Kalman filtering naturally accounts for visually guided and predictive smooth pursuit dynamics.

The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.

Dramatic impairment of prediction due to frontal lobe degeneration.

Prediction is essential for motor function in everyday life. For instance, predictive mechanisms improve the perception of a moving target by increasing eye speed anticipatively, thus reducing motion blur on the retina. Subregions of the frontal lobes play a key role in eye movements in general and in smooth pursuit in particular, but their precise function is not firmly established. Here, the role of frontal lobes in the timing of predictive action is demonstrated by studying predictive smooth pursuit during transient blanking of a moving target in mild frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD) patients. While control subjects and AD patients predictively reaccelerated their eyes before the predicted time of target reappearance, FTLD patients did not. The difference was so dramatic (classification accuracy >90%) that it could even lead to the definition of a new biomarker. In contrast, anticipatory eye movements triggered by the disappearance of the fixation point were still present before target motion onset in FTLD patients and visually guided pursuit was normal in both patient groups compared with controls. Therefore, FTLD patients were only impaired when the predicted timing of an external event was required to elicit an action. These results argue in favor of a role of the frontal lobes in predictive movement timing.

Biological motion influences the visuomotor transformation for smooth pursuit eye movements.

Humans are very sensitive to the presence of other living persons or animals in their surrounding. Human actions can readily be perceived, even in a noisy environment. We recently demonstrated that biological motion, which schematically represents human motion, influences smooth pursuit eye movements during the initiation period (Orban de Xivry, Coppe, Lefèvre, & Missal, 2010). This smooth pursuit response is driven both by a visuomotor pathway, which transforms retinal inputs into motor commands, and by a memory pathway, which is directly related to the predictive properties of smooth pursuit. To date, it is unknown which of these pathways is influenced by biological motion. In the present study, we first use a theoretical model to demonstrate that an influence of biological motion on the visuomotor and memory pathways might both explain its influence on smooth pursuit initiation. In light of this model, we made theoretical predictions of the possible influence of biological motion on smooth pursuit during and after the transient blanking of the stimulus. These qualitative predictions were then compared with recordings of eye movements acquired before, during and after the transient blanking of the stimulus. The absence of difference in smooth pursuit eye movements during blanking of the stimuli and the stronger visually guided smooth pursuit reacceleration after reappearance of the biological motion stimuli in comparison with control stimuli suggests that biological motion influences the visuomotor pathway but not the memory pathway.

Biological motion drives perception and action.

Presenting a few dots moving coherently on a screen can yield to the perception of human motion. This perception is based on a specific network that is segregated from the traditional motion perception network and that includes the superior temporal sulcus (STS). In this study, we investigate whether this biological motion perception network could influence the smooth pursuit response evoked by a point-light walker. We found that smooth eye velocity during pursuit initiation was larger in response to the point-light walker than in response to one of its scrambled versions, to an inverted walker or to a single dot stimulus. In addition, we assessed the proximity to the point-light walker (i.e. the amount of information about the direction contained in the scrambled stimulus and extracted from local motion cue of biological motion) of each of our scrambled stimuli in a motion direction discrimination task with manual responses and found that the smooth pursuit response evoked by those stimuli moving across the screen was modulated by their proximity to the walker. Therefore, we conclude that biological motion facilitates smooth pursuit eye movements, hence influences both perception and action.