Hungarian 'focus position' is typically thought of as a central example of a 'discourse configurational' phenomenon, since it not only involves the expression of information-structural (or 'discourse semantic') meaning through the manipulation of word order but also interacts syntactically with other elements of the sentence. In this thesis, I argue that this kind of phenomenon highlights fundamental theoretical problems with conventional assumptions about the relationships between linguistic form and different kinds of meaning and demonstrate that these problems have led to empirical inadequacies in the syntactic analysis of Hungarian. I propose an alternative analysis that makes use of a dynamic, incremental parsing-based approach to grammar, which in turn allows for the influence of inferential pragmatic operations (investigated in terms of Relevance Theory) at all stages in the process of interpreting linguistic form. This opens up possibilities of structural and interpretive underspecification that allow for the interpretation of the 'focus position' to be unified with the information-structural interpretation of sentences that do not contain a syntactically focused expression. This analysis explains the interaction of syntactic foci with other pre-verbal items. The burden of explanation is thus shifted away from specialised, abstract syntactic representations and onto independently necessary aspects of cognitive organisation. The use of 'discourse semantic' primitives---whether in terms of focus or exhaustivity---to encode the effects of the 'focus position' is shown to be both theoretically problematic and empirically inadequate. The information-structural meanings associated with the position must be viewed not as the input to interpretive processes but instead as the result of inferential processes performed in context. Reanalysis of the syntactic evidence shows the relevant position to be not merely pre-verbal, but underlyingly pre-tense, showing that the unmarked position of the main verb is essentially the same as that of syntactically focused expressions. This leads to an analysis whereby both 'neutral', topic-comment readings and cases of narrow focus emerge from inferences over a common interpretive procedure. This procedure is identified as 'main predication': the point in the parsing of a sentence at which the application of a single predicate effects the conversion of a mere description of an event into a truth-conditional assertion. Main predication is represented using neo-Davidsonian, event-based semantics (the effect of the main predicate being equivalent to that of the application of an existential quantifier over an event variable in the neo-Davidsonian approach) and made dynamic by the use of the epsilon calculus. This analysis predicts the postposing of any (otherwise pre-tense) 'verbal modifier' (VM) in the presence of a syntactic focus and the apparent information-structural ambiguity of VMs when they are pre-tense. Certain constraints on the distribution of quantifiers are also predicted, one such constraint being adequately characterisable only within a semantically underspecified, procedural account. The behaviour of the negative particle "nem" is also given a maximally simple explanation. The apparently variable scope of the negative operator is explicable without ad hoc syntactic mechanisms: the apparent wide scope reading associated with 'sentential' negation follows inferentially from narrow scope negation of temporal information. The syntactic positions of negation are predictable on this basis. In addition, the assumption of consistent narrow scope negation correctly predicts that VMs must postpose or receive a narrow focus reading in the presence of "nem".

Graduate

Within linguistics, the dominant truth-conditional approach to semantics belongs to the Tarskian, model-theoretic tradition. Theories in this tradition offer an abstract, mathematical description of the truth conditions of natural language expressions in terms of their correspondence with the world. This thesis takes issue with existing modeltheoretic accounts of quantification on the basis that the specific abstract relations that they describe could not plausibly be models of natural language-to-world relations. Recent decades have seenmuch philosophical interest in naturalistic theories of reference and mental content. In one sense, these theories address the above concern by trying to identify something naturalistic for semantic correspondence to consist in, such as causalhistorical chains or ceteris paribus laws. In another sense, they fail to address the problem, since no account is given of either the semantic structure or the truth conditions of even the tiniest fragment of a natural language. Crucially, it is far from clear that modeltheoretic semantics, in anything like its present form, can accommodate the solutions proposed by naturalistic theories of content. If correspondence truth and naturalism are both to be retained, a new theory is needed. I begin by arguing that the class nominalism underlying model-theoretic semantics is unsuited to this naturalistic project, and propose that a variant of Armstrong’s realist metaphysic, incorporating Donald Baxter’s theory of aspects, provides the ideal ontology. I revise and extend Baxter’s theory for a more complete and precise account of the instantiation of properties and relations, and show that the theory of aspects allows for an appealing treatment of both numbers and general facts. Against the background of this realist metaphysic, and drawing on insights from naturalistic theories of mental content, I propose an original theory of mentally represented semantic structures and their truth-conditional analysis. Within this framework, I treat the core semantic phenomena of predication, negation, conjunction, and disjunction, and devote considerable attention to relations. I also develop a detailed theory of quantification, which includes a fully naturalistic account of both universal quantification and numerals.

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This study examines the use of the neural net paradigm as a modeling tool to represent word meanings. The neural net paradigm, also called "connectionism" and "parallel distributed processing," provides a new metaphor and vocabulary for representing the structure of the mental lexicon. As a research method applied to the componential analysis of word meanings, the neural net approach has one primary advantage over the traditional introspective method: freedom from the investigator's personal biases.The connectionist method is illustrated in this thesis with an extensive examination of the meanings of the words "cup" and "mug." These words have been studied previously by Labov (1973), Wierzbicka (1985), Andersen (1975), and Kempton (1978), using very different methods.The neural net models developed in this study are based on empirical data acquired through interviews with nine informants who classified 37 objects, 37 photographs, and 37 line drawings as "cups," "mugs," or "neither." These responses were combined with a data file representing the coded attributes of each object, to construct neural net models which reflect each informant's classification process.In the neural net models, the "cup" and "mug" features are interconnected with positive and negative weights that represent the association strengths of the features. When the connection weights are set so that they reflect the informants' responses, the neural net models can account for the extreme discrepancies in object-naming among informants, and the models can also account for the inconsistent classifications of each individual informant with respect to the mode of presentation (drawing, photograph, or actual object). Further, the neural net modelscan predict classifications for novel objects with an accuracy varying from 82% to 100%.By examining the connection weight patterns within the neural net model, it is possible to discover the "cup" and "mug" features which are most salient for each informant, and for the informants collectively. This analysis shows that each informant has acquired internal meanings for the words "cup" and "mug" which are unique to the individual, although there is considerable overlap with respect to the most salient features. / Department of English

Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. In this thesis I extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent upon a set of structural and temporal rules. The thesis aims at achieving two main goals. The first goal is to define a taxonomy of semantics that describe different causal events in the environment. Ten semantics are defined in this thesis and divided into two main groups; simple causal semantics and complex causal semantics. Simple causal semantics describe basic semantics, which form the building blocks for more complex information and include causal amplification, causal dampening, causal strength, and causal multiplicity. Complex causal semantics are built by enhancing or combining one or more simple semantics and include additive causality, contradictive causality, fully-mediated causality, partially-mediated causality, threshold causality, and bidirectional causality. The second goal of this thesis is to design simple visual representations to describe the causal information. Three representation types were designed during the course of this research; static-graph, static-sequence, and animation. Nine experiments were also conducted to test the effectiveness of these representations. The first five experiments compared the static-graph and the animated representations through Memory Recall and Intuitiveness Evaluations tests. Results of these experiments suggest that animations were ~8% more accurate and performed ~9% faster than the static-graph representations. The last four experiments compared an enhanced static representation, called static-sequence, to the animations to test if sequential animation of causal relations had any influence on the superior performance of the animations in the previous experiments. Results of these experiments suggest that there was no significant difference in the performance of the static-sequence representations when compared to the static-graph representations. The results also suggest that the animations performed more accurately than their static counterparts mainly due to their intuitiveness. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations.

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