Reward Action in the Initiation of Smooth Pursuit Eye Movements

Joshua, Mati; Lisberger, Stephen G.

doi:10.1523/jneurosci.4676-11.2012

Cited by 29 publications

(48 citation statements)

References 41 publications

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“…Although reward did not affect the learning speed of simian smooth pursuit eye movements, it apparently did influence the expression of the learned pursuit eye movements (Joshua and Lisberger, 2012). Also, learning to use a robotic arm can be influenced by reward (Nikooyan and Ahmed, 2015) or punishment (Galea et al, 2015) in healthy human subjects.…”

Section: Discussionmentioning

confidence: 99%

Selective reward affects the rate of saccade adaptation

Kojima

Soetedjo

2017

Neuroscience

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In this study we tested whether a selective reward could affect the adaptation of saccadic eye movements in monkeys. We induced the adaptation of saccades by displacing the target of a horizontal saccade vertically as the eye moved toward it, thereby creating an apparent vertical dysmetria. The repeated upward target displacement caused the originally horizontal saccade to gradually deviate upward over the course of several hundred trials. We induced this directional adaptation in both right- and leftward saccades in every experiment (n=20). However, in half of the experiments (n=10), we rewarded monkeys only when they made leftward saccades and in the other half only for rightward saccades. The reaction time of saccades in the rewarded direction was shorter and we, like others, interpreted this change as a sign of the reward’s preferential effect in that direction. Saccades in the rewarded direction showed more rapid adaptation of their directions than did saccades in the non-rewarded direction, indicating that the selective reward increased the speed of saccade adaptation. The differences in adaptation speed were reflected in changes in saccade metrics, which were usually more noticeable in the deceleration phases of saccades than in their acceleration phases. Because previous studies have shown that the oculomotor cerebellum is involved with saccade deceleration and also participates in saccade adaptation, it is possible that selective reward could influence cerebellar plasticity.

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Section: Discussionmentioning

confidence: 99%

Selective reward affects the rate of saccade adaptation

Kojima

Soetedjo

2017

Neuroscience

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“…We modified a smooth pursuit eye movement leaning paradigm to test whether motor commands are needed for learning to occur. When monkeys (and humans) are trained to track a moving target that repeatedly undergoes the same change in direction at a predictable time, a learned smooth pursuit eye movement is elicited prior to change in target direction (Joshua and Lisberger 2012;Medina et al 2005). These behavioral changes occur quickly and asymptotically after a few as 50 trials (Hall et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Passive motor learning: Oculomotor adaptation in the absence of feedback on behavioral errors

Cain¹,

Joshua²

2020

Preprint

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Motor adaptation is commonly thought to be a trial-and-error process in which accuracy of movement improves with repetition of behavior. We challenged this view by testing whether erroneous movements are necessary for motor adaptation. In the eye movement system, the association between motor command and errors can be disentangled, since errors in the predicted stimulus trajectory can be perceived even without movements. We modified a smooth pursuit eye movement adaptation paradigm in which monkeys learn to make an eye movement that predicts an upcoming change in target direction. We trained monkeys to fixate on a target while covertly, an additional target initially moved in one direction and then changed direction after 250 ms. Monkeys showed a learned response to infrequent probe trials in which they were instructed to follow the moving target. Further experiments confirmed that probing learning or residual eye movement during fixation did not drive learning. These results show that movement is not necessary for motor adaptation and provide an animal model for studying how passive learning is implemented. The standard model assumes that the interaction between movement and error signals in the cerebellum underlies adaptive learning. Our results indicate that either sensory inputs are sufficient for driving learning in the cerebellum or that learning is implemented partly outside the cerebellum.

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“…Previous studies on value-modulations of smooth pursuit showed that value information can bias the initial pursuit direction, when the rewarded direction is cued in advance (Joshua & Lisberger, 2012), or when spatially-defined targets were used (Ferrera, 2000). In our paradigm, the rewarded direction was not predictable which allowed us to directly compare the time course of salience and value effects.…”

Section: Discussionmentioning

confidence: 99%

“…In this case, reward decreases smooth pursuit latency and increases acceleration slightly. These effects seem to be small in magnitude, compared to the effects on target selection (Joshua & Lisberger, 2012). Similar to pursuit, reward also leads to a decrease in saccade latency (Milstein & Dorris, 2007;Rothkirch et al, 2013;Takikawa et al, 2002) and an increase in saccade peak velocity (Chen et al, 2013;Takikawa et al, 2002;Xu-Wilson, Zee, & Shadmehr, 2009).…”

Section: Effects Of Reward With Single Targetsmentioning

confidence: 90%

“…Similar to saccades, instructions and reward can bias the target selection for smooth pursuit. When two targets differ in their reward and a cue informs about the motion direction of the two targets, pursuit initiation is biased towards the rewarded motion direction (Ferrera, 2000;Joshua & Lisberger, 2012). Non-visual feedback can also increase pursuit gain during transient target blanking (Madelain & Krauzlis, 2003 is how different types of signals are traded off in the control of pursuit movements.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic integration of information about salience and value for smooth pursuit eye movements

2015

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Eye movement behavior can be determined by bottom-up factors like visual salience and by top-down factors like expected value. These different types of signals have to be combined for the control of eye movements. In this study we investigated how smooth pursuit eye movements integrate salience and value information. Observers were asked to track a random-dot kinematogram containing two coherent motion directions. To manipulate salience, the coherence or the density of one of the motion signals was varied. To manipulate value, observers won or lost money in a separate experiment if they were tracking one or the other motion direction. Our results show that pursuit direction was initially determined only by salience. 300-400 ms after target motion onset, pursuit steered towards the rewarded direction and the salience effects disappeared. The time course of this effect depended crucially on the difficulty to segment the two signal directions. These results indicate that salience determines early pursuit responses in the same way as saccades with short latencies. Value information is processed slower and dominates pursuit after several 100 ms.

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Reward Action in the Initiation of Smooth Pursuit Eye Movements

Cited by 29 publications

References 41 publications

Selective reward affects the rate of saccade adaptation

Selective reward affects the rate of saccade adaptation

Passive motor learning: Oculomotor adaptation in the absence of feedback on behavioral errors

Dynamic integration of information about salience and value for smooth pursuit eye movements

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