Optimal Control of Gaze Shifts

Kardamakis, Andreas A.; Moschovakis, Adonis

doi:10.1523/jneurosci.5518-08.2009

Cited by 48 publications

(60 citation statements)

References 46 publications

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“…Considering intertrial intervals, they nevertheless concluded later that an exponential CoT was inconsistent with the data (Haith et al, 2012). Here we relied upon the same experimental data reported by Collewijn et al (1988), and we used the same linear model of the oculomotor plant and trajectory cost (i.e., "effort" cost, quadratic in the control variable) to identify the CoT underlying human saccades (as described also by Harris and Wolpert, 1998;Tanaka et al, 2006;Kardamakis and Moschovakis, 2009;Shadmehr et al, 2010).…”

Section: Illustration For Saccadesmentioning

confidence: 99%

Why Don't We Move Slower? The Value of Time in the Neural Control of Action

2016

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To want something now rather than later is a common attitude that reflects the brain's tendency to value the passage of time. Because the time taken to accomplish an action inevitably delays task achievement and reward acquisition, this idea was ported to neural movement control within the "cost of time" theory. This theory provides a normative framework to account for the underpinnings of movement time formation within the brain and the origin of a self-selected pace in human and animal motion. Then, how does the brain exactly value time in the control of action? To tackle this issue, we used an inverse optimal control approach and developed a general methodology allowing to squarely sample infinitesimal values of the time cost from experimental motion data. The cost of time underlying saccades was found to have a concave growth, thereby confirming previous results on hyperbolic reward discounting, yet without making any prior assumption about this hypothetical nature. For self-paced reaching, however, movement time was primarily valued according to a striking sigmoidal shape; its rate of change consistently presented a steep rise before a maximum was reached and a slower decay was observed. Theoretical properties of uniqueness and robustness of the inferred time cost were established for the class of problems under investigation, thus reinforcing the significance of the present findings. These results may offer a unique opportunity to uncover how the brain values the passage of time in healthy and pathological motor control and shed new light on the processes underlying action invigoration.

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Section: Illustration For Saccadesmentioning

confidence: 99%

Why Don't We Move Slower? The Value of Time in the Neural Control of Action

2016

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“…To fit the model, the free parameters were further reduced by setting ␣ e1 , ␣ e2 , and ␣ h to unity so that the penalty functions of SDN for eye and head are purely determined by the system dynamics rather than by additional weighting. The fact that our model is based on eye and head dynamics distinguishes it from that of Kardamakis and Moschovakis (2009). They proposed a minimum effort strategy, which penalizes the eye control signal with an eye-eccentricity-dependent quadratic function and the head control signal with a state-independent constant.…”

Section: Optimization Criteria For Goal-directed Movementsmentioning

confidence: 99%

“…5C). In contrast, a model not taking into account plant dynamics (Kardamakis and Moschovakis, 2009) would have an unchanged optimization criterion in the weighted condition and predict a motor command that leads to changes in the head movement via only the altered head dynamics. Such a model would be unable to predict the experimental findings in the weighted condition (Lehnen, 2006).…”

Section: Optimization Criteria For Goal-directed Movementsmentioning

confidence: 99%

“…Rather, the optimal movement is chosen according to a strategy that does not rely on VOR correction but rather takes the head-related costs throughout the whole movement into account for the optimization process. Like many previous optimization models (Uno et al, 1989;Wolpert, 1998, 2006;Tanaka et al, 2006;van Beers, 2008;Kardamakis and Moschovakis, 2009), our model only considers feedforward mechanisms. This is appropriate to reflect well practiced movements, but obviously, feedback mechanisms involved in the control of head movements can play a role during active gaze shifts, e.g., by correcting undesired deviations on-line and from trial-to-trial.…”

