Reward action in the initiation of smooth pursuit eye movements

Consensus Paper: Cerebellum and Reward

Cerebellum is a key-structure for the modulation of motor, cognitive, social and affective functions, contributing to automatic behaviours through interactions with the cerebral cortex, basal ganglia and spinal cord. The predictive mechanisms used by the cerebellum cover not only sensorimotor functions but also reward-related tasks. Cerebellar circuits appear to encode temporal difference error and reward prediction error. From a chemical standpoint, cerebellar catecholamines modulate the rate of cerebellar-based cognitive learning, and mediate cerebellar contributions during complex behaviours. Reward processing and its associated emotions are tuned by the cerebellum which operates as a controller of adaptive homeostatic processes based on interoceptive and exteroceptive inputs. Lobules VI-VII/areas of the vermis are candidate regions for the cortico-subcortical signaling pathways associated with loss aversion and reward sensitivity, together with other nodes of the limbic circuitry. There is growing evidence that the cerebellum works as a hub of regional dysconnectivity across all mood states and that mental disorders involve the cerebellar circuitry, including mood and addiction disorders, and impaired eating behaviors where the cerebellum might be involved in longer time scales of prediction as compared to motor operations. Cerebellar patients exhibit aberrant social behaviour, showing aberrant impulsivity/compulsivity. The cerebellum is a master-piece of reward mechanisms, together with the striatum, ventral tegmental area (VTA) and prefrontal cortex (PFC). Critically, studies on reward processing reinforce our view that a fundamental role of the cerebellum is to construct internal models, perform predictions on the impact of future behaviour and compare what is predicted and what actually occurs.

Organization of reward and movement signals in the basal ganglia and cerebellum

The basal ganglia and the cerebellum are major subcortical structures in the motor system. The basal ganglia have been cast as the reward center of the motor system, whereas the cerebellum is thought to be involved in adjusting sensorimotor parameters. Recent findings of reward signals in the cerebellum have challenged this dichotomous view. To compare the basal ganglia and the cerebellum directly, we recorded from oculomotor regions in both structures from the same monkeys. We partitioned the trial-by-trial variability of the neurons into reward and eye-movement signals to compare the coding across structures. Reward expectation and movement signals were the most pronounced in the output structure of the basal ganglia, intermediate in the cerebellum, and the smallest in the input structure of the basal ganglia. These findings suggest that reward and movement information is sharpened through the basal ganglia, resulting in a higher signal-to-noise ratio than in the cerebellum.

Sensorimotor-linked reward modulates smooth pursuit eye movements in monkeys

Reward is essential for shaping behavior. Using sensory cues to imply forthcoming rewards, previous studies have demonstrated powerful effects of rewards on behavior. Nevertheless, the impact of reward on the sensorimotor transformation, particularly when reward is linked to behavior remains uncertain. In this study, we investigated how reward modulates smooth pursuit eye movements in monkeys. Three distinct associations between reward and eye movements were conducted in independent blocks. Results indicated that reward increased eye velocity during the steady-state pursuit, rather than during the initiation. The influence depended on the particular association between behavior and reward: a faster eye velocity was linked with reward. Neither rewarding slower eye movements nor randomizing rewards had a significant effect on behavior. The findings support the existence of distinct mechanisms involved in the initiation and steady-state phases of pursuit, and contribute to a deeper understanding of how reward interacts with these two periods of pursuit.

Form Properties of Moving Targets Bias Smooth Pursuit Target Selection in Monkeys

During natural viewing, we often recognize multiple objects, detect their motion, and select one object as the target to track. It remains to be determined how such behavior is guided by the integration of visual form and motion perception. To address this, we studied how monkeys made a choice to track moving targets with different forms by smooth pursuit eye movements in a two-target task. We found that pursuit responses were biased toward the motion direction of a target with a hole. By computing the relative weighting, we found that the target with a hole exhibited a larger weight for vector computation. The global hole feature dominated other form properties. This dominance failed to account for changes in pursuit responses to a target with different forms moving singly. These findings suggest that the integration of visual form and motion perception can reshape the competition in sensorimotor networks to guide behavioral selection.

