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Nearly all imaginable human activities rest on a context-appropriate dynamic control of the flow of retinal data into the nervous system via eye movements. The brain’s task is to move the eyes so as to exert intelligent predictive control over the informational content of the retinal data stream. An intelligent oculomotor controller would first model future contingent upon each possible next action in the oculomotor repertoire, then rank-order the repertoire by assigning a value v(a,t) to each possible action a at each time t, and execute the oculomotor action with the highest predicted value each time. We present a striking evidence of such an intelligent neural control of human eyes in a laboratory task of visual search for a small target camouflaged by a natural-like stochastic texture, a task in which the value of fixating a given location naturally corresponds to the expected information gain about the unknown location of the target. Human searchers behave as if maintaining a map of beliefs (represented as probabilities) about the target location, updating their beliefs with visual data obtained on each fixation optimally using the Bayes Rule. On average, human eye movement patterns appear remarkably consistent with an intelligent strategy of moving eyes to maximize the expected information gain, but inconsistent with the strategy of always foveating the currently most likely location of the target (a prevalent intuition in the existing theories). We derive principled, simple, accurate, and robust mathematical formulas to compute belief and information value maps across the search area on each fixation (or time step). The formulas are exact expressions in the limiting cases of small amount of information extracted, which occurs when the number of potential target locations is infinite, or when the time step is vanishingly small (used for online control of fixation duration). Under these circumstances, the computation of information value map reduces to a linear filtering of beliefs on each time step, and beliefs can be maintained simply as running weighted averages. A model algorithm employing these simple computations captures many statistical properties of human eye movements in our search task. / text

Microsaccades are an important component of the small eye movements that constitute fixation, the basis of visual perception. The specific function of microsaccades has been a long-standing research problem. Only recently, conclusive evidence emerged, showing that microsaccades aid both visual perception and oculomotor control. The main goal of this thesis was to improve our understanding of the implementation of microsaccade generation within the circuitry of saccade control, an unsolved issue in oculomotor research. We make a case for a model according to which microsaccades and saccades result from mutually dependent motor plans, competing for expression. The model consists of an activation field, coding for fixation at its center and for saccades at peripheral locations; saccade amplitude increases with eccentricity. Activity during fixation spreads to slightly peripheral locations in the field and, thus, may result in the generation of microsaccades. Inhibition of remote and excitation of neighbouring locations govern the dynamics of the field, resulting in a strong competition between fixation and saccade generation. We propose that this common-field model of microsaccade and saccade generation finds a neurophysiological counterpart in the motor map of the superior colliculus (SC), a key brainstem structure involved in the generation of saccades. In a series of five behavioral experiments, we tested implications of the model. Predictions were derived concerning (1) the behavior of microsaccades in a given task (microsaccade rate, amplitude, and direction), (2) the interactions of microsaccades and subsequent saccades, and (3) the relationship between microsaccadic behavior and neurophysiological processes at the level of the SC. The results yielded strong support for the model at all three levels of analysis, suggesting that microsaccade statistics are indicative of the state of the fixation-related part of the SC motor map. / Mikrosakkaden sind ein wichtiger Bestandteil der kleinen Augenbewegungen, aus denen Fixationen, die Basis der visuellen Wahrnehmung, bestehen. Neuere Arbeiten erbrachten schlüssige Evidenz dafür, das Mikrosakkaden eine wichtige Rolle in der Wahrnehmung und der Blickbewegungskontrolle spielen. Hauptanliegen dieser Dissertation war es, unser Verständnis der Implementierung der Generierung von Mikrosakkaden im Kreislauf der Sakkadensteuerung zu vertiefen. Wir schlagen ein Modell vor, in dem Mikrosakkaden und Sakkaden konkurrierende Bewegungsprogramme darstellen, die um ihre Umsetzung wettstreiten. Das Modell besteht aus einem Aktivationsfeld, in dem Fixation im Zentrum und Sakkaden in der Peripherie repräsentiert sind (Sakkadenamplitude steigt mit der Exzentrizität). Aktivität während der Fixation breitet sich zu leicht peripheren Orten im Feld aus und kann so zur Generierung von Mikrosakkaden führen. Hemmung von entfernten und Erregung von benachbarten Orten bestimmen die Dynamik im Feld, was zu einem starken Wettstreit zwischen Fixation und Sakkadengenerierung beiträgt. Wir schlagen vor, dass dieses common-field model of microsaccade and saccade generation ein neurophysiologisches Pendant in der Bewegungskarte des colliculus superior (CS) findet, einer Struktur im Hirnstamm, die im starken Zusammenhang mit der Entstehung von Sakkaden steht. In fünf behavioralen Experimenten wurden Implikationen des Modells überprüft. Vorhersagen wurden auf drei Ebenen abgeleitet: (1) Verhalten der Mikrosakkaden in bestimmten Aufgaben (Mikrosakkadenrate, -amplitude und -richtung), (2) Interaktionen von Mikrosakkaden und nachfolgenden Sakkaden, (3) der Zusammenhang zwischen Mikrosakkadenverhalten und neurophysiologischen Prozessen auf der Ebene des CS. Die Ergebnisse unterstützten das Modell auf allen drei Analyseebenen. Mikrosakkaden scheinen ein Indikator der Fixationsaktivität in der Bewegungskarte des CS zu sein.