Shared sensory estimates for human motion perception and pursuit eye movements

Bayesian approaches to smooth pursuit of random dot kinematograms: effects of varying RDK noise and the predictability of RDK direction

Smooth pursuit eye movements respond on the basis of both immediate and anticipated target motion, where anticipations may be derived from either memory or perceptual cues. To study the combined influence of both immediate sensory motion and anticipation, subjects pursued clear or noisy random dot kinematograms (RDKs) whose mean directions were chosen from Gaussian distributions with SDs = 10° (narrow prior) or 45° (wide prior). Pursuit directions were consistent with Bayesian theory in that transitions over time from dependence on the prior to near total dependence on immediate sensory motion (likelihood) took longer with the noisier RDKs and with the narrower, more reliable, prior. Results were fit to Bayesian models in which parameters representing the variability of the likelihood either were or were not constrained to be the same for both priors. The unconstrained model provided a statistically better fit, with the influence of the prior in the constrained model smaller than predicted from strict reliability-based weighting of prior and likelihood. Factors that may have contributed to this outcome include prior variability different from nominal values, low-level sensorimotor learning with the narrow prior, or departures of pursuit from strict adherence to reliability-based weighting. Although modifications of, or alternatives to, the normative Bayesian model will be required, these results, along with previous studies, suggest that Bayesian approaches are a promising framework to understand how pursuit combines immediate sensory motion, past history, and informative perceptual cues to accurately track the target motion that is most likely to occur in the immediate future.NEW & NOTEWORTHY Smooth pursuit eye movements respond on the basis of anticipated, as well as immediate, target motions. Bayesian models using reliability-based weighting of previous (prior) and immediate target motions (likelihood) accounted for many, but not all, aspects of pursuit of clear and noisy random dot kinematograms with different levels of predictability. Bayesian approaches may solve the long-standing problem of how pursuit combines immediate sensory motion and anticipation of future motion to configure an effective response.

Tracking and perceiving diverse motion signals: Directional biases in human smooth pursuit and perception

Human smooth pursuit eye movements and motion perception behave similarly when observers track and judge the motion of simple objects, such as dots. But moving objects in our natural environment are complex and contain internal motion. We ask how pursuit and perception integrate the motion of objects with motion that is internal to the object. Observers (n = 20) tracked a moving random-dot kinematogram with their eyes and reported the object’s perceived direction. Objects moved horizontally with vertical shifts of 0, ±3, ±6, or ±9° and contained internal dots that were static or moved ±90° up/down. Results show that whereas pursuit direction was consistently biased in the direction of the internal dot motion, perceptual biases differed between observers. Interestingly, the perceptual bias was related to the magnitude of the pursuit bias (r = 0.75): perceptual and pursuit biases were directionally aligned in observers that showed a large pursuit bias, but went in opposite directions in observers with a smaller pursuit bias. Dissociations between perception and pursuit might reflect different functional demands of the two systems. Pursuit integrates all available motion signals in order to maximize the ability to monitor and collect information from the whole scene. Perception needs to recognize and classify visual information, thus segregating the target from its context. Ambiguity in whether internal motion is part of the scene or contributes to object motion might have resulted in individual differences in perception. The perception-pursuit correlation suggests shared early-stage motion processing or perception-pursuit interactions.

Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probed human speed representation using ocular tracking of stimuli of different statistics. First, we compared ocular responses to single drifting gratings (DGs) with a given set of spatiotemporal frequencies to broadband motion clouds (MCs) of matched mean frequencies. Motion energy distributions of gratings and clouds are point-like, and ellipses oriented along the constant speed axis, respectively. Sampling frequency space, MCs elicited stronger, less variable, and speed-tuned responses. DGs yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supralinearly integrated along an iso-velocity line and sublinearly integrated away from it. A dynamical probabilistic model characterizes these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal "scale" axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organized along speed and scale axes.

The role of vestibular cues in postural sway

Controlling posture requires continuous sensory feedback about body motion and orientation, including from the vestibular organs. Little is known about the role of tilt vs. translation vs. rotation vestibular cues. We examined whether intersubject differences in vestibular function were correlated with intersubject differences in postural control. Vestibular function was assayed using vestibular direction-recognition perceptual thresholds, which determine the smallest motion that can be reliably perceived by a subject seated on a motorized platform in the dark. In study A, we measured thresholds for lateral translation, vertical translation, yaw rotation, and head-centered roll tilts. In study B, we measured thresholds for roll, pitch, and left anterior-right posterior and right anterior-left posterior tilts. Center-of-pressure (CoP) sway was measured in sensory organization tests (study A) and Romberg tests (study B). We found a strong positive relationship between CoP sway and lateral translation thresholds but not CoP sway and other thresholds. This finding suggests that the vestibular encoding of lateral translation may contribute substantially to balance control. Since thresholds assay sensory noise, our results support the hypothesis that vestibular noise contributes to spontaneous postural sway. Specifically, we found that lateral translation thresholds explained more of the variation in postural sway in postural test conditions with altered proprioceptive cues (vs. a solid surface), consistent with postural sway being more dependent on vestibular noise when the vestibular contribution to balance is higher. These results have potential implications for vestibular implants, balance prostheses, and physical therapy exercises.NEW & NOTEWORTHY Vestibular feedback is important for postural control, but little is known about the role of tilt cues vs. translation cues vs. rotation cues. We studied healthy human subjects with no known vestibular pathology or symptoms. Our findings showed that vestibular encoding of lateral translation correlated with medial-lateral postural sway, consistent with lateral translation cues contributing to balance control. This adds support to the hypothesis that vestibular noise contributes to spontaneous postural sway.