The cost of correcting for error during sensorimotor adaptation

Learning from error is often a slow process. In machine learning, the learning rate depends on a loss function that specifies a cost for error. Here, we hypothesized that during motor learning, error carries an implicit cost for the brain because the act of correcting for error consumes time and energy. Thus, if this implicit cost could be increased, it may robustly alter how the brain learns from error. To vary the implicit cost of error, we designed a task that combined saccade adaptation with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task. We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased and the brain learned more from error. However, when error cost was small, the pupil constricted and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carries an implicit cost for the brain. Modulating this cost affects how much the brain learns from error.

Visual selective attention and the control of tracking eye movements: a critical review

People’s eyes are directed at objects of interest with the aim of acquiring visual information. However, processing this information is constrained in capacity, requiring task-driven and salience-driven attentional mechanisms to select few among the many available objects. A wealth of behavioral and neurophysiological evidence has demonstrated that visual selection and the motor selection of saccade targets rely on shared mechanisms. This coupling supports the premotor theory of visual attention put forth more than 30 years ago, postulating visual selection as a necessary stage in motor selection. In this review, we examine to which extent the coupling of visual and motor selection observed with saccades is replicated during ocular tracking. Ocular tracking combines catch-up saccades and smooth pursuit to foveate a moving object. We find evidence that ocular tracking requires visual selection of the speed and direction of the moving target, but the position of the motion signal may not coincide with the position of the pursuit target. Further, visual and motor selection can be spatially decoupled when pursuit is initiated (open-loop pursuit). We propose that a main function of coupled visual and motor selection is to serve the coordination of catch-up saccades and pursuit eye movements. A simple race-to-threshold model is proposed to explain the variable coupling of visual selection during pursuit, catch-up and regular saccades, while generating testable predictions. We discuss pending issues, such as disentangling visual selection from preattentive visual processing and response selection, and the pinpointing of visual selection mechanisms, which have begun to be addressed in the neurophysiological literature.

Passive Motor Learning: Oculomotor Adaptation in the Absence of Behavioral Errors

Motor adaptation is commonly thought to be a trial-and-error process in which the 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 movements 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 the monkeys to fixate on a target while covertly, an additional target initially moved in one direction and then changed direction after 250 ms. The monkeys showed a learned response to infrequent probe trials in which they were instructed to follow the moving target. Additional experiments confirmed that probing learning or residual eye movements during fixation did not drive learning. These results show that motor adaptation can be elicited in the absence of movement and provide an animal model for studying the implementation of passive motor learning. Current models assume that the interaction between movement and error signals underlies adaptive motor learning. Our results point to other mechanisms that may drive learning in the absence of movement.

Hypomania and saccadic changes in Parkinson's disease: influence of D2 and D3 dopaminergic signalling

