Competition between parallel sensorimotor learning systems

Sensorimotor learning is supported by at least two parallel systems: A strategic process that benefits from explicit knowledge, and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error

Motor Learning and Consolidation: The Case of Visuomotor Rotation

Abstract Adaptation to visuomotor rotation is a particular form of motor learning distinct from force-field adaptation, sequence learning, and skill learn-ing. Nevertheless, study of adaptation to visuomotor rotation has yielded a number of findings and principles that are likely of general importance to procedural learning and memory. First, rotation learning is implicit and appears to proceed through reduction in a visual prediction error generated by a forward model, such implicit adaptation occurs even when it is in conflict with an explicit task goal. Second, rotation learning is subject to different forms of interference: retrograde, anterograde through aftereffects, and contextual blocking of retrieval. Third, opposite rotations can be recalled within a short time interval without interference if implicit contextual cues (effector change) rather than explicit cues (color change) are used. Fourth, rotation learning consolidates both over time and with increased initial training (saturation learning)