Trial-by-Trial Transformation of Error Into Sensorimotor Adaptation Changes With Environmental Dynamics

Fine, Michael; Thoroughman, Kurt A.

doi:10.1152/jn.00196.2007

Cited by 117 publications

(71 citation statements)

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“…Our results demonstrate that these analyses should at least be supplemented with more detailed temporal analyses that directly quantify how fluctuations on each trial are corrected on the next Gates and Dingwell 2008). This is consistent with many studies that highlight the importance of such trial-to-trial analyses (Cheng and Sabes 2007;Fine and Thoroughman 2007;Scheidt et al 2001;Smith et al 2006;Thoroughman et al 2007;van Beers 2009;Verstynen and Sabes 2011).…”

Section: Discussionsupporting

confidence: 88%

“…In repetitive tasks including reaching, each repetition of a movement is strongly influenced by the immediately preceding movement Scheidt et al 2001) but not by movements farther in the past (Scheidt et al 2001). Such findings are consistent with many accepted models of single-step trial-to-trial learning (Cheng and Sabes 2007;Fine and Thoroughman 2007;Smith et al 2006;van Beers 2009;Verstynen and Sabes 2011). Therefore, to directly quantify the influence of each reaching movement on the subsequent movement, we modeled each time series as…”

Section: Methodssupporting

confidence: 80%

“…Autocorrelation-based models have shown strong dependence of each consecutive movement on the immediately preceding movement for both reaching (Gates and Dingwell 2008;Scheidt et al 2001) and walking . Indeed, there is a substantial literature indicating that such trial-to-trial analyses are essential to gaining a comprehensive understanding of motor learning and motor control processes (Cheng and Sabes 2007;Fine and Thoroughman 2007;Smith et al 2006;Thoroughman et al 2007;van Beers 2009;Verstynen and Sabes 2011).…”

mentioning

confidence: 99%

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Trial-to-trial dynamics and learning in a generalized, redundant reaching task

Dingwell

Smallwood

Cusumano

2013

Journal of Neurophysiology

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Dingwell JB, Smallwood RF, Cusumano JP. Trial-to-trial dynamics and learning in a generalized, redundant reaching task. J Neurophysiol 109: 225-237, 2013. First published October 10, 2012 doi:10.1152/jn.00951.2011.-If humans exploit task redundancies as a general strategy, they should do so even if the redundancy is decoupled from the physical implementation of the task itself. Here, we derived a family of goal functions that explicitly defined infinite possible redundancies between distance (D) and time (T) for unidirectional reaching. All [T, D] combinations satisfying any specific goal function defined a goal-equivalent manifold (GEM). We tested how humans learned two such functions, D/T ϭ c (constant speed) and D·T ϭ c, that were very different but could both be achieved by neurophysiologically and biomechanically similar reaching movements. Subjects were never explicitly shown either relationship, but only instructed to minimize their errors. Subjects exhibited significant learning and consolidation of learning for both tasks. Initial error magnitudes were higher, but learning rates were faster, for the D·T task than for the D/T task. Learning the D/T task first facilitated subsequent learning of the D·T task. Conversely, learning the D·T task first interfered with subsequent learning of the D/T task. Analyses of trial-to-trial dynamics demonstrated that subjects actively corrected deviations perpendicular to each GEM faster than deviations along each GEM to the same degree for both tasks, despite exhibiting significantly greater variance ratios for the D/T task. Variance measures alone failed to capture critical features of trial-to-trial control. Humans actively exploited these abstract task redundancies, even though they did not have to. They did not use readily available alternative strategies that could have achieved the same performance.

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Section: Discussionsupporting

confidence: 88%

Section: Methodssupporting

confidence: 80%

mentioning

confidence: 99%

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Trial-to-trial dynamics and learning in a generalized, redundant reaching task

Dingwell

Smallwood

Cusumano

2013

Journal of Neurophysiology

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“…Our model predicts that the change in feedforward compensation for a disturbance will include both an increase in the impedance of the limb and a change in the applied force. If the disturbance is particularly difficult to counteract, such as a brief force pulse (Fine and Thoroughman, 2006) or if its strength is unpredictable (Fine and Thoroughman, 2007) then the CNS might reduce the difference in the slope of the V-shaped learning function (Fig. 1 B) between the agonist and antagonist muscles.…”

Section: Discussionmentioning

confidence: 99%

CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

Franklin¹,

et al. 2008

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We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.

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“…multisensory integration | sensorimotor learning | motor variability E rror correction based on sensory feedback is a ubiquitous mechanism for maintaining behavioral performance (1)(2)(3)(4). However, sensory information is vulnerable to environmental noise and to errors in sensory encoding arising at the periphery or during central processing of the sensory signal (5).…”

mentioning

confidence: 99%

Vocal learning is constrained by the statistics of sensorimotor experience

Sober

Brainard

2012

Proc. Natl. Acad. Sci. U.S.A.

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The brain uses sensory feedback to correct behavioral errors. Larger errors by definition require greater corrections, and many models of learning assume that larger sensory feedback errors drive larger motor changes. However, an alternative perspective is that larger errors drive learning less effectively because such errors fall outside the range of errors normally experienced and are therefore unlikely to reflect accurate feedback. This is especially crucial in vocal control because auditory feedback can be contaminated by environmental noise or sensory processing errors. A successful control strategy must therefore rely on feedback to correct errors while disregarding aberrant auditory signals that would lead to maladaptive vocal corrections. We hypothesized that these constraints result in compensation that is greatest for smaller imposed errors and least for larger errors. To test this hypothesis, we manipulated the pitch of auditory feedback in singing Bengalese finches. We found that learning driven by larger sensory errors was both slower than that resulting from smaller errors and showed less complete compensation for the imposed error. Additionally, we found that a simple principle could account for these data: the amount of compensation was proportional to the overlap between the baseline distribution of pitch production and the distribution experienced during the shift. Correspondingly, the fraction of compensation approached zero when pitch was shifted outside of the song's baseline pitch distribution. Our data demonstrate that sensory errors drive learning best when they fall within the range of production variability, suggesting that learning is constrained by the statistics of sensorimotor experience.multisensory integration | sensorimotor learning | motor variability E rror correction based on sensory feedback is a ubiquitous mechanism for maintaining behavioral performance (1-4). However, sensory information is vulnerable to environmental noise and to errors in sensory encoding arising at the periphery or during central processing of the sensory signal (5). Such contamination can result in large mismatches between the actual and expected sensory feedback that do not necessarily reflect errors in performance. The brain must therefore decide whether to modify behavior based on sensory feedback (and risk "adapting" to signals that do not accurately reflect performance) or ignore sensory input (and risk leaving errors uncorrected). This problem is especially important in vocal behaviors, in which performance can have significant consequences for the success of an organism and auditory feedback is vulnerable to extrinsic acoustic signals and errors in auditory encoding.During development, both humans and songbirds learn by processes of vocal imitation that rely heavily on auditory feedback (2). Early vocalizations bear little resemblance to mature song or speech, resulting in large differences (errors) between experienced auditory feedback and the sensory goal. With practice, vocalizations become m...

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Trial-by-Trial Transformation of Error Into Sensorimotor Adaptation Changes With Environmental Dynamics

Abstract: Fine MS, Thoroughman KA. Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics.

Cited by 117 publications

References 32 publications

Trial-to-trial dynamics and learning in a generalized, redundant reaching task

Trial-to-trial dynamics and learning in a generalized, redundant reaching task

CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

Vocal learning is constrained by the statistics of sensorimotor experience

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