Capacity Limits Lead to Information Bottlenecks in Ongoing Rapid Motor Behaviors

Studies of ongoing, rapid motor behaviors 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 behaviors. 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.