July 2013
Volume 13, Issue 9
Vision Sciences Society Annual Meeting Abstract  |   July 2013
The 50s cliff: Perceptuo-Motor Learning Rate Across the Lifespan
Author Affiliations
  • Rachel Coats
    Centre for Sport and Exercise Sciences, University of Leeds
  • Andrew Wilson
    Department of Psychology, Leeds Metropolitan University
  • Winona Snapp-Childs
    Department of Psychological and Brain Sciences, Indiana University
  • Aaron Fath
    Department of Psychological and Brain Sciences, Indiana University
  • Geoffrey Bingham
    Department of Psychological and Brain Sciences, Indiana University
Journal of Vision July 2013, Vol.13, 483. doi:https://doi.org/10.1167/13.9.483
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      Rachel Coats, Andrew Wilson, Winona Snapp-Childs, Aaron Fath, Geoffrey Bingham; The 50s cliff: Perceptuo-Motor Learning Rate Across the Lifespan. Journal of Vision 2013;13(9):483. https://doi.org/10.1167/13.9.483.

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      © ARVO (1962-2015); The Authors (2016-present)

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Introduction Many perceptuo-motor tasks require coordinated rhythmic movements of different limbs. People can only produce two stable coordination patterns without training: 0[sup]0[/sup] and 180[sup]0[/sup]. Others (e.g. 90[sup]0[/sup]) usually have to be learned. Surprisingly, there are no major studies of such learning across the lifespan. In 2010, we presented data from 20, 70 and 80 year olds. Here we present data from all decades from 20 to 80. Method ~10 participants in each decade from 20s through 80s took part. Two vertically displaced white dots appeared in a display against a black background. The computer controlled the top dot and the participant the bottom one using a joystick. The target coordinative pattern was displayed before participants attempted to produce it. There were three assessment sessions (Baseline, Post-training, Retention) each containing 12 trials (4 x 0[sup]0[/sup], 180[sup]0[/sup] and 90[sup]0[/sup]) and five training sessions (each 10 x 90[sup]0[/sup]). During training, feedback was provided by changing the dots from white to green when the movement was at 90[sup]0[/sup]± an error band that decreased as performance improved. Results All groups improved with training and all retained it except 60 year olds. An exponential model was fit to learning curves using two methods to yield convergent measures of learning rates that were found to decrease with age with modest decline until a steep drop after 50. Statistical analyses confirmed this picture. Overall, learning rates dropped by half. Conclusions Although adults over 60 learn, they do so at half the rate. Programs for recovery from stroke and other conditions should respect these reduced yet effective learning rates. Multiple factors are likely responsible for the learning deficit that comes with age, but motion perception is likely to be a key factor in this case, and the 50s is the crucial decade.

Meeting abstract presented at VSS 2013


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