Capacity limits lead to information bottlenecks in ongoing rapid motor behaviours.

Studies of ongoing, rapid motor behaviours have often focused on the decision-making implicit in the task. Here, we instead study how decision-making integrates with the perceptual and motor systems and propose a framework of limited-capacity, pipelined processing with flexible resources to understand rapid motor behaviours. Results from three experiments show that human performance is consistent with our framework: participants perform objectively worse as task difficulty increases and, surprisingly, this drop in performance is largest for the most skilled performers. As well, our analysis shows that the worst-performing participants can perform equally well under increased task demands, which is consistent with flexible neural resources being allocated to reduce bottleneck effects and improve overall performance. We conclude that capacity limits lead to information bottlenecks and that processes like attention help reduce the effects that these bottlenecks have on maximal performance. Significance Statement Behaviour results from perception, decision-making, and motor control. These processes can be modelled serially, but we must often decide about future actions in parallel with ongoing actions. We use computational models to demonstrate that simply perceiving, deciding, and moving in parallel inevitably produces performance-limiting interference. We see that the best performers in rapid motor behaviour tasks are counter-intuitively the most affected by increasing task demands, which is consistent with sharing attention between pipelined processes.