We discuss a data-driven analysis of EEG data recorded during a combined EEG/fMRI study of visual processing during a contour integration task. The analysis is based on an ensemble empirical mode decomposition (EEMD) and discusses characteristic features of event related modes (ERMs) resulting from the decomposition. We identify clear differences in certain ERMs in response to contour vs noncontour Gabor stimuli mainly for response amplitudes peaking around 100 [ms] (called P100) and 200 [ms] (called N200) after stimulus onset, respectively. We observe early P100 and N200 responses at electrodes located in the occipital area of the brain, while late P100 and N200 responses appear at electrodes located in frontal brain areas. Signals at electrodes in central brain areas show bimodal early/late response signatures in certain ERMs. Head topographies clearly localize statistically significant response differences to both stimulus conditions. Our findings provide an independent proof of recent models which suggest that contour integration depends on distributed network activity within the brain.

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User interfaces to information systems can be modelled by providing generalized descriptions of the contributions to the dialog from both partners: user and system. In this paper, we refer to such descriptions as "interaction models". Due to the probable integration of heterogeneous types of information in future information systems, we discuss an interaction model, which refers to a knowledge based model of document description (cf HAHN/REIMER 86). Using interactive graphics the model employs the feature "informational zooming" to investigate informational entities on an adequate level of abstraction.The knowledge-based full-text information system TOPIC/TOPOGRAPHIC integrates the presentation of various types of information (topical, factual and textual) into a comprehensive interaction model based on informational objects. Only three operators suffice for accessing the information structures at all levels. This is accomplished by context depending menus that are generated dynamically during the dialog if a further specification of the command is needed. Thus a user-friendly access to several layers of information about texts is possible:(1) Topical structures of relevant texts at different levels of generality (cascaded abstracts) (2) Facts from those texts automatically extracted during the text analysis (3) Passages from the original text which are presented according to the user's zooming operations.A survey of the functionality of the system is given in the appendix. l Interaction Models of Information SystemsUser interfaces to information systems can be modelled by providing generalized descriptions of the contributions to the dialog from both partners: user and system. In this paper, we will refer to such descriptions as "interaction models", which are determined by design decisions 1 This paper is an enhanced version of the paper presented at ACMSIGIR '87, published in: Yu, C.T. / Van Rijsbergen, C. J. (eds): Proceedings of the lOth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval. New York, 1987, pp. 45-56. This text is published under the following Creative Commons Licence: AttributionNonCommercial-NoDerivs 2.0 Germany (http://creativecommons.org/licenses/by-nc-nd/2.0/de/). 1 on the semantic (or "substantive", cf IIVARY 86) and on the syntactic level: Semantic restrictions result from the fact that the data stored in the system represent a model of a part of reality, especially descriptions of documents, which in most systems do not contain the same information as the documents themselves. Syntactic conventions are usually given as formal grammars (for command languages), or by abstract automata defining the possible state transitions during a dialog. In the sequel, we summarize some properties of common interaction models (for factual, bibliographic and full text data bases). Due to the probable integration of heterogeneous types of information in future information systems, we will then discuss an interaction model, which refers to a knowledg...

A few years ago Nagel and Schreckenberg introduced a model for traffic flow, 1 which, though quite simple, is able to reproduce the basic properties of traffic. Meanwhile many variations of the model have been developed, part of them concentrate on introducing an inhomogenous fleet of cars with different maximum velocities.Here we also want to study the effect of introducing a certain fraction of vehicles with a lower maximum velocity ("trucks") to the Nagel-Schreckenberg-model. We give a detailed description of this effect and focus on the time development of the influence of trucks on traffic flow.