In order to understand the influence of two dopaminergic signalling pathways, TaqIA rs1800497 (influencing striatal D2 receptor density) and Ser9Gly rs6280 (influencing the striatal D3 dopamine-binding affinity), on saccade generation and psychiatric comorbidities in Parkinson’s disease, this study aimed to investigate the association of saccadic performance in hypomanic or impulsive behaviour in parkinsonian patients; besides we questioned whether variants of D2 (A1+/A1−) and D3 (B1+/B1−) receptor polymorphism influence saccadic parameters differently, and if clinical parameters or brain connectivity changes modulate this association in the nigro-caudatal and nigro-collicular tract. Initially, patients and controls were compared regarding saccadic performance and differed in the parameter duration in memory-guided saccades (MGS) and visually guided saccades (VGS) trials (p < 0.0001) and in the MGS trial (p < 0.03). We were able to find associations between hypomanic behaviour (HPS) and saccade parameters (duration, latency, gain and amplitude) for both conditions [MGS (p = 0.036); VGS (p = 0.033)], but not for impulsive behaviour. For the A1 variant duration was significantly associated with HPS [VGS (p = 0.024); MGS (p = 0.033)]. In patients with the B1 variant, HPS scores were more consistently associated with duration [VGS (p = 0.005); MGS (p = 0.015), latency [VGS (p = 0.022)]] and amplitude [MGS (p = 0.006); VGS (p = 0.005)]. The mediation analysis only revealed a significant indirect effect for amplitude in the MGS modality for the variable UPDRS-ON (p < 0.05). All other clinical scales and brain connectivity parameters were not associated with behavioural traits. Collectively, our findings stress the role of striatal D2 and D3 signalling mechanisms in saccade generation and suggest that saccadic performance is associated with the clinical psychiatric state in Parkinson’s disease.

Encoding of eye movements explains reward-related activity in cerebellar simple spikes

The cerebellum exhibits both motor and reward-related signals. However, it remains unclear whether reward is processed independently from the motor command or might reflect the motor consequences of the reward drive. To test how reward-related signals interact with sensorimotor processing in the cerebellum, we recorded Purkinje cell simple spike activity in the cerebellar floccular complex while monkeys were engaged in smooth pursuit eye movement tasks. The color of the target signaled the size of the reward the monkeys would receive at the end of the target motion. When the tracking task presented a single target, both pursuit and neural activity were only slightly modulated by the reward size. The reward modulations in single cells were rarely large enough to be detected. These modulations were only significant in the population analysis when we averaged across many neurons. In two-target tasks where the monkey learned to select based on the size of the reward outcome, both behavior and neural activity adapted rapidly. In both the single- and two-target tasks, the size of the reward-related modulation matched the size of the effect of reward on behavior. Thus, unlike cortical activity in eye movement structures, the reward-related signals could not be dissociated from the motor command. These results suggest that reward information is integrated with the eye movement command upstream of the Purkinje cells in the floccular complex. Thus reward-related modulations of the simple spikes are akin to modulations found in motor behavior and not to the central processing of the reward value.NEW & NOTEWORTHY Disentangling sensorimotor and reward signals is only possible if these signals do not completely overlap. We recorded activity in the floccular complex of the cerebellum while monkeys performed tasks designed to separate representations of reward from those of movement. Activity modulation by reward could be accounted for by the coding of eye movement parameters, suggesting that reward information is already integrated into motor commands upstream of the floccular complex.

Cerebellar climbing fibers encode expected reward size

Climbing fiber inputs to the cerebellum encode error signals that instruct learning. Recently, evidence has accumulated to suggest that the cerebellum is also involved in the processing of reward. To study how rewarding events are encoded, we recorded the activity of climbing fibers when monkeys were engaged in an eye movement task. At the beginning of each trial, the monkeys were cued to the size of the reward that would be delivered upon successful completion of the trial. Climbing fiber activity increased when the monkeys were presented with a cue indicating a large reward, but not a small reward. Reward size did not modulate activity at reward delivery or during eye movements. Comparison between climbing fiber and simple spike activity indicated different interactions for coding of movement and reward. These results indicate that climbing fibers encode the expected reward size and suggest a general role of the cerebellum in associative learning beyond error correction.

Using extracellular low frequency signals to improve the spike sorting of cerebellar complex spikes

BACKGROUND

The challenge of spike sorting has been addressed by numerous electrophysiological studies. These methods tend to focus on the information conveyed by the high frequencies, but ignore the potentially informative signals at lower frequencies. Activation of Purkinje cells in the cerebellum by input from the climbing fibers results in a large amplitude dendritic spike concurrent with a high-frequency burst known as a complex spike. Due to the variability in the high-frequency component of complex spikes, previous methods have struggled to sort these complex spikes in an accurate and reliable way. However, complex spikes have a prominent extracellular low-frequency signal generated by the input from the climbing fibers, which can be exploited for complex spike sorting.

