Velocity tracking control best explains the modulation of saccade kinematics during eye-hand coordination.

Fast movements like saccadic eye movements that occur in the absence of sensory feedback are thought to be controlled by internal feedback. Such internal feedback provides an instantaneous estimate of the output, which serves as a proxy for sensory feedback, that can be used by the controller to correct deviations from the desired plan. In the predominant view, the desired plan/input is encoded in the form of a static displacement signal (endpoint model), believed to be encoded in the spatial map of the superior colliculus (SC). However, recent evidence has shown that SC neurons have a dynamic signal that correlates with saccade velocity, suggesting that information for velocity-based control is available for generating saccades. Motivated by this observation, we used a novel optimal control framework to test whether saccadic execution could be achieved by tracking a dynamic velocity signal at the input. We validated this velocity tracking model in a task where the peak saccade velocity was modulated by the speed of a concurrent hand movement independent of the saccade endpoint. A comparison showed that in this task, the velocity tracking model performed significantly better than the endpoint model. These results suggest that the saccadic system may have additional flexibility to incorporate a velocity-based internal feedback control when imposed by task goals or context.

Computational Mechanisms Mediating Inhibitory Control of Coordinated Eye-Hand Movements.

Significant progress has been made in understanding the computational and neural mechanisms that mediate eye and hand movements made in isolation. However, less is known about the mechanisms that control these movements when they are coordinated. Here, we outline our computational approaches using accumulation-to-threshold and race-to-threshold models to elucidate the mechanisms that initiate and inhibit these movements. We suggest that, depending on the behavioral context, the initiation and inhibition of coordinated eye-hand movements can operate in two modes-coupled and decoupled. The coupled mode operates when the task context requires a tight coupling between the effectors; a common command initiates both effectors, and a unitary inhibitory process is responsible for stopping them. Conversely, the decoupled mode operates when the task context demands weaker coupling between the effectors; separate commands initiate the eye and hand, and separate inhibitory processes are responsible for stopping them. We hypothesize that the higher-order control processes assess the behavioral context and choose the most appropriate mode. This computational mechanism can explain the heterogeneous results observed across many studies that have investigated the control of coordinated eye-hand movements and may also serve as a general framework to understand the control of complex multi-effector movements.

Neuronal connections of direct and indirect pathways for stable value memory in caudal basal ganglia.

Direct and indirect pathways in the basal ganglia work together for controlling behavior. However, it is still a controversial topic whether these pathways are segregated or merged with each other. To address this issue, we studied the connections of these two pathways in the caudal parts of the basal ganglia of rhesus monkeys using anatomical tracers. Our previous studies showed that the caudal basal ganglia control saccades by conveying long-term values (stable values) of many visual objects toward the superior colliculus. In experiment 1, we injected a tracer in the caudate tail (CDt), and found local dense plexuses of axon terminals in the caudal-dorsal-lateral part of substantia nigra pars reticulata (cdlSNr) and the caudal-ventral part of globus pallidus externus (cvGPe). These anterograde projections may correspond to the direct and indirect pathways, respectively. To verify this in experiment 2, we injected different tracers into cdlSNr and cvGPe, and found many retrogradely labeled neurons in CDt and, in addition, the caudal-ventral part of the putamen (cvPut). These cdlSNr-projecting and cvGPe-projecting neurons were found intermingled in both CDt and cvPut (which we call "striatum tail"). A small but significant proportion of neurons (<15%) were double-labeled, indicating that they projected to both cdlSNr and cvGPe. These anatomical results suggest that stable value signals (good vs. bad) are sent from the striatum tail to cdlSNr and cvGPe in a biased (but not exclusive) manner. These connections may play an important role in biasing saccades toward higher valued objects and away from lower valued objects.

Visual Neurons in the Superior Colliculus Discriminate Many Objects by Their Historical Values.

