Different Varieties of Uncertainty in Human Decision-Making

Abstract

The study of uncertainty in decision-making is receiving greater attention in the fields of cognitive and computational neuroscience. Several lines of evidence are beginning to elucidate different variants of uncertainty. Particularly, risk, ambiguity, and expected and unexpected forms of uncertainty are well articulated in the literature. In this article we review both empirical and theoretical evidence arguing for the potential distinction between three forms of uncertainty; expected uncertainty, unexpected uncertainty, and volatility. Particular attention will be devoted to exploring the distinction between unexpected uncertainty and volatility which has been less appreciated in the literature. This includes evidence mainly from neuroimaging, neuromodulation, and electrophysiological studies. We further address the possible differentiation of cognitive control mechanisms used to deal with these forms of uncertainty. Finally, we explore whether the dual modes of control theory provides a theoretical framework for understanding the distinction between unexpected uncertainty and volatility.

Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty

“…Adaptive decision making relies on estimating the reward values of objects or actions which have to be constantly updated, since those values can unpredictably change over time in an uncertain world (Bland and Schaefer, 2012; Courville et al, 2006; Mathys et al, 2011; O'Reilly, 2013). There are two problems at the heart of this estimation, depending on the model used to tackle reward under uncertainty.…”

“…First, there is a tradeoff between having an accurate estimate of reward values and being able to quickly update those values due to changes in the environment (adaptability-precision tradeoff). Second, estimating uncertainty is very challenging without a proper model of the environment, but such estimation is the foundation upon which alternative models of the environment could be built (Bland and Schaefer, 2012; Courville et al, 2006; O'Reilly, 2013). …”

Confidence as Bayesian Probability: From Neural Origins to Behavior

“…The Bayesian view provides a normative account for the updating process, indicating how, based on uncertainty, prior knowledge and new observations should be combined during learning. Optimal algorithms, such as the Kalman filter, developed by engineers in the 1960s form a foundation for modern accounts of learning in cognitive science and neuroscience (Bach and Dolan, 2012;Bland and Schaefer, 2012;Daunizeau et al, 2010;Mathys et al, 2011;Nassar et al, 2010;Payzan-LeNestour and Bossaerts, 2011;Preuschoff and Bossaerts, 2007). Essentially, the more confident we are in a new observation (e.g., because the stimulus is clear), the more this observation should impact our prior knowledge.…”

Prior probability and feature predictability interactively bias perceptual decisions

“…Compared to stimuli with more ambiguous features, located closer to the category boundary, stimuli represented further away were categorized more rapidly due to a higher drift-rate on decision evidence (i.e., faster rate of evidence accumulation). However, it remains unclear how prior knowledge influences this relationship and how uncertainties in predicted and observed stimulus features interact during perceptual decision-making (see Bland & Schaefer, 2012 for review).…”