Limited generalization with varied, as compared to specific, practice in short-term motor learning

Willey, Chéla R.; Liu, Zili

doi:10.1016/j.actpsy.2017.11.008

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…If, in addition to the results of the two experimental groups RG and DG, we take into account the results of the control group CG for analysis, we can notice a relation to both the “specificity–versus–generalization” problem [ 68 , 69 ] and the resonance problem [ 18 , 19 , 70 ], which, strictly speaking, can be considered as the same problem on different scales. Whereas the specificity–versus–generalization problem is binary coded (specific/general), the stochastic resonance with its optimum function is at first sight ternary coded (too much/exact/too little).…”

Section: Discussionmentioning

confidence: 99%

Repetition without Repetition or Differential Learning of Multiple Techniques in Volleyball?

Apidogo

Burdack

Schöllhorn

2021

IJERPH

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A variety of approaches have been proposed for teaching several volleyball techniques to beginners, ranging from general ball familiarization to model-oriented repetition to highly variable learning. This study compared the effects of acquiring three volleyball techniques in parallel with three approaches. Female secondary school students (N = 42; 15.6 ± 0.54 years) participated in a pretest for three different volleyball techniques (underhand pass, overhand pass, and overhead serve) with an emphasis on accuracy. Based on their results, they were parallelized into three practice protocols, a repetitive learning group (RG), a differential learning group (DG), and a control group (CG). After a period of six weeks with 12 intervention sessions, all participants attended a posttest. An additional retention test after two weeks revealed a statistically significant difference between DG, RG, and CG for all single techniques as well as the combined multiple technique. In each technique—the overhand pass, the underhand pass, the overhand service, and the combination of the three techniques—DG performed best (each p < 0.001).

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Section: Discussionmentioning

confidence: 99%

Repetition without Repetition or Differential Learning of Multiple Techniques in Volleyball?

Apidogo

Burdack

Schöllhorn

2021

IJERPH

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“…We are hardly the first to report a small to null effect of varied training on transfer performance. Previous studies have also reported a null effect (Van Rossum, 1990;Tuitert et al, 2017), or a limited effect of varied training on transfer (Brady, 2008;Cardis et al, 2018;Newell, 2003;Willey & Liu, 2018). However, it is possible that the amount of variation in acceleration is overshadowed by the inherent amount of variability that players experience even when playing games with the same acceleration, thus nullifying any benefit from being having practiced multiple accelerations.…”

Section: Behavioral Resultsmentioning

confidence: 94%

A decay-based account of learning and adaptation in complex skills.

Seow¹,

Betts²,

Anderson³

2021

Journal of Experimental Psychology: Learning, Memory, and Cogni

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How do humans adapt to parametric changes in a task without having to learn a new skill from scratch? Many studies of memory and sensorimotor adaptation have proposed theories that incorporate a decay on prior events, which leads the agent to eventually forget old experiences. This study investigates if a similar decay mechanism can account for human adaptation in complex skills that require the simultaneous integration of cognitive, motor, and perceptual processes. In 2 experiments, subjects learned to play a novel racing video game while adapting to parametric changes in the physics of the game's controls. Human learning and performance were modeled using the ACT-R cognitive architecture, which has been used successfully to model learning and fluency across a wide range of skills in prior research. Anderson et al. ( 2019) introduced the Controller module, a new component of the architecture that learns the setting of control parameters for actions and allows the agent to execute the rapid and precise actions that are necessary for good performance on complex tasks. Model simulations support including a moderate time-based decay on the weight of the experiences that the Controller uses. This is implemented in the Controller module by discounting the influence of older observations which helps the agent to focus on recent experiences that better reflect the current relationship between different settings of a control parameter and the rate of payoff from using that setting.

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“…This allows a test under realistic conditions, as they occur e.g. in psychophysics tasks as time reproduction [39], throwing [40,38], lifting or walking tasks [11,41]. These values can now be thought of as target values that a subject tries to achieve with her actions and can be interpreted on trial by trial and subject by subject level.…”

Section: Inferring Behavioral Parameters From Synthetic Datamentioning

confidence: 99%

Inferring perceptual decision making parameters from behavior in production and reproduction tasks

Neupärtl¹,

Rothkopf²

2021

Preprint

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Bayesian models of behavior have provided computational level explanations in a range of psychophysical tasks. One fundamental experimental paradigm is the production or reproduction task, in which subjects are instructed to generate an action that either reproduces a previously sensed stimulus magnitude or achieves a target response. This type of task therefore distinguishes itself from other psychophysical tasks in that the responses are on a continuum and effort plays an important role with increasing response magnitude. Based on Bayesian decision theory we present an inference method to recover perceptual uncertainty, response variability, and the cost function underlying human responses. Crucially, the cost function is parameterized such that effort is explicitly included. We present a hybrid inference method employing MCMC sampling utilizing appropriate proposal distributions and an inner loop utilizing amortized inference with a neural network that approximates the mode of the optimal response distribution. We show how this model can be utilized to avoid unidentifiability of experimental designs and that parameters can be recovered through validation on synthetic and application to experimental data. Our approach will enable behavioral scientists to perform Bayesian inference of decision making parameters in production and reproduction tasks.

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Limited generalization with varied, as compared to specific, practice in short-term motor learning

Cited by 6 publications

References 24 publications

Repetition without Repetition or Differential Learning of Multiple Techniques in Volleyball?

Repetition without Repetition or Differential Learning of Multiple Techniques in Volleyball?

A decay-based account of learning and adaptation in complex skills.

Inferring perceptual decision making parameters from behavior in production and reproduction tasks

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