The superior colliculus (SC) is an important structure in the mammalian brain that orients the animal toward distinct visual events. Visually responsive neurons in SC are modulated by visual object features, including size, motion, and color. However, it remains unclear whether SC activity is modulated by non-visual object features, such as the reward value associated with the object. To address this question, three monkeys were trained (>10 days) to saccade to multiple fractal objects, half of which were consistently associated with large rewards while other half were associated with small rewards. This created historically high-valued ('good') and low-valued ('bad') objects. During the neuronal recordings from the SC, the monkeys maintained fixation at the center while the objects were flashed in the receptive field of the neuron without any reward. We found that approximately half of the visual neurons responded more strongly to the good than bad objects. In some neurons, this value-coding remained intact for a long time (>1 year) after the last object-reward association learning. Notably, the neuronal discrimination of reward values started about 100 ms after the appearance of visual objects and lasted for more than 100 ms. These results provide evidence that SC neurons can discriminate objects by their historical (long-term) values. This object value information may be provided by the basal ganglia, especially the circuit originating from the tail of the caudate nucleus. The information may be used by the neural circuits inside SC for motor (saccade) output or may be sent to the circuits outside SC for future behavior.

An independent race model involving an abort and re-plan strategy explains reach redirecting movements during planning and execution.

Although race models have been extensively used to study inhibitory control, the mechanisms that enable change of reach plans in the context of race models remain unexplored. We used a redirect task in which targets occasionally changed their locations to study the control of reaching movements during movement planning and execution phases. We tested nine different race model architectures that could explain the redirect behavior of reaching movements. We show that an independent GO-STOP-GO model that reflects a plan-abort-re-plan strategy involving non-interacting elements successfully explained the various behavioral measures such as the compensation function and the pattern of error response reaction times. By extending the same race model to the execution phase, we could explain the extent and the pattern of hypometric trials. Interestingly, the race model also provided evidence that redirecting a movement during planning and execution shared the same inhibitory mechanism. Taken together, this study demonstrates the applicability of an independent race model to understand the computational mechanisms underlying the control of reach movements.

Contrasting speed-accuracy tradeoffs for eye and hand movements reveal the optimal nature of saccade kinematics.

In contrast to hand movements, the existence of a neural representation of saccade kinematics is unclear. Saccade kinematics is typically thought to be specified by motor error/desired displacement and generated by brain stem circuits that are not penetrable to voluntary control. We studied the influence of instructed hand movement velocity on the kinematics of saccades executed without explicit instructions. When the hand movement was slow the saccade velocity decreased, independent of saccade amplitude. We leveraged this modulation of saccade velocity to study the optimality of saccades (in terms of velocity and endpoint accuracy) in relation to the well-known speed-accuracy tradeoff that governs voluntary movements (Fitts' law). In contrast to hand movements that obeyed Fitts' law, normometric saccades exhibited the greatest endpoint accuracy and lower reaction times, relative to saccades accompanying slow and fast hand movements. In the slow condition, where saccade endpoint accuracy suffered, we observed that targets were more likely to be foveated by two saccades resulting in step-saccades. Interestingly, the endpoint accuracy was higher in two-saccade trials, compared with one-saccade trials in both the slow and fast conditions. This indicates that step-saccades are a part of the kinematic plan for optimal control of endpoint accuracy. Taken together, these findings suggest normometric saccades are already optimized to maximize endpoint accuracy and the modulation of saccade velocity by hand velocity is likely to reflect the sharing of kinematic plans between the two effectors.NEW & NOTEWORTHY The optimality of saccade kinematics has been suggested by modeling studies but experimental evidence is lacking. However, we observed that, when subjects voluntarily modulated their hand velocity, the velocity of saccades accompanying these hand movements was also modulated, suggesting a shared kinematic plan for eye and hand movements. We leveraged this modulation to show that saccades had less endpoint accuracy when their velocity decreased, illustrating that normometric saccades have optimal speed and accuracy.

Evidence of common and separate eye and hand accumulators underlying flexible eye-hand coordination.

