Cortical beta power reflects a neural implementation of decision boundary collapse in speeded decisions.

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 analyzed EEG beta power lateralization (BPL). The latter served as a neural proxy for decision signals. We analyzed a large dataset (n=863; 434 female and 429 male) from a speeded flanker task and data from an independent confirmation sample (n=119; 70 females and 49 male). 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, coinciding with the motor response, may serve as a neural proxy for the decision threshold. We show that BPL around the response decreased with increasing RTs. Together, our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes. Significance Statement This study uncovers compelling evidence to suggest that under high time pressure, dynamic decision thresholds drive the termination of decision-formation, as seen in the beta power lateralization (BPL) over the motor cortex during a speeded Flanker task. This is demonstrated by behavioral modelling, showing that subjects time-dependent decision policy is best captured in drift diffusion models that allow dynamic decision bounds and confirmed in the neural signal, whereby BPL over motor cortices reflects features of the modelled decision signal.