NEW METHOD

We exploited the low-frequency signal (20-400 Hz) to improve complex spike sorting by applying Principal Component Analysis (PCA).

RESULTS AND COMPARISONS

The low-frequency first PC achieves a better separation of the complex spikes from noise. The low-frequency data facilitate the detection of events entering into the analysis, and therefore can be harnessed to analyze the data with a larger signal to noise ratio. These advantages make this method more effective for complex spike sorting than methods restricted to the high-frequency signal (> 600 Hz).

CONCLUSIONS

Gathering low frequency data can improve spike sorting. This is illustrated for the case of complex spikes in the cerebellum. Our characterization of the dendritic low-frequency components of complex spikes can be applied elsewhere to gain insights into processing in the cerebellum.

Reinforcement effects in anticipatory smooth eye movements

When predictive information about target motion is available, anticipatory smooth pursuit eye movements (aSPEM) are consistently generated before target appearance, thereby reducing the typical sensorimotor delay between target motion onset and foveation. By manipulating the probability for target motion direction, we were able to bias the direction and mean velocity of aSPEM. This suggests that motion-direction expectancy has a strong effect on the initiation of anticipatory movements. To further understand the nature of anticipatory smooth eye movements, we investigated different effects of reinforcement on aSPEM. In a first experiment, the reinforcement was contingent to a particular anticipatory behavior. A monetary reward was associated to a criterion-matching anticipatory velocity as estimated online during the gap before target motion onset. Our results showed a small but significant effect of behavior-contingent monetary reward on aSPEM. In a second experiment, the proportion of rewarded trials was manipulated across motion directions (right vs. left) independently from participants' behavior. Our results indicate that a bias in expected reward does not systematically affect anticipatory eye movements. Overall, these findings strengthen the notion that anticipatory eye movements can be considered as an operant behavior (similar to visually guided ones), whereas the expectancy for a noncontingent reward cannot efficiently bias them.

Encoding of Reward and Decoding Movement from the Frontal Eye Field during Smooth Pursuit Eye Movements

Expectation of reward potentiates sensorimotor transformations to drive vigorous movements. One of the main challenges in studying reward is to determine how representations of reward interact with the computations that drive behavior. We recorded activity in smooth pursuit neurons in the frontal eye field (FEF) of two male rhesus monkeys while controlling the eye speed by manipulating either reward size or target speed. The neurons encoded the different reward conditions more strongly than the different target speed conditions. This pattern could not be explained by differences in the eye speed, since the eye speed sensitivity of the neurons was also larger for the reward conditions. Pooling the responses by the preferred direction of the neurons attenuated the reward modulation and led to a tighter association between neural activity and behavior. Therefore, a plausible decoder such as the population vector could explain how the FEF both drives behavior and encodes reward beyond behavior.SIGNIFICANCE STATEMENT Motor areas combine sensory and reward information to drive movement. To disambiguate these sources, we manipulated the speed of smooth pursuit eye movements by controlling either the size of the reward or the speed of the visual motion signals. We found that the relationship between activity in frontal eye field and eye kinematics varied: the eye speed sensitivity was larger for the different reward conditions than for the different target speed conditions. Decoders that pooled signals by the preferred direction of the neurons attenuated the reward modulations. These decoders may indicate how reward can be both encoded beyond eye kinematics at the single neuron level and drive movement at the population level.

