Craig P. Speelman and Kim Kirsner
- Published in print:
- 2005
- Published Online:
- January 2008
- ISBN:
- 9780198570417
- eISBN:
- 9780191708657
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198570417.003.0003
- Subject:
- Psychology, Cognitive Psychology
This chapter presents a number of challenges for research into skill acquisition and transfer. In particular, a range of factors are considered that can determine whether a skill will be ...
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This chapter presents a number of challenges for research into skill acquisition and transfer. In particular, a range of factors are considered that can determine whether a skill will be generalizable beyond the conditions of training, or specific to these conditions, including the nature of the task under consideration and the nature of the training programme. The effect of transfer of training on the shape of learning curves is also discussed, starting with a comprehensive review of the account provided in Anderson's ACT* theory of the determiners of learning rate. Consideration is then given to the effect on learning rate of performing a task that involves component skills with varying practice histories. A number of conclusions are stated about this effect: when a task involves old and new components, the task will be learned at a slower rate than that at which each of the two sets of components improves. The amount by which this learning rate will be attenuated will be moderated by the relative number of processing steps between old and new components of the task, and by the amount of practice that the old skills had prior to learning the new task. Learning curves that represent improved performance on a task are thus suggested to reflect summaries of learning curves of component skills. Some evidence in the research literature in support of these predictions is presented. The chapter concludes with a discussion of the influence context effects and individual differences can have on learning curves.Less
This chapter presents a number of challenges for research into skill acquisition and transfer. In particular, a range of factors are considered that can determine whether a skill will be generalizable beyond the conditions of training, or specific to these conditions, including the nature of the task under consideration and the nature of the training programme. The effect of transfer of training on the shape of learning curves is also discussed, starting with a comprehensive review of the account provided in Anderson's ACT* theory of the determiners of learning rate. Consideration is then given to the effect on learning rate of performing a task that involves component skills with varying practice histories. A number of conclusions are stated about this effect: when a task involves old and new components, the task will be learned at a slower rate than that at which each of the two sets of components improves. The amount by which this learning rate will be attenuated will be moderated by the relative number of processing steps between old and new components of the task, and by the amount of practice that the old skills had prior to learning the new task. Learning curves that represent improved performance on a task are thus suggested to reflect summaries of learning curves of component skills. Some evidence in the research literature in support of these predictions is presented. The chapter concludes with a discussion of the influence context effects and individual differences can have on learning curves.
Reza Shadmehr and Sandro Mussa-Ivaldi
- Published in print:
- 2012
- Published Online:
- August 2013
- ISBN:
- 9780262016964
- eISBN:
- 9780262301282
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262016964.003.0010
- Subject:
- Neuroscience, Research and Theory
This chapter presents a discussion on structural learning and identification of the structure of the learner. It reveals that the prior exposure to a rotation perturbation, despite being random and ...
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This chapter presents a discussion on structural learning and identification of the structure of the learner. It reveals that the prior exposure to a rotation perturbation, despite being random and unlearnable, seemed to significantly enhance learning rates for a member of the same perturbation class. It shows that the problem of structural learning is that of describing a dynamical system that in principle can accurately predict the sensory consequences of motor commands, that is, learn the structure of a forward model. This chapter suggests that Expectation Maximization is an alternate approach to estimating the structure of a linear dynamical system.Less
This chapter presents a discussion on structural learning and identification of the structure of the learner. It reveals that the prior exposure to a rotation perturbation, despite being random and unlearnable, seemed to significantly enhance learning rates for a member of the same perturbation class. It shows that the problem of structural learning is that of describing a dynamical system that in principle can accurately predict the sensory consequences of motor commands, that is, learn the structure of a forward model. This chapter suggests that Expectation Maximization is an alternate approach to estimating the structure of a linear dynamical system.
