Structure learning in action.

'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a 'learning to learn' mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system.

Unsteady transition in an axial-flow turbine. Part 1. Measurements on the turbine rotor

Previously published measurements in a low-speed, single-stage, axial-flow turbine have been reanalyzed in the light of more recent understanding. The measurements include time-resolved hot-wire traverses and surface hot film gage measurements at the midspan of the rotor suction surface with three different rotor-stator spacings. This paper investigates the suction surface boundary layer transition process, using surface-distance time plots and boundary layer cross sections to demonstrate the unsteady and two-dimensional nature of the process.