Catastrophic interference in human motor learning

Biological sensorimotor systems are not static maps that transform input (sensory information) into output (motor behavior). Evidence from many lines of research suggests that their representations are plastic, experience-dependent entities. While this plasticity is essential for exible behavior, it presents the nervous system with di cult organizational challenges. If the sensorimotor system adapts itself to perform well under one set of circumstances, will it then perform poorly when placed in an environment with di erent demands (negative transfer)? Will a later experience-dependent change undo the bene ts of previous learning (catastrophic interference)? We explore the rst question in a separate paper in this volume (Shadmehr et al. 1995). Here we present psychophysical and computational results that explore the question of catastrophic interference in the context of a dynamic motor learning task. Under some conditions, subjects show evidence of catastrophic interference. Under other conditions, however, subjects appear to be immune to its e ects. These results suggest that motor learning can undergo a process of consolidation. Modular neural networks are well suited for the demands of learning multiple input/output mappings. By incorporating the notion of fast- and slow-changing connections into a modular architecture, we were able to account for the psychophysical results.