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Jena, Universiẗat, Diss., 1999.

Der sozialwissenschaftliche Beitrag von Sandra Tausch (M. A.), Frühkindliche Differenzkategorisierung – eine explorative Analyse von Kinderkleidung, analysiert intersektional und in kritischer Abgrenzung zu Positionen und Intentionen des Gender-Marketings, die Interferenzen von ‚Class‘ und ‚Gender‘, indem er die Vergeschlechtlichung von Kinderbekleidung mit ihrem geschlechtersemantischen Impact und ihrer Normierungsmacht für Käufer*innen und Träger*innen untersucht. Ausgehend von der These, dass Kleidung und Mode generell als symbolische kulturelle Bedeutungsträger wesentliche Konstituenten persönlicher Identitäten und sozialer (Gruppen-) Zugehörigkeiten darstellen und diese kommunizieren, zeigt die explorative empirische Analyse der Verfasserin am Beispiel einer ausgewählten Produktgruppe (Oberteile) und ihrer farblichen und bildmotivischen Designs, welche (hetero-) stereotypen und asymmetrischen Vorstellungen von Weiblichkeit und Männlichkeit hier transportiert werden und frühkindliche Differenzkategorisierungen (re-) produzieren oder hinterfragen.

As mentioned in the introduction, the objective of this work has been to get a more realistic understanding of economic decision making processes by adopting an interdisciplinary approach which takes into consideration at the same time economic and psychological issues. The research in particular has been focused on the psychological concept of categorization, which in the standard economic theory has received until now no attention, and on its implications for decision making. The three experimental studies conducted in this work provide empirical evidence that individuals don not behave according to the perfect rationality and maximization assumptions which underly the SEUT, but rather as bounded rational satisfiers who try to simplify the decision problems they face through the process of categorization. The results of the first experimental study, on bilateral integrative negotiation, show that most of the people categorize a continuum of outcomes in two categories (satisfying/not satisfying), and treat all the options within each category as equivalent. This process of categorization leads the negotiators to make suboptimal agreements and to what I call the ?Zone of Agreement Bias? (ZAB). The experimental study on committees? decision making with logrolling provides evidence of how the categorization of outcomes in satisfying/not satisfying can affect the process of coalition formation in multi-issue decisions. In the first experiment, involving 3-issues and 3-parties decisions under majority rule, the categorization of outcomes leads most of the individuals to form suboptimal coalitions and make Pareto-dominated agreements. The second experiment, aimed at comparing the suboptimizing effect of categorization under majority and unanimity rule, shows that the unanimity rule can lead to a much higher rate of optimal agreements than the majority rule. The third experiment, involving 4-issues and 4-parties decisions provides evidence that the results of experiments 1 and 2 hold even when the level of complexity of the decision problem increases.

No description available.

The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible.

The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible.

As mentioned in the introduction, the objective of this work has been to get a more realistic understanding of economic decision making processes by adopting an interdisciplinary approach which takes into consideration at the same time economic and psychological issues. The research in particular has been focused on the psychological concept of categorization, which in the standard economic theory has received until now no attention, and on its implications for decision making. The three experimental studies conducted in this work provide empirical evidence that individuals don not behave according to the perfect rationality and maximization assumptions which underly the SEUT, but rather as bounded rational satisfiers who try to simplify the decision problems they face through the process of categorization. The results of the first experimental study, on bilateral integrative negotiation, show that most of the people categorize a continuum of outcomes in two categories (satisfying/not satisfying), and treat all the options within each category as equivalent. This process of categorization leads the negotiators to make suboptimal agreements and to what I call the ?Zone of Agreement Bias? (ZAB). The experimental study on committees? decision making with logrolling provides evidence of how the categorization of outcomes in satisfying/not satisfying can affect the process of coalition formation in multi-issue decisions. In the first experiment, involving 3-issues and 3-parties decisions under majority rule, the categorization of outcomes leads most of the individuals to form suboptimal coalitions and make Pareto-dominated agreements. The second experiment, aimed at comparing the suboptimizing effect of categorization under majority and unanimity rule, shows that the unanimity rule can lead to a much higher rate of optimal agreements than the majority rule. The third experiment, involving 4-issues and 4-parties decisions provides evidence that the results of experiments 1 and 2 hold even when the level of complexity of the decision problem increases.

Mit dieser Arbeit ist es erstmalig gelungen, funktionelle MRT und Dipol-Quellenlokalisation in einer Weise zu kombinieren, die es erlaubt, ein und dieselbe kortikale Aktivität simultan mit beiden Verfahren zu erfassen. Insbesondere wurde dies durch (a) Korrektur eines vom Tomographen induzierten Artefaks in den EKPs und (b) durch eine deutliche Verbesserung des experimentellen Designs, und damit einer wesentlich effektiveren Nutzung von EEG und fMRT-Messzeit erreicht. So wurde es dadurch möglich, mit beiden Methoden die kortikale Aktivität einer Einzelpulsstimulation noch aufzulösen. Eine wesentliche Voraussetzung für die simultane Kombination beider Verfahren: Aufgrund der sehr verschiedenen Latenzen von elektrophysiologischer (< 1ms) und vaskulärer (SII->ant. Inseln und medialeWand) in Übereinstimmung mit der Literatur (Forss et al., 1996; Mauguiere et al., 1997b) blieb. So ergab die Quellenlokalisation für die Wahlreaktionsaufgabe fünf Dipole innerhalb des Gehirns, welche mittels Koregistrierung den Aktivierungen des primären somatosensorschen Kortex (20 - 140ms), des sekundären somatosensorischen Kortex (50 - 150ms), der beiden anterioren Inseln (80 - 140ms) und des supplementär-motorischen Region (90 - 140ms, 220 - 270ms) aus der funktionellen MRT zugeordnet wurden. Durch einen Vergleich der Aktivierungsmuster von Wahl- und Einfachreaktionsaufgabe jeweils in der Dipol-Quellenanalyse und in der funktionellen MRT konnten weitere Belege dafür gefunden werden, daß, wie in der Literatur postuliert (Romo and Salinas, 2001), der kontralaterale sekundäre somatosensorische Kortex an der Kategorisierung somatosensorischer Stimulusattribute beteiligt ist. So ergab ein Vergleich der Dipolzeitverläufe für Wahl- und Einfachreaktionsaufgabe lediglich für den Dipol im kontralateral somatosensorischen Kortex im Intervall 57-62 ms nach Stimulusapplikation einen signifikant unterschiedlichen Aktivierungsverlauf (p < 0,001). Übereinstimmend zeigte die funktionelle MRT für die Wahlreaktionsaufgabe neben einer stärkeren Aktivierung der SMA eine hochsignifikant stärkere Aktivierung im Areal des kontralateralen sekundären somatosensorischen Kortex (p-cluster < 0,001). / In this study, we have shown that it is feasible to perform dipole source analysis and fMRI based on the same neuronal activity associated with somatosensory categorization. This was possible by reduction of scanner-induced baseline artifact interfering with the ERPs as well as an optimized experimental protocol for interleaved EEG and fMRI acquisition. We consider this study to be a further step toward imaging brain activity simultaneously at high spatial and temporal resolution. Since an event-related protocol with a single brief pulse stimulation paradigm was successfully employed, this approach seems to be suitable for the investigation of cognitive tasks. By further technical improvements also the exploration of brain activity in single subjects might become possible, opening the field of clinical applications. In particular for the characterization of irregular and nonreproducible events, a substantial contribution of combined EEG–fMRI studies toward a more detailed understanding of physiological processes underlying cerebral activations is expected.