Trends in Neurosciences, Volume 29, Issue 1, 1 January 2006, Pages 58-64
John W. Krakauer and Reza Shadmehr
An issue of great recent interest is whether motor memory consolidates in a manner analogous to declarative memory that is, with the formation of a memory that progresses over time from a fragile state, which is susceptible to interference by a lesion or a conflicting motor task, to a stabilized state, which is resistant to such interference. Here, we first review studies that examine the anatomical basis for motor consolidation. Evidence implicates cerebellar circuitry in two types of associative motor learning eyelid conditioning and vestibulo-ocular reflex adaptation and implicates primary motor cortex in skilled finger movements. We also review evidence for and against a consolidation process for adaptation of arm movements. We propose that contradictions have arisen because consolidation can be masked by inhibition of memory retrieval.
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Trends in Cognitive Sciences, Volume 4, Issue 4, 1 April 2000, Pages 138-147
Raymond M. Klein
Immediately following an event at a peripheral location there is facilitation for the processing of other stimuli near that location. This is said to reflect a reflexive shift of attention towards the source of stimulation. After attention is removed from such a peripheral location, there is then delayed responding to stimuli subsequently displayed there. This inhibitory aftereffect, first described in 1984 and later labeled inhibition of return (IOR), encourages orienting towards novel locations and hence might facilitate foraging and other search behaviors. Since its relatively recent discovery, IOR has been the subject of intensive investigation, from many angles and with a wide variety of approaches. After describing the seminal contribution of Posner and Cohen (Who), this review will discuss what causes IOR and, once initiated, what effects IOR has on subsequent processing (What). The time course (When) and spatial distribution (Where) of IOR, and what is known about IORs neural implementation (How) and functional significance (Why) are also discussed.
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Trends in Cognitive Sciences, Volume 12, Issue 11, 1 November 2008, Pages 411-417
Ladan Shams and Aaron R. Seitz
Studies of learning, and in particular perceptual learning, have focused on learning of stimuli consisting of a single sensory modality. However, our experience in the world involves constant multisensory stimulation. For instance, visual and auditory information are integrated in performing many tasks that involve localizing and tracking moving objects. Therefore, it is likely that the human brain has evolved to develop, learn and operate optimally in multisensory environments. We suggest that training protocols that employ unisensory stimulus regimes do not engage multisensory learning mechanisms and, therefore, might not be optimal for learning. However, multisensory-training protocols can better approximate natural settings and are more effective for learning.
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Copyright 2009 Elsevier Ltd. All rights reserved.
Current Biology, Volume 19, Issue 4, 352-357, 12 February 2009
1 Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
2 Bernstein Center for Computational Neuroscience, Albert Ludwigs University, D-79104 Freiburg, Germany
3 Institute of Biology I, Albert Ludwigs University, D-79104 Freiburg, Germany
4 Institute of Biology III, Albert Ludwigs University, D-79104 Freiburg, Germany
When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1,2,3,4,5,6,7,8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9,10,11,12,13,14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning.