Deciding with muscles

Sequential sampling models have a rich tradition in psychology, and play a central role in our understanding of human decision-making. According to these models, decisions arise from the gradual accumulation of noisy evidence over time until a decision threshold is reached. This framework accounts for both choice behavior and reaction times, and is further supported by neurophysiological evidence: neural activity in various brain regions exhibits accumulation-to-bound dynamics consistent with model predictions. Recent empirical findings extend this view, revealing that similar accumulation-like signals can also be observed in the electrical activity of muscle fibers, suggesting that decision signals may propagate continuously from perceptual processing through motor execution. This raises a fundamental theoretical question: How are actions requiring substantial muscular effort initiated when the quality of sensory evidence is low or when individuals are under time pressure? We hypothesized that evidence-independent urgency signals are necessary to drive muscle activation via a continuous flow of information in such contexts. To formalize this hypothesis, we extended a recent computational framework —the gated cascade diffusion model— which links decision and motor processes. We tested this model extension against behavioral and neuromuscular data from two experiments in which sensory evidence quality, required response force, and speed pressure were systematically manipulated. Model fits and formal comparisons with alternative accounts provided strong support for our hypothesis. These findings enhance our understanding of the interface between decision-making and motor systems, particularly in contexts that require effortful responses, which are common in everyday behavior.