An illusion induced by an illusion -perceptual filling-in of coloured negative afterimages

Visual filling-in relates to a perceptual phenomenon in which a stimulus pattern apparently undergoes dynamic changes assuming an attribute such as colour, texture, or brightness from the surround. This perceptual completion effect has up to now been shown only for real images. Here, we present filling-in in negative afterimages, a phenomenon not yet reported. Using coloured disk-ring patterns for stimuli, we demonstrate that afterimage filling-in arises independently, and is not simply a replica of filling-in observed in real images. Such filling-in does not occur when the afterimage is elicited dichoptically, suggesting its emergence within the monocular visual pathway. In this way, our findings indicate that filling-in under certain conditions may derive from an active neural mechanism located at low levels of the visual pathway

Eine Client/Server-Architektur für statistische Visualisierungen und Analyse von Finanzdaten

Statistische Visualisierungen bilden ein wichtiges Instrument der Daten-Analyse. In dieser Arbeit wird eine Client/Server-Architektur für die Ausgabe dieser vorgestellt. Die Architektur basiert auf drei Komponenten, wobei zwei Teile eine Grafikbibliothek für statistischen Visualisierungen enthalten. Es werden die Aufgaben jeder Komponente vorgestellt und deren Kommunikation miteinander erläutert. Der in dieser Arbeit vorgestellte Prototyp kann aufWebseiten, insbesondere im Bereich E-Learning, eingesetzt werden. Die Kommunikation erfolgt über die Middleware CORBA. Die Komponenten wurden in C++ und JAVA implementiert. Im zweiten Teil der Arbeit werden Modell zur Analyse von Finanzdaten basieren auf einer Studentschen Verteilung vorgestellt. Im Univariaten eine Studentsche Zeitreihe eingeführt. Im Multivariaten wird die Modellierung über Copulas vorgestellt. Es werden Copulas definiert, Schätzer und Simulationsalgorithmen angegeben. Besonders wird auf die Fragestellung: ”Welches ist die richtige Copula?“ eingegangen.Statistical visualizations form an important instrument of the data analysis. In this thesis a client/server-architecture for these visualizations is introduced. The architecture is based on the components, whereby two of them contain a graphic-library for statistical visualizations. The functions of each component are presented and their communication is described. The prototype introduced in this work can be used on web pages, in particular e-learning. The communication is made by the middleware CORBA. The components were implemented in C++ and JAVA. In the second part of the thesis models for analysis of finance data with student t-distributions, especially a timeseries, are introduced. In the multivariate case models depending on Copulas are presented. Copulas are defined, Estimators and simulationsalgorithms are given. Particularly the question ”Which copula is the right one?“is discussed

Detection vs. grouping thresholds for elements differing in spacing, size and luminance. An alternative approach towards the psychophysics of Gestalten

AbstractThree experiments were performed to compare thresholds for the detection of non-uniformity in spacing, size and luminance with thresholds for grouping. In the first experiment a row of 12 black equi-spaced dots was used and the spacing after the 3rd, 6th, and 9th dot increased in random steps to determine the threshold at which the observer detected an irregularity in the size of the gaps. Thereafter, spacing in the same locations was increased further to find the threshold at which the observer perceived four groups of three dots each (triplets). In the second experiment, empty circles were used instead of dots and the diameter of the circles in the first and second triplet increased until the difference in size gave rise either to a detection or grouping response. In the third experiment, the dots in the second and fourth triplet were increased in luminance. The aim again was to compare the difference in brightness required for detection or grouping, respectively. Results demonstrate that the threshold for perceiving stimuli as irregularly spaced or dissimilar in size or brightness is much smaller than the threshold for grouping. In order to perceive stimuli as grouped, stimulus differences had to be 5.2 times (for dot spacing), 7.4 times (for size) and 6.6 times (for luminance) larger than for detection. Two control experiments demonstrated that the difference between the two kinds of thresholds persisted even when only two gaps were used instead of three and when gap position was randomized