Effect of Visual Error Size on Saccade Adaptation in Monkey

Robinson, Farrel R.; Noto, Christopher T.; Bevans, Scott E.

doi:10.1152/jn.00656.2002

Cited by 112 publications

(129 citation statements)

References 16 publications

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“…The inverse relationship between pitch error size and vocal learning reported here parallels similar findings in the response of the human arm movement system to visual (6) and force (3) perturbations and of the primate oculomotor system to visual errors during saccades (4). Similarly, in response to brief (200-500 ms) pitch perturbations during ongoing speech, transient compensatory vocal changes in human subjects occur on fewer trials (16) and correct a smaller fraction of the imposed error (17) for larger perturbations.…”

Section: Discussionsupporting

confidence: 86%

“…In contrast, some studies of arm and eye movements have shown that as the size of an experimentally induced sensory error increases, the size of the behavioral response approaches an asymptote (3) or begins to decline (4,6). Furthermore, when the magnitude of an auditory perturbation is gradually increased across a single training session, vocal compensation by human subjects can plateau or begin to decrease (7,8), suggesting that larger errors are less effective at driving learning.…”

mentioning

confidence: 97%

“…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%

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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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Section: Discussionsupporting

confidence: 86%

mentioning

confidence: 97%

mentioning

confidence: 99%

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Vocal learning is constrained by the statistics of sensorimotor experience

Sober

Brainard

2012

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

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“…6) would constantly signal whether saccades were staying accurate. Consistent with this suggestion, Robinson et al (2003) recently showed that smaller errors are more efficient at driving adaptation than are large ones. Finally, a model that uses an error signal that indicates only the direction of saccade dysmetria is sufficient to simulate the general properties of saccade adaptation (Dean et al, 1994).…”

Section: Do Changes In Cs Activity Drive Saccade Adaptation?mentioning

confidence: 58%

Complex Spike Activity of Purkinje Cells in the Oculomotor Vermis during Behavioral Adaptation of Monkey Saccades

2006

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Throughout life, the oculomotor system can correct itself when saccadic eye movements become inaccurate. This adaptation mechanism can be engaged in the laboratory by displacing the target when the saccade toward it is in flight. Forward and backward target displacements cause gradual increases and decreases in saccade amplitude, respectively. Equipped with this paradigm, we asked whether Purkinje cells (P-cells) in the vermis of the oculomotor cerebellum, lobules VIc and VII, changed their complex spike (CS) discharge during the behavioral adaptation of horizontal saccades. We tested the hypothesis that CS activity would change only when a targeting saccade caused an error in eye position relative to the target, i.e., during the error interval between the primary and corrective saccades. We examined only those P-cells whose simple spike activity exhibited either a burst or pause with saccades in several directions. Approximately 80% of such P-cells exhibited an increase in CS activity during the error interval when the adaptation paradigm imposed horizontal eye-position errors in one direction and a decrease in activity for errors in the other. As adaptation progressed and errors were reduced, there was no consistent change in the CS activity. These data suggest that the CS activity of P-cells in the oculomotor vermis signals the direction but not the magnitude of eye-position error during saccade adaptation. Our results are consistent with cerebellar learning models that have been proposed to explain adaptation of the vestibulo-ocular reflex so similar mechanisms may also underlie plasticity of this precision voluntary oculomotor behavior.

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“…If the shift vector was place-coded in the SC, why did stimulation at sites representing errors Ͼ5°fail to elicit clear adaptation-like changes? Robinson et al (2003) examined the effect of error size on saccade adaptation using errors whose size was fixed during adaptation. They found that errors Ͻ45% the size of the target step elicited average gain changes of ϳ0.3-0.4, whereas error sizes Ͼ45% of the target step produced much lesser gain changes.…”

Section: Representation Of Instructions For Saccade Adaptation In the Scmentioning

confidence: 99%

Learning Signals from the Superior Colliculus for Adaptation of Saccadic Eye Movements in the Monkey

Kaku¹,

Yoshida²,

Iwamoto³

2009

J. Neurosci.

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Vital to motor learning is information about movement error. Using this information, the brain creates neural learning signals that instruct a plasticity mechanism to produce appropriate behavioral learning. Little is known, however, about brain structures that generate learning signals for voluntary movements. Here we show that signals from the superior colliculus (SC) can drive learning in saccadic eye movements in the monkey. Electrical stimulation of the SC deeper layers, subthreshold for evoking saccades, was applied immediately (ϳ60 ms) after the end of horizontal saccades in one or both directions. The target disappeared during saccades and remained invisible for 1 s to eliminate effects of postsaccadic visual information. Repetitive pairing of saccades with SC stimulation produced a marked, two-dimensional shift in movement endpoint relative to the target location. The elicited endpoint shift took a gradual, approximately exponential course over several hundred saccades as in visually induced saccade adaptation. The shift in movement endpoint remained nearly unchanged after stimulation was discontinued, indicating involvement of neuronal plasticity. When both rightward and leftward saccades were paired with stimulation, their endpoints shifted in similar directions. The endpoint shift was directed contralaterally to the stimulated SC. The direction and size of the endpoint shift depended on the stimulation site in the SC. We propose that the SC, a brainstem structure long known to be crucial for saccade execution, is involved in motor learning and sends signals that dictate the direction of adaptive shift in saccade endpoint.

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Effect of Visual Error Size on Saccade Adaptation in Monkey

Cited by 112 publications

References 16 publications

Vocal learning is constrained by the statistics of sensorimotor experience

Vocal learning is constrained by the statistics of sensorimotor experience

Complex Spike Activity of Purkinje Cells in the Oculomotor Vermis during Behavioral Adaptation of Monkey Saccades

Learning Signals from the Superior Colliculus for Adaptation of Saccadic Eye Movements in the Monkey

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