are noisier and therefore more prone to central tendency biases, leading small (or large) payoffs to be overestimated (or underestimated) more. Congruent with the behavioural modelling, fMRI population-receptive field modelling showed that on trials where intraparietal payoff representations were noisier, choices were also less consistent and less risk-neutral, in line with participants resorting more to their prior belief about potential payoffs. Our results highlight that individual risk preferences and their puzzling changes across contexts and choice repetitions are mechanistically rooted in perceptual inference on noisy parietal magnitude representations, with profound implications for economic, psychological, and neuroscience theories of risky behaviour.