Effect of overlearning on retention.

Driskell, James E.; Willis, Ruth P.; Copper, Carolyn

doi:10.1037/0021-9010.77.5.615

Cited by 246 publications

(194 citation statements)

References 35 publications

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“…However, the over learning effect has been demonstrated on a wide variety of learning paradigms including motor skill learning (e.g., disassembling and assembling a device) (Melnick 1971;Melnick et al 1972;Hagman 1982, 1991). Tests for retention given after a set time interval posttraining (for example, 24 h) typically revealed that the greater the degree of over learning, the greater the retention of the task (Craig et al 1972;Driskell et al 1992;Postman 1962;Rohrer et al 2005). Despite the abundance of research, previous models of over learning (Craig et al 1972;Schendel and Hagman 1991) predict neither the rate nor the maximal amount of improvement in retention conferred by increased training.…”

Section: Introductionmentioning

confidence: 97%

“…For example, if reaching a high level of performance (typically set as 1 error-free trial, although this does not imply that the subject achieved perfect performance in subsequent trials) required 10 trials, 50% over learning requires performing an additional 5 trials. Initial studies focused on verbal recall (memorizing lists of syllables or nouns) (Krueger 1929;Luh 1922) on which most of the work to date has been done (for a review, Driskell et al 1992). However, the over learning effect has been demonstrated on a wide variety of learning paradigms including motor skill learning (e.g., disassembling and assembling a device) (Melnick 1971;Melnick et al 1972;Hagman 1982, 1991).…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Retention Explained by a Model of Short-Term Learning in the Adaptive Control of Reaching

Joiner¹,

Smith

2008

Journal of Neurophysiology

174

203

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Joiner WM, Smith MA. Long-term retention explained by a model of short-term learning in the adaptive control of reaching. J Neurophysiol 100: 2948 -2955, 2008. First published September 10, 2008 doi:10.1152/jn.90706.2008. Extensive theoretical, psychophysical, and neurobiological work has focused on the mechanisms by which short-term learning develops into long-term memory. Better understanding of these mechanisms may lead to the ability to improve the efficiency of training procedures. A key phenomenon in the formation of long-term memory is the effect of over learning on retentiondiscovered by Ebbinghaus in 1885: when the initial training period in a task is prolonged even beyond what is necessary for good immediate recall, long-term retention improves. Although this over learning effect has received considerable attention as a phenomenon in psychology research, the mechanisms governing this process are not well understood, and the ability to predict the benefit conveyed by varying degrees of over learning does not yet exist. Here we studied the relationship between the duration of an initial training period and the amount of retention 24 h later for the adaptation of human reaching arm movements to a novel force environment. We show that in this motor adaptation task, the amount of long-term retention is predicted not by the overall performance level achieved during the training period but rather by the level of a specific component process in a multi-rate model of short-term memory formation. These findings indicate that while multiple learning processes determine the ability to learn a motor adaptation, only one provides a gateway to long-term memory formation. Understanding the dynamics of this key learning process may allow for the rational design of training and rehabilitation paradigms that maximize the long-term benefit of each session.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Long-Term Retention Explained by a Model of Short-Term Learning in the Adaptive Control of Reaching

Joiner¹,

Smith

2008

Journal of Neurophysiology

174

203

View full text Add to dashboard Cite

show abstract

“…A meta-analysis revealed that overlearning has a significant moderate overall effect independent of task type and retention interval (Driskell et al 1992). The effects of overlearning are stronger for cognitive tasks than for physical tasks.…”

Section: Theoretical Issues In Ergonomics Sciencementioning

confidence: 99%

“…Overlearning refers to the deliberate overtraining in a task after the learner has reached a set criterion (Driskell et al 1992). Overlearning is a prolongation of…”

Section: Cognitive Apprenticeshipmentioning

confidence: 99%

Designing training for process control simulators: a review of empirical findings and current practices

Kluge

Sauer

Schüler

et al. 2009

Theoretical Issues in Ergonomics Science

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Although simulators have been widely used as training environments in different industries (e.g. oil and nuclear power), there is little rigorous empirical work evaluating the effectiveness of the training methods employed. This article examines the use of simulator training in process control environments. The results of an exploratory field study are reported and the current practices of simulator training are described. The study revealed that simulator training varied considerably across organisations, often with little theoretical or empirical work to guide training design. To evaluate the utility and effectiveness of different methods of simulator training in process control environments, the article also presents a literature review of the research on laboratory-and field-based training. Several training methods are identified as having particular potential for temporal and adaptive transfer and are to be empirically tested in future studies.

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“…Karpicke and Roediger 2008). Indeed, according to a meta-analysis conducted by Driskell et al (1992), overlearning produces a positive effect of moderate magnitude on recall performance. However, the meta-analysis also showed that the positive effect of overlearning depends on the retention interval, the amount of overlearning, and the type of task.…”

Section: Dropout Learningmentioning

confidence: 99%

Individual differences in criterion-based dropout learning in old age: the role of processing speed and verbal knowledge

Kurtz

Zimprich

2013

Eur J Ageing

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In the present study, individual differences in criterion-based dropout learning in old age were investigated. The predictive role of processing speed and verbal knowledge for individual differences in this type of learning was analyzed. Learning trajectories were modeled using a multilevel approach based on N = 47 older participants (M age = 69.3, SD = 6.4). Participants learned five lists of eight cue-target word pairs across six study-test cycles maximally possible. Results indicate that there were reliable individual differences in the initial level, speed of learning, and quadratic growth of learning. After adjusting for age-related effects, both higher verbal knowledge and higher processing speed had a positive effect on speed of learning. Additionally, verbal knowledge had a negative effect on quadratic growth of learning. Speed of learning and quadratic growth had a positive effect on a delayed recall test after adjusting for effects of processing speed and verbal knowledge. These results emphasize the role of cognitive abilities for individual differences in criterionbased dropout learning in old age.

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Effect of overlearning on retention.

Cited by 246 publications

References 35 publications

Long-Term Retention Explained by a Model of Short-Term Learning in the Adaptive Control of Reaching

Long-Term Retention Explained by a Model of Short-Term Learning in the Adaptive Control of Reaching

Designing training for process control simulators: a review of empirical findings and current practices

Individual differences in criterion-based dropout learning in old age: the role of processing speed and verbal knowledge

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