In the Active Perception Laboratory, mathematical and computational models are used to study the impact of behavior on visual computations and the establishment of representations.

On the basis of a joint analysis of the characteristics of fixational eye movements and the statistics of natural scenes, we have proposed a theory of the advantages of acquiring visual information by means of continually jittering eyes (read more). This theory presents a new picture of early visual processing. It replaces the traditional view of the early visual system as a passive encoding stage with the more complex view that neurons in the early pathway are part of an active strategy of visual processing and feature extraction, whose function can only be understood in conjunction with eye movements. It argues that information about fine spatial detail is not just stored in spatial maps of neural activity, as commonly assumed, but also in the temporal structure of the responses of neuronal ensembles, a dynamics critically shaped by eye movements and therefore potentially under task control. It implies different encoding/decoding mechanisms for spatial information than the ones commonly postulated, mechanisms reminiscent of those of motion perception. It suggests that, rather than an inflexible encoding stage designed to optimize the average transmission of information, the retina and fixational eye movements, together, constitute an adaptive system whose properties can be rapidly adjusted to optimize performance on the specific task. Furthermore, it predicts that eye movements contribute to fundamental properties of spatial vision currently attributed to neural mechanisms alone.

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Spatiotemporal input to the retina during two different visual fixations: (a) a perfectly steady fixation in which the retinal image is immobile; and (b) an unstable fixation in which the retina moved as a random walk. For simplicity, a visual scene with a single spatial dimension is considered. In both (a) and (b), the top panel illustrates the one-dimensional scene and the retina. The two bottom panels show the visual input signals in both the spaceľ time domain (left) and the frequency domain (right).



Several of the predictions of this theory have now been confirmed. This include the findings that fixational eye movements enhance high spatial frequency vision (Rucci et al., 2007) and operate a spatiotemporal redistribution of the input energy that precisely counterbalances the spectral characteristics of natural scenes (Kuang et al., 2012). This effect has major implications for the way visual information is encoded in the retina and challenges traditional information-theoretic views.

We have also used neural models to examine the impact of exposure to a continually moving retinal image during development. These studies have raised the hypothesis that the retinal image motion resulting from fixational eye movements plays an important role in the maturation of fundamental response properties of neurons in the striate cortex. Abnormal fixational eye movements occur in many conditions. According to our studies, some of the visual deficits associated with these disorders may actually be a consequence of chronic exposure to altered oculomotor activity (read more).