Eye and hand movements are initiated by anatomically separate regions in the brain, and yet these movements can be flexibly coupled and decoupled, depending on the need. The computational architecture that enables this flexible coupling of independent effectors is not understood. Here, we studied the computational architecture that enables flexible eye-hand coordination using a drift diffusion framework, which predicts that the variability of the reaction time (RT) distribution scales with its mean. We show that a common stochastic accumulator to threshold, followed by a noisy effector-dependent delay, explains eye-hand RT distributions and their correlation in a visual search task that required decision-making, while an interactive eye and hand accumulator model did not. In contrast, in an eye-hand dual task, an interactive model better predicted the observed correlations and RT distributions than a common accumulator model. Notably, these two models could only be distinguished on the basis of the variability and not the means of the predicted RT distributions. Additionally, signatures of separate initiation signals were also observed in a small fraction of trials in the visual search task, implying that these distinct computational architectures were not a manifestation of the task design per se. Taken together, our results suggest two unique computational architectures for eye-hand coordination, with task context biasing the brain toward instantiating one of the two architectures. NEW & NOTEWORTHY: Previous studies on eye-hand coordination have considered mainly the means of eye and hand reaction time (RT) distributions. Here, we leverage the approximately linear relationship between the mean and standard deviation of RT distributions, as predicted by the drift-diffusion model, to propose the existence of two distinct computational architectures underlying coordinated eye-hand movements. These architectures, for the first time, provide a computational basis for the flexible coupling between eye and hand movements.

A common control signal and a ballistic stage can explain the control of coordinated eye-hand movements.

Voluntary control has been extensively studied in the context of eye and hand movements made in isolation, yet little is known about the nature of control during eye-hand coordination. We probed this with a redirect task. Here subjects had to make reaching/pointing movements accompanied by coordinated eye movements but had to change their plans when the target occasionally changed its position during some trials. Using a race model framework, we found that separate effector-specific mechanisms may be recruited to control eye and hand movements when executed in isolation but when the same effectors are coordinated a unitary mechanism to control coordinated eye-hand movements is employed. Specifically, we found that performance curves were distinct for the eye and hand when these movements were executed in isolation but were comparable when they were executed together. Second, the time to switch motor plans, called the target step reaction time, was different in the eye-alone and hand-alone conditions but was similar in the coordinated condition under assumption of a ballistic stage of ∼40 ms, on average. Interestingly, the existence of this ballistic stage could predict the extent of eye-hand dissociations seen in individual subjects. Finally, when subjects were explicitly instructed to control specifically a single effector (eye or hand), redirecting one effector had a strong effect on the performance of the other effector. Taken together, these results suggest that a common control signal and a ballistic stage are recruited when coordinated eye-hand movement plans require alteration.

Eye-hand coordination during a double-step task: evidence for a common stochastic accumulator.

Many studies of reaching and pointing have shown significant spatial and temporal correlations between eye and hand movements. Nevertheless, it remains unclear whether these correlations are incidental, arising from common inputs (independent model); whether these correlations represent an interaction between otherwise independent eye and hand systems (interactive model); or whether these correlations arise from a single dedicated eye-hand system (common command model). Subjects were instructed to redirect gaze and pointing movements in a double-step task in an attempt to decouple eye-hand movements and causally distinguish between the three architectures. We used a drift-diffusion framework in the context of a race model, which has been previously used to explain redirect behavior for eye and hand movements separately, to predict the pattern of eye-hand decoupling. We found that the common command architecture could best explain the observed frequency of different eye and hand response patterns to the target step. A common stochastic accumulator for eye-hand coordination also predicts comparable variances, despite significant difference in the means of the eye and hand reaction time (RT) distributions, which we tested. Consistent with this prediction, we observed that the variances of the eye and hand RTs were similar, despite much larger hand RTs (∼90 ms). Moreover, changes in mean eye RTs, which also increased eye RT variance, produced a similar increase in mean and variance of the associated hand RT. Taken together, these data suggest that a dedicated circuit underlies coordinated eye-hand planning.

A common stochastic accumulator with effector-dependent noise can explain eye-hand coordination.

The computational architecture that enables the flexible coupling between otherwise independent eye and hand effector systems is not understood. By using a drift diffusion framework, in which variability of the reaction time (RT) distribution scales with mean RT, we tested the ability of a common stochastic accumulator to explain eye-hand coordination. Using a combination of behavior, computational modeling and electromyography, we show how a single stochastic accumulator to threshold, followed by noisy effector-dependent delays, explains eye-hand RT distributions and their correlation, while an alternate independent, interactive eye and hand accumulator model does not. Interestingly, the common accumulator model did not explain the RT distributions of the same subjects when they made eye and hand movements in isolation. Taken together, these data suggest that a dedicated circuit underlies coordinated eye-hand planning.