Multiple components in direction learning in smooth pursuit eye movements of monkeys

We analyzed behavioral features of smooth pursuit eye movements to characterize the course of acquisition and expression of multiple neural components of motor learning. Monkeys tracked a target that began to move in an initial "pursuit" direction and suddenly, but predictably, changed direction after a fixed interval of 250 ms. As the trial is repeated, monkeys learn to make eye movements that predict the change in target direction. Quantitative analysis of the learned response revealed evidence for multiple, dynamic, parallel processes at work during learning. 1) The overall learning followed at least two trial courses: a fast component grew and saturated rapidly over tens of trials, and a slow component grew steadily over up to 1,000 trials. 2) The temporal specificity of the learned response within each trial was crude during the first 100 trials but then improved gradually over the remaining trials. 3) External influences on the gain of pursuit initiation modulate the expression but probably not the acquisition of learning. The gain of pursuit initiation and the expression of the learned response decreased in parallel, both gradually through a 1,000-trial learning block and immediately between learning trials with different gains in the initiation of pursuit. We conclude that at least two distinct neural mechanisms drive the acquisition of pursuit learning over 100 to 1,000 trials (3 to 30 min). Both mechanisms generate underlying memory traces that are modulated in relation to the gain of pursuit initiation before expression in the final motor output. NEW & NOTEWORTHY We show that cerebellum-dependent direction learning in smooth pursuit eye movements grows in at least two components over 1,100 behavioral learning repetitions. One component grows over tens of trials and the other over hundreds. Within trials, learned temporal specificity gradually improves over hundreds of trials. The expression of each learning component on a given trial can be modified by external factors that do not affect the underlying memory trace.

Smooth Pursuit Eye Movement of Monkeys Naive to Laboratory Setups With Pictures and Artificial Stimuli

When animal behavior is studied in a laboratory environment, the animals are often extensively trained to shape their behavior. A crucial question is whether the behavior observed after training is part of the natural repertoire of the animal or represents an outlier in the animal’s natural capabilities. This can be investigated by assessing the extent to which the target behavior is manifested during the initial stages of training and the time course of learning. We explored this issue by examining smooth pursuit eye movements in monkeys naïve to smooth pursuit tasks. We recorded the eye movements of monkeys from the 1st days of training on a step-ramp paradigm. We used bright spots, monkey pictures and scrambled versions of the pictures as moving targets. We found that during the initial stages of training, the pursuit initiation was largest for the monkey pictures and in some direction conditions close to target velocity. When the pursuit initiation was large, the monkeys mostly continued to track the target with smooth pursuit movements while correcting for displacement errors with small saccades. Two weeks of training increased the pursuit eye velocity in all stimulus conditions, whereas further extensive training enhanced pursuit slightly more. The training decreased the coefficient of variation of the eye velocity. Anisotropies that grade pursuit across directions were observed from the 1st day of training and mostly persisted across training. Thus, smooth pursuit in the step-ramp paradigm appears to be part of the natural repertoire of monkeys’ behavior and training adjusts monkeys’ natural predisposed behavior.

Dissecting patterns of preparatory activity in the frontal eye fields during pursuit target selection

We investigated the composition of preparatory activity of frontal eye field (FEF) neurons in monkeys performing a pursuit target selection task. In response to the orthogonal motion of a large and a small reward target, monkeys initiated pursuit biased toward the direction of large reward target motion. FEF neurons exhibited robust preparatory activity preceding movement initiation in this task. Preparatory activity consisted of two components, ramping activity that was constant across target selection conditions, and a flat offset in firing rates that signaled the target selection condition. Ramping activity accounted for 50% of the variance in the preparatory activity and was linked most strongly, on a trial-by-trial basis, to pursuit eye movement latency rather than to its direction or gain. The offset in firing rates that discriminated target selection conditions accounted for 25% of the variance in the preparatory activity and was commensurate with a winner-take-all representation, signaling the direction of large reward target motion rather than a representation that matched the parameters of the upcoming movement. These offer new insights into the role that the frontal eye fields play in target selection and pursuit control. They show that preparatory activity in the FEF signals more strongly when to move rather than where or how to move and suggest that structures outside the FEF augment its contributions to the target selection process.NEW & NOTEWORTHY We used the smooth eye movement pursuit system to link between patterns of preparatory activity in the frontal eye fields and movement during a target selection task. The dominant pattern was a ramping signal that did not discriminate between selection conditions and was linked, on trial-by-trial basis, to movement latency. A weaker pattern was composed of a constant signal that discriminated between selection conditions but was only weakly linked to the movement parameters.

