The Effect of Intolerance of Uncertainty on Indecisiveness in Anxiety and Obsessive-Compulsive Disorders.

Decision-making processes are profoundly influenced by the context in which options are presented. The perceived value of an option is often shaped not solely by its intrinsic attributes but also in relation to other available alternatives. For instance, winning second place in a contest might feel rewarding until one realizes there were only two participants. This context-dependent valuation is a well-documented phenomenon, observed in both value-based decision-making scenarios, where option values are explicitly known, and in reinforcement learning (RL), where values are acquired through trial and error. This influence of context on value perception may serve as an adaptive mechanism, enabling organisms to make efficient decisions under neurobiological and environmental constraints. However, this same mechanism can also lead to systematic biases and seemingly irrational behaviors, particularly when choices are presented outside of their original learning context. Typically, decision-makers tend to prefer the option that was most favorable within its original learning context, even when another option has a higher absolute value but was less favorable within its own context. This behavior, which has been observed in humans and various other species, suggests that decision-makers prioritize options based on their relative value within their learning contexts, rather than their absolute value, sometimes leading to sub-optimal choices in new environments. One prominent computational framework that has been proposed to account for context-dependent valuation is range normalization. According to this model, the subjective value of an option is rescaled based on the minimum and maximum values present in a given context. While range normalization successfully predicts subjective values in simple binary choice scenarios, it encounters limitations when additional options are introduced. For example, when participants are presented with three options of evenly spaced values, the pure range normalization model predicts that the subjective value of the middle option should fall exactly halfway between the lowest and highest options. However, empirical evidence contradicts this prediction, indicating that participants tend to perceive the mid-value option as being close to the lowest one. This discrepancy indicates the presence of a non-linear bias in valuation processes, suggesting that the range model does not fully capture the cognitive mechanisms underlying subjective valuation. To address this limitation, Bavard and Palminteri introduced a weighted range normalization model, incorporating a power-transformation parameter to capture non-linearities in valuation. This refinement improves the model’s predictive accuracy but remains a descriptive account, without addressing the cognitive mechanisms underlying these valuation processes. In this study, we hypothesize that the functional form of outcome normalization could stem from attentional mechanisms that bias evidence accumulation as a function of the outcomes and expected values of available options. This hypothesis is informed by a growing body of research that has highlighted the significant role of attention in both value-based decision-making and reinforcement learning. Prior studies in reinforcement learning have demonstrated a bidirectional interaction between selective attentional processes and learning, where high-value features attract attention and, in turn, attention influences how value is attributed to different features. Here, we propose that the bidirectional interaction between attention and learning may account for the non-linearities observed in context-dependent valuation. Specifically, we suggest that the more attention paid to an option, the closer its subjective value will align with its range normalized value.