Abstract
Excessive information sampling in psychiatric patients characterized by high trait anxiety has been inconsistently linked with alterations in inferential and valuation processes. Methodological limitations could account in part for these inconsistencies. To address this, computational models of inference and valuation were applied to data collected from a transdiagnostic sample of adults with and without an anxiety or compulsive disorder using a version of the beads task with enhanced experimental controls. Participants diagnosed with an anxiety or compulsive disorder (n = 35) and healthy controls (n = 23) completed the beads task with three majority-to-minority ratios of blue versus green beads (60:40, 75:25, 90:10). First, a Bayesian belief-updating model was fit to quantify the iterative process by which new information (bead color) and prior beliefs were integrated to influence current beliefs about jar identity. Next, a parameterized partially observable Markov decision process model was used to parse the contribution of value-based decisions to sampling behavior and included a relative subjective cost parameter, C(sub) , for each bead-ratio condition. Higher trait anxiety was associated with more draws-to-decision, most robustly in the 90:10 bead-ratio condition. Only relative subjective cost of sampling decisions, and not inferential differences in weighting of new or old information, satisfactorily accounted for this relation. Specifically, lower C(sub)(0.9) was associated with more trait anxiety and more draws-to-decision. In a condition with high objective evidence strength, transdiagnostic trait-anxiety-related increases in information sampling were explained by a cost-benefit analysis where relatively higher subjective cost was assigned to an incorrect guess, highlighting valuation as a potential treatment target for future research.