Date of Award

Spring 2014

Degree Type

Thesis

Degree Name

Master of Arts (MA)

Department

Languages and Cultures

First Advisor

John Sundquist

Second Advisor

Elena Bendicto

Committee Member 1

Mariko Wei

Committee Member 2

Daniel Olson

Abstract

In this thesis I explore the syntactic structure of emotive factive predicates in Spanish, English, Tatar, and Mayangna. I analyze emotive factive predicates of the glad type, such as (1). (1) I'm glad you liked the cake.

First, I analyze the basic structural configuration of these predicates, claiming that the clausal Source of Experience argument is a complement to the lexical head glad. This is similar to the configuration of canonical transitive attitude predicates like semi-factive know and intensional think. Second, I claim, following Kiparsky and Kiparsky (1970) and Krapova (2010), that factive heads select complements headed by a (null) D head, and that therefore clausal complements to emotive factive heads like glad and semi-factive heads like know are in fact DPs. Three types of evidence support this claim: 1) morphological data from Tatar and Mayangna; 2) distribution of DPs in all four languages; and 3) extraction facts from factive complements and definite/referential DPs in all four languages.

Finally, I offer a first approach at mapping the syntax of emotive factive predicates to the semantics. I propose that an intensional Operator is present in the derivation of emotive factive predicates which is absent in the derivation of semi-factive ones. I discuss the different selectional restrictions on DP complements between the two types of factive heads as well as the different flavors of presupposition that hold between emotive factive predicates and semi-factive ones. Morphological mood distribution from Spanish offers further insights into the syntactic differences between the two types of factive predicates.

This thesis adds to the body of research on factive predicates in the Minimalist tradition by offering a fresh take on an old analysis using data from understudied languages and well-studied languages alike.

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