Section: Impact Of Feedback Mechanismsmentioning

confidence: 99%

“…4). In contrast to the minimum-effort approach (Kardamakis and Moschovakis, 2009), which explicitly penalizes eye eccentricity with an additional penalty function, in our model the initial position dependence is a consequence of the plant dynamics. In accordance with Harris and Wolpert (1998) and Tanaka et al (2004), our model also accounts for the skewed eye and bell-shaped head velocity profiles (Fig.…”

Section: Comparison Of Simulations and Experimental Datamentioning

confidence: 99%

See 2 more Smart Citations

Optimal Control of Natural Eye-Head Movements Minimizes the Impact of Noise

Sağlam

Lehnen²,

Glasauer³

2011

J. Neurosci.

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When shifting gaze to foveate a new target, humans mostly choose a unique set of eye and head movements from an infinite number of possible combinations. This stereotypy suggests that a general principle governs the movement choice. Here, we show that minimizing the impact of uncertainty, i.e., noise affecting motor performance, can account for the choice of combined eye-head movements. This optimization criterion predicts all major features of natural eye-head movements-including the part where gaze is already on target and the eye counter-rotates-such as movement durations, relative eye-head contributions, velocity profiles, and the dependency of gaze shifts on initial eye position. As a critical test of this principle, we show that it also correctly predicts changes in eye and head movement imposed by an experimental increase in the head moment of inertia. This suggests that minimizing the impact of noise is a simple and powerful principle that explains the choice of a unique set of movement profiles and segment coordination in goal-directed action.

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Fast gaze reorientations by combined movements of the eye, head, trunk and lower extremities

et al. 2015

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Large reorientations of the line of sight, involving combined rotations of the eyes, head, trunk and lower extremities, are executed either as fast single-step or as slow multiple-step gaze transfers. In order to obtain more insight into the mechanisms of gaze and multisegmental movement control, we have investigated time-optimal gaze shifts (i.e. with the instruction to move as fast as possible) during voluntary whole-body rotations to remembered targets up to 180° eccentricity performed by standing healthy humans in darkness. Fast, accurate, single-step movement patterns occurred in approximately 70 % of trials, i.e. considerably more frequently than in previous studies with the instruction to turn at freely chosen speed (30 %). Head-in-space velocity in these cases was significantly higher than during multiple-step transfers and displayed a conspicuously regular bell-shaped profile, increasing smoothly to a peak and then decreasing slowly until realignment with the target. Head-in-space acceleration was on average not different during reorientations to the different target eccentricities. In contrast, head-in-space velocity increased with target eccentricity due to the longer duration of the acceleration phase implemented during trials to more distant targets. Eye saccade amplitude approached the eye-in-orbit mechanical limit and was unrelated to eye/head velocity, duration or target eccentricity. Overall, the combined movement was stereotyped such that the first two principal components accounted for data variance almost up to gaze shift end, suggesting that the three mechanical degrees of freedom under consideration (eye-in-orbit, head-on-trunk and trunk-in-space) are on average reduced to two kinematic degrees of freedom (i.e. eye, head-in-space). Synchronous EMG activity in the anterior tibial and gastrocnemius muscles preceded the onset of eye rotation. Since the magnitude and timing of peak head-in-space velocity were scaled with target eccentricity and because head-on-trunk and trunk-in-space displacements were on average linearly correlated, we propose a separate controller for head-in-space movement, whereas the movement of the eye-in-space may be, in contrast, governed by global, i.e. gaze feedback. The rapid progression of the line of sight can be sustained, and the reactivation of the vestibulo-ocular reflex would be postponed, until gaze error approaches zero only in association with a strong head-in-space neural control signal.

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Optimal Control of Gaze Shifts

Cited by 48 publications

References 46 publications

Why Don't We Move Slower? The Value of Time in the Neural Control of Action

Why Don't We Move Slower? The Value of Time in the Neural Control of Action

Optimal Control of Natural Eye-Head Movements Minimizes the Impact of Noise

Fast gaze reorientations by combined movements of the eye, head, trunk and lower extremities

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