The Perceptual-Motor Workspace and the Acquisition of Skill

Newell, K. M.; McDonald, P. Vernon; Kügler, P.

doi:10.1007/978-94-011-3626-6_9

Cited by 27 publications

(23 citation statements)

References 12 publications

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“…As was shown in K. M. Newell et al (2001), the averaging effect can mask the exponential changes of the data, therefore suggesting a power-law-like behavior. However, when the nonlinear regression method was applied in the current analysis, the averaging effect vanished and the results showed a strong evidence of exponential learning behavior with a single time scale.…”

Section: Discussionmentioning

confidence: 88%

“…M. Newell et al (2001) predicted on the basis of theoretical arguments that the function of change for an individual learning a single-limb positioning or timing task will be an exponential function because that is the rate of change of growtwdecay functions relaxing to a fixed point with no Y. -T. Liu, G. Mayer-Kress, & K. M. Newell reorganization of the dynamical landscape (Kaplan & Glass, 1995).…”

Section: The Pennsylvania State Universitymentioning

confidence: 98%

“…M. Newell et al (2001) have hypothesized that averaging can change the time scale of the observed change in performance over time and hence inferences about the appropriate learning function. The unidimensional timing data originally reported in K. M. Newell, Liu, and Mayer-Kress (1997) on the sequential properties of learning are examined here in relation to the exponential and power-law functions of learning.…”

Section: The Pennsylvania State Universitymentioning

confidence: 99%

“…Finally, as another check on the distinctive feature of the functions, we took the exponents from the curve fitting of the data to produce simulations of the learning function. Our assessment of those distinctive properties was theoretically motivated (K. M. Newell et al, 2001); it allows one to go beyond curve fitting and inferences based solely on the percentage of variation accounted for.…”

Section: The Pennsylvania State Universitymentioning

confidence: 99%

“…To provide an assessment of the exponential fit to the learning data, we calculated the proportion of error change (R,,) from trial to trial (K. M. Newell et al, 2001;K. M. Newell, McDonald, & Kugler, 1991; also see Heathcote et al, 2000).…”

Section: Participant A)mentioning

confidence: 99%

See 4 more Smart Citations

Beyond Curve Fitting: A Dynamical Systems Account of Exponential Learning in a Discrete Timing Task

Liu

Mayer‐Kress

Newell

2003

Journal of Motor Behavior

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The authors examined the function for learning a discrete timing task from a dynamical systems perspective rather than solely the traditional curve-fitting viewpoint. Adult participants (N = 8) practiced a single-limb angular movement task of 125 ms over 20 degrees for 200 trials. There was no significant difference in percentage of variance accounted for in 3 parameter exponential and power-law nonlinear fits to the individual and averaged data. The percentage of variance increased in both exponential and power-law equations when the data were averaged over participants and trials. Drawing on a dynamical systems approach to time scales in motor learning and on analysis of the distinctive features of exponential and power-law functions, however, the authors conclude that the exponential is the learning function for that task and that level of practice.

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Section: Discussionmentioning

confidence: 88%

Section: The Pennsylvania State Universitymentioning

confidence: 98%

Section: The Pennsylvania State Universitymentioning

confidence: 99%

Section: The Pennsylvania State Universitymentioning

confidence: 99%

Section: Participant A)mentioning

confidence: 99%

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Beyond Curve Fitting: A Dynamical Systems Account of Exponential Learning in a Discrete Timing Task

Liu

Mayer‐Kress

Newell

2003

Journal of Motor Behavior

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Learning new perception–action solutions in virtual ball bouncing

Morice¹,

Siegler²,

Bardy

et al. 2007

Exp Brain Res

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How do humans discover stable solutions to perceptual-motor tasks as they interact with the physical environment? We investigate this question using the task of rhythmically bouncing a ball on a racket, for which a passively stable solution is defined. Previously, it was shown that participants exploit this passive stability but can also actively stabilize bouncing under perceptual control. Using a virtual ball-bouncing display, we created new behavioral solutions for rhythmic bouncing by introducing a temporal delay (45 degrees -180 degrees ) between the motion of the physical racket and that of the virtual racket. We then studied how participants searched for and realized a new solution. In all delay conditions, participants learned to maintain bouncing just outside the passively stable region, indicating a role for active stabilization. They recovered the approximate initial phase of ball impact in the virtual racket cycle (half-way through the upswing) by adjusting the impact phase with the physical racket. With short delays (45 degrees , 90 degrees ), the impact phase quickly shifted later in the physical racket upswing. With long delays (135 degrees , 180 degrees ), bouncing was destabilized and phase was widely visited before a new preferred phase gradually emerged, during the physical downswing. Destabilization was likely due to the loss of spatial symmetry between the ball and physical racket motion at impact. The results suggest that new behavioral solutions may be discovered and stabilized through broad irregular sampling of variable space rather than through a systematic search.

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Beyond animated skeletons: How can biomechanical feedback be used to enhance sports performance?

Glazier

2021

Journal of Biomechanics

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The Perceptual-Motor Workspace and the Acquisition of Skill

Cited by 27 publications

References 12 publications

Beyond Curve Fitting: A Dynamical Systems Account of Exponential Learning in a Discrete Timing Task

Beyond Curve Fitting: A Dynamical Systems Account of Exponential Learning in a Discrete Timing Task

Learning new perception–action solutions in virtual ball bouncing

Beyond animated skeletons: How can biomechanical feedback be used to enhance sports performance?

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