Confidence in predicted position error explains saccadic decisions during pursuit

A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors, and the timing of saccades can be highly variable. The observed distributions of saccade frequency and trigger time remain poorly understood, and this decision process remains imprecisely quantified. Here, we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioral observations on the frequency and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions that saccade decisions are highly sensitive to uncertainty for small predicted position errors, but this influence diminishes as the magnitude of predicted position error increases. We suggest that this predictive, confidence-based decision-making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.NEW & NOTEWORTHY This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a threshold. This model explains saccade frequency and trigger time distributions across target trajectories and makes novel predictions about the influence of sensory uncertainty in saccade decisions during pursuit.

Properties of smooth pursuit and visual motion reaction time to second-order motion stimuli

A large number of psychophysical and neurophysiological studies have demonstrated that smooth pursuit eye movements are tightly related to visual motion perception. This could be due to the fact that visual motion sensitive cortical areas such as meddle temporal (MT), medial superior temporal (MST) areas are involved in motion perception as well as pursuit initiation. Although the directional-discrimination and perceived target velocity tasks are used to evaluate visual motion perception, it is still uncertain whether the speed of visual motion perception, which is determined by visuomotor reaction time (RT) to a small target, is related to pursuit initiation. Therefore, we attempted to determine the relationship between pursuit latency/acceleration and the visual motion RT which was measured to the visual motion stimuli that moved leftward or rightward. The participants were instructed to fixate on a stationary target and press one of the buttons corresponding to the direction of target motion as soon as possible once the target starts to move. We applied five different visual motion stimuli including first- and second-order motion for smooth pursuit and visual motion RT tasks. It is well known that second-order motion induces lower retinal image motion, which elicits weaker responses in MT and MST compared to first-order motion stimuli. Our results showed that pursuit initiation including latency and initial eye acceleration were suppressed by second-order motion. In addition, second-order motion caused a delay in visual motion RT. The better performances in both pursuit initiation and visual motion RT were observed for first-order motion, whereas second-order (theta motion) induced remarkable deficits in both variables. Furthermore, significant Pearson's correlation and within-subjects correlation coefficients were obtained between visual motion RT and pursuit latency/acceleration. Our findings support the suggestion that there is a common neuronal pathway involved in both pursuit initiation and the speed of visual motion perception.

The relationship of olfactory function and clinical traits in major depressive disorder

People who have developed a good sense of smell could experience much more happiness and pleasure, which would trigger a discussion that olfactory disorder might correlate with the pathogenesis of major depressive disorder (MDD). Similar experiments conducted on rats have confirmed that nerve damage of olfactory pathway can induce a series of depression-like changes, including behavior, neurobiochemistry, and neuroimmunity. These changes will recover progressively with anti-depression treatment. While in similar studies on human beings, olfactory dysfunction has been found in people suffering from depression. This review briefly discusses the correlation between olfactory deficits and clinical traits of depression in different dimensions, such as the severity, duration and cognitive impairment of depression. Improving olfactory function may be expected to be a potential antidepressant therapy.

Comparison of the precision of smooth pursuit in humans and head unrestrained monkeys

Direct comparison of results of humans and monkeys is often complicated by differences in experimental conditions. We replicated in head unrestrained macaques experiments of a recent study comparing human directional precision during smooth pursuit eye movements (SPEM) and saccades to moving targets (Braun & Gegenfurtner, 2016). Directional precision of human SPEM follows an exponential decay function reaching optimal values of 1.5°-3° within 300 ms after target motion onset, whereas precision of initial saccades to moving targets is slightly better. As in humans, we found general agreement in the devel-opment of directional precision of SPEM over time and in the differences between direc-tional precision of initial saccades and SPEM initiation. However, monkeys showed over-all lower precision in SPEM compared to humans. This was most likely due to differences in experimental conditions, such as in the stabilization of the head, which was by a chin and a head rest in human subjects and unrestrained in monkeys.

State dependence of stimulus-induced variability tuning in macaque MT

Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state.

The brain controls behavior fluidly in a wide variety of cognitive contexts that alter the precision of neural responses. We examine how neural variability changes versus the mean response as a function of the stimulus and the behavioral state. We show that this scaled variability can have qualitatively different stimulus tuning in different behavioral contexts. In alert primates, scaled variability is tuned to the direction of motion of a visual stimulus and decreases around the preferred direction of each neuron. Under anesthesia, neurons show flat scaled variability tuning and, overall, responses are significantly more variable. We develop a simple model that includes a parameter describing firing rate gain fluctuations that can explain these changes. Our results suggest that tuned decreases in scaled variability during wakefulness may be mediated by an active process that suppresses synchronization and makes information transmission more reliable.