What Makes You Go Faster?: The Effect of Reward on Speeded Action under Risk

Evaluating the potential reward and risk associated with a choice of action plays an important role in everyday decision making. However, the details behind how reward and risk affect the decisions for actions remain unclear. The present study investigates the influence of reward and risk on a decision to make a speeded motor response. One hundred and ten college students performed a Speed-Rewarded Go-NoGo task during which they were rewarded proportionally based on the speed and accuracy of their response. On each trial, the magnitude of potential reward and the probability of a forthcoming Go signal (Go-probability) were presented prior to the Go or NoGo signal. Personality traits, such as risk taking and impulsive tendencies, were measured to determine their contribution in explaining individual differences in task performance. The results showed that larger amount of rewards can motivate people to respond faster, and this effect was modulated by the assessed risk, suggesting that decisions for actions are based on a systematic trade-off between rewards and risks. Moreover, when the assessed risk was high, individuals with greater risk taking and impulsive tendencies did not adequately adjust their behavior across different reward levels. These findings shed light on the mechanistic understanding of the effect of reward and risk on decisions for a speeded action.

Selective reward affects the rate of saccade adaptation

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). In half of the experiments (n=10), we rewarded monkeys only when they made leftward saccades and in the other half (n=10) 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.

Interactions between target location and reward size modulate the rate of microsaccades in monkeys

We have studied how rewards modulate the occurrence of microsaccades by manipulating the size of an expected reward and the location of the cue that sets the expectations for future reward. We found an interaction between the size of the reward and the location of the cue. When monkeys fixated on a cue that signaled the size of future reward, the frequency of microsaccades was higher if the monkey expected a large vs. a small reward. When the cue was presented at a site in the visual field that was remote from the position of fixation, reward size had the opposite effect: the frequency of microsaccades was lower when the monkey was expecting a large reward. The strength of pursuit initiation also was affected by reward size and by the presence of microsaccades just before the onset of target motion. The gain of pursuit initiation increased with reward size and decreased when microsaccades occurred just before or after the onset of target motion. The effect of the reward size on pursuit initiation was much larger than any indirect effects reward might cause through modulation of the rate of microsaccades. We found only a weak relationship between microsaccade direction and the location of the exogenous cue relative to fixation position, even in experiments where the location of the cue indicated the direction of target motion. Our results indicate that the expectation of reward is a powerful modulator of the occurrence of microsaccades, perhaps through attentional mechanisms.

Cerebellar encoding of multiple candidate error cues in the service of motor learning

For learning to occur through trial and error, the nervous system must effectively detect and encode performance errors. To examine this process, we designed a set of oculomotor learning tasks with more than one visual object providing potential error cues, as would occur in a natural visual scene. A task-relevant visual target and a task-irrelevant visual background both influenced vestibulo-ocular reflex learning in rhesus monkeys. Thus, motor learning does not identify a single error cue based on behavioral relevance, but can be simultaneously influenced by more than one cue. Moreover, the relative weighting of the different cues could vary. If the speed of the visual target's motion on the retina was low (≪1°/s), background motion dominated learning, but if target speed was high, the effects of the background were suppressed. The target and background motion had similar, nonlinear effects on the putative neural instructive signals carried by cerebellar climbing fibers, but with a stronger influence of the background on the climbing fibers than on learning. In contrast, putative neural instructive signals carried by the simple spikes of Purkinje cells were influenced solely by the motion of the visual target. Because they are influenced by different cues during training, joint control of learning by the climbing fibers and Purkinje cells may expand the learning capacity of the cerebellar circuit.

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

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.