I won’t be writing about every chapter of this book, but this is the first of my posts about books I’m reading, as I attempt to find some coherence in my reading which tends to involve being halfway through 10 books at the same time. Chapter 2, “The Movement Chauvanist”, heavily features Dan Wolpert, whose work I’m very interested in, and the first chapter closes with links to his work having mentioned the need for prediction during movement in earlier parts of the chapter. The author Zach Schonbrun focuses on the people doing the science as well as the science, and the athletes too of course. Here are a few highlights from the first chapter of The Performance Cortex.
Baseball is the discussed in depth, with a project called deCervo at the centre – an attempt by some guys from Paul Sajda’s lab to identify the neural correlates of expert performance. Amazing vision has often been attributed as one of the distinguishing factors in baseball, with lots of professionals have above-average vision (not infrequently as good as 20-10). But it appears more of a prerequisite, with no relationship amongst professionals between the best vision and the best performance. In fact, a study is mentioned where cricket players wore contact lenses that blurred vision to a level that approached legal blindness but were still able to hit a ball bowled at them. Instead, EEG was used as part of deCervo’s work to collect neurophysiological data that could identify the best performers.
Jason Sherwin ran a cool experiment comparing 5 expert cellists and 5 non-musicians during a task that required them to pick out pitch-shifted anomalies during the playback of a classical song. Both groups were good (scored over 80%), but the cellists were better. The groups also differed in brain activity recorded with EEG; among the differences between groups was increased activity in the right-lateralised motor cortex that would correspond to the cellists note-playing hand, despite there being no actual movement during the task. Sherwin argues this is evidence of embodied cognition and it leads nicely into the idea of predictions from an internal forward model being used in error detection, something I’m working on (stronger predictive signal may increase error detection and with it, motor learning).
There is some interesting discussion on Michael Jordan’s foray into baseball, which despite not reaching the top level, was actually quite impressive given his lack of experience in the sport. But there’s actually a drill I want to highlight and finish this post with. Someone called Daniel Laby devised a training exercise to be completed by a baseball batter Manny Ramirez, which involved throwing a frisbee with “wiffle balls” attached to it. I had to look up what a wiffle ball is – it seems to be a lightweight plastic ball with air holes through it which slow down its flight. Ramirez was tasked with catching the frisbee, but only by grabbing one of the balls and not the frisbee. The drill actually developed so that the frisbee was embedded in four balls of different colours, of which one was shouted out mid-flight, demanding the athlete catch the frisbee by the ball of only that colour. (It was developed further again by swapping out the coloured balls for balls with different seams relating to different pitches in baseball.)