Discrimination of curvature from motion during smooth pursuit eye movements and fixation

Smooth pursuit and motion perception have mainly been investigated with stimuli moving along linear trajectories. Here we studied the quality of pursuit movements to curved motion trajectories in human observers and examined whether the pursuit responses would be sensitive enough to discriminate various degrees of curvature. In a two-interval forced-choice task subjects pursued a Gaussian blob moving along a curved trajectory and then indicated in which interval the curve was flatter. We also measured discrimination thresholds for the same curvatures during fixation. Motion curvature had some specific effects on smooth pursuit properties: trajectories with larger amounts of curvature elicited lower open-loop acceleration, lower pursuit gain, and larger catch-up saccades compared with less curved trajectories. Initially, target motion curvatures were underestimated; however, ∼300 ms after pursuit onset pursuit responses closely matched the actual curved trajectory. We calculated perceptual thresholds for curvature discrimination, which were on the order of 1.5 degrees of visual angle (°) for a 7.9° curvature standard. Oculometric sensitivity to curvature discrimination based on the whole pursuit trajectory was quite similar to perceptual performance. Oculometric thresholds based on smaller time windows were higher. Thus smooth pursuit can quite accurately follow moving targets with curved trajectories, but temporal integration over longer periods is necessary to reach perceptual thresholds for curvature discrimination.NEW & NOTEWORTHY Even though motion trajectories in the real world are frequently curved, most studies of smooth pursuit and motion perception have investigated linear motion. We show that pursuit initially underestimates the curvature of target motion and is able to reproduce the target curvature ∼300 ms after pursuit onset. Temporal integration of target motion over longer periods is necessary for pursuit to reach the level of precision found in perceptual discrimination of curvature.

Spatiotemporal Filter for Visual Motion Integration from Pursuit Eye Movements in Humans and Monkeys

Despite the enduring interest in motion integration, a direct measure of the space-time filter that the brain imposes on a visual scene has been elusive. This is perhaps because of the challenge of estimating a 3D function from perceptual reports in psychophysical tasks. We take a different approach. We exploit the close connection between visual motion estimates and smooth pursuit eye movements to measure stimulus-response correlations across space and time, computing the linear space-time filter for global motion direction in humans and monkeys. Although derived from eye movements, we find that the filter predicts perceptual motion estimates quite well. To distinguish visual from motor contributions to the temporal duration of the pursuit motion filter, we recorded single-unit responses in the monkey middle temporal cortical area (MT). We find that pursuit response delays are consistent with the distribution of cortical neuron latencies and that temporal motion integration for pursuit is consistent with a short integration MT subpopulation. Remarkably, the visual system appears to preferentially weight motion signals across a narrow range of foveal eccentricities rather than uniformly over the whole visual field, with a transiently enhanced contribution from locations along the direction of motion. We find that the visual system is most sensitive to motion falling at approximately one-third the radius of the stimulus aperture. Hypothesizing that the visual drive for pursuit is related to the filtered motion energy in a motion stimulus, we compare measured and predicted eye acceleration across several other target forms.SIGNIFICANCE STATEMENT A compact model of the spatial and temporal processing underlying global motion perception has been elusive. We used visually driven smooth eye movements to find the 3D space-time function that best predicts both eye movements and perception of translating dot patterns. We found that the visual system does not appear to use all available motion signals uniformly, but rather weights motion preferentially in a narrow band at approximately one-third the radius of the stimulus. Although not universal, the filter predicts responses to other types of stimuli, demonstrating a remarkable degree of generalization that may lead to a deeper understanding of visual motion processing.

Efficient sensory cortical coding optimizes pursuit eye movements

In the natural world, the statistics of sensory stimuli fluctuate across a wide range. In theory, the brain could maximize information recovery if sensory neurons adaptively rescale their sensitivity to the current range of inputs. Such adaptive coding has been observed in a variety of systems, but the premise that adaptation optimizes behaviour has not been tested. Here we show that adaptation in cortical sensory neurons maximizes information about visual motion in pursuit eye movements guided by that cortical activity. We find that gain adaptation drives a rapid (<100 ms) recovery of information after shifts in motion variance, because the neurons and behaviour rescale their sensitivity to motion fluctuations. Both neurons and pursuit rapidly adopt a response gain that maximizes motion information and minimizes tracking errors. Thus, efficient sensory coding is not simply an ideal standard but a description of real sensory computation that manifests in improved behavioural performance.

Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG

This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia.

•

The brain encodes states of the world probabilistically with means and precisions.

•

Precision (inverse variance) may be encoded by the synaptic gain of pyramidal cells.

•

We estimate subjects' sensory precision using a model of oculomotor pursuit and DCM.

•

We estimate subjects' synaptic gain in V1 using DCM of MEG data during pursuit.

•

Estimates of synaptic gain in V1 and sensory precision are significantly correlated.