Statistical Knowledge and Learning in Phonology

This thesis deals with the theory of the phonetic component of grammar in a formal probabilistic inference framework: (1) it has been recognized since the beginning of generative phonology that some language-specific phonetic implementation is actually context-dependent, and thus it can be said that there are gradient "phonetic processes" in grammar in addition to categorical "phonological processes." However, no explicit theory has been developed to characterize these processes. Meanwhile, (2) it is understood that language acquisition and perception are both really informed guesswork: the result of both types of inference can be reasonably thought to be a less-than-perfect committment, with multiple candidate grammars or parses considered and each associated with some degree of credence. Previous research has used probability theory to formalize these inferences in implemented computational models, especially in phonetics and phonology. In this role, computational models serve to demonstrate the existence of working learning/per- ception/parsing systems assuming a faithful implementation of one particular theory of human language, and are not intended to adjudicate whether that theory is correct. The current thesis (1) develops a theory of the phonetic component of grammar and how it relates to the greater phonological system and (2) uses a formal Bayesian treatment of learning to evaluate this theory of the phonological architecture and for making predictions about how the resulting grammars will be organized. The coarse description of the consequence for linguistic theory is that the processes we think of as "allophonic" are actually language-specific, gradient phonetic processes, assigned to the phonetic component of grammar; strict allophones have no representation in the output of the categorical phonological grammar

The Construction of Semantic Memory: Grammar-Based Representations Learned from Relational Episodic Information

After acquisition, memories underlie a process of consolidation, making them more resistant to interference and brain injury. Memory consolidation involves systems-level interactions, most importantly between the hippocampus and associated structures, which takes part in the initial encoding of memory, and the neocortex, which supports long-term storage. This dichotomy parallels the contrast between episodic memory (tied to the hippocampal formation), collecting an autobiographical stream of experiences, and semantic memory, a repertoire of facts and statistical regularities about the world, involving the neocortex at large. Experimental evidence points to a gradual transformation of memories, following encoding, from an episodic to a semantic character. This may require an exchange of information between different memory modules during inactive periods. We propose a theory for such interactions and for the formation of semantic memory, in which episodic memory is encoded as relational data. Semantic memory is modeled as a modified stochastic grammar, which learns to parse episodic configurations expressed as an association matrix. The grammar produces tree-like representations of episodes, describing the relationships between its main constituents at multiple levels of categorization, based on its current knowledge of world regularities. These regularities are learned by the grammar from episodic memory information, through an expectation-maximization procedure, analogous to the inside–outside algorithm for stochastic context-free grammars. We propose that a Monte-Carlo sampling version of this algorithm can be mapped on the dynamics of “sleep replay” of previously acquired information in the hippocampus and neocortex. We propose that the model can reproduce several properties of semantic memory such as decontextualization, top-down processing, and creation of schemata