Why You Can't Shoot the Same Foul Shot Twice
In April 1993 Price missed what would have been his record-tying shot from the charity stripe; later that month, Michael Williams of the Minnesota Timberwolves would end up breaking the record in a game against the Utah Jazz. Most scientists, who over the past decade may have hypothesized why the all-time NBA career free-thrower (he made 90.4 percent of his shots) missed that crucial attempt, would likely have blamed the inconsistency of so-called on-line movements--the neural activity and muscle contractions that occur after Price bent his knees and started his motion. A new study in this week's issue of Neuron, however, reports that another factor came into play: the brain does not plan the execution of a shot in exactly the same fashion each step of the way.
"The punch line with [our] paper," says Stanford University electrical engineer and neuroscientist Krishna Shenoy, "is that this is the first evidence that neural activity--brain activity that happens well before the movement ever begins--has a lot to say about the variability or the exact movement that you're going to get."
Shenoy and his team studied two rhesus monkeys as they made a simple, practiced movement--reaching to touch a target--to determine whether so-called "off-line" activity had any effect on the variability of each movement. First, the monkeys were trained to make a quick reach when they saw a green target and to execute a faster motion when they saw a red target.
As the monkeys performed these tasks, the researchers studied individual neurons in the premotor cortex of their brains (the outer layer of the brain responsible for higher functions, such as movement planning) to see whether each nerve cell increased its activity for slower or faster reaches. Once each neuron had been catalogued, the team monitored them while the monkeys made a series of reaches, varying each motion's speed naturally.
"What we did is record that preparatory activity way before the movement ever begins," Shenoy says, "and show that you can predict whether the upcoming movement will be slightly faster or slightly slower on average." In fact, the team found that the off-line neural activity was highly predictive of the speed of each reach.
Next, the group attempted to estimate what percentage of variability in a motion can be attributed to neural activity at the planning stage. They tested muscle activity during the same reaching exercise using electromyography to determine how well on-line variability correlated with movement variability. To their surprise, the results were similar to those in their study of off-line effects. "The bottom line is the neural recordings can explain upcoming velocity variability as well as muscle recordings can," says Afsheen Afshar, a graduate student who worked on the study. He adds that off-line activity probably accounts for half of movement variability, whereas on-line effects influence the other half.
Not all experts agree, however. Emmanuel Todorov, a cognitive scientist at the University of California, San Diego, is skeptical that motor preparation is a major source of variability in physical motions. He says that the tasks performed were too simple to reach definitive conclusions and that more difficult activities (like crumpling paper), which require some sensory guidance once the motion has begun, would rely less on planning. "We should be careful not to overgeneralize," he warns. "The relative contributions of different neuronal mechanisms are likely to depend on the nature of the behavior."
Paul Cisek, a neurophysiologist at the University of Montreal, says Shenoy's work is important to the study of motion control, as consideration of the influence of off-line activity had all but been forgotten in recent years. "The precise fraction of influence that planning processes have on movement variability is not easy to calculate, but probably not terribly important," he notes. "Knowing that it is there and that it is not negligible is important, whether it is 30 percent, 50 percent or 70 percent. It suggests that computational models of motor control need to take planning variability into account."