Why is it so easy for us to produce complex natural language sentences? It is known that speakers seem to cheat a little when they come up with complex structures. They have a tendency to repeat, e.g., linguistic structures such as syntactic rules or lexicon entries rather than making new, independent decