Cortical beta power reflects a neural implementation of decision boundary collapse in a speeded flanker task

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms that underly dynamic response thresholds remains very incomplete. To investigate this issue, we used a multistage drift diffusion model (DDM) and also analysed EEG beta power lateralization (BPL). The latter served as a neural proxy for decision signals. We analysed a large dataset (n=863) from a speeded flanker task and data from an independent confirmation sample (n=119). We show that a DDM with collapsing decision thresholds, a process where the decision boundary reduces over time, captured participants time-dependent decision policy better than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak may serve as a neural proxy for the decision threshold. Our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.