Spectral properties of human cognition and skill

 Many interactive human skills are based on real-time error detection and correction. Here we investigate the spectral properties of such skills, focusing on a synchronization task. A simple autoregressive error correction model, based on separate ‘motor’ and ‘cognitive’ sources, provides an excellent fit to experimental spectral data. The model can also apply to recurrent processes not based on error correction, allowing commentary on previous claims of 1/ f-type noise in human cognition. A comparison of expert and non-expert subjects suggests that performance skill is not only based on reduced variance and bias, but also on the construction of richer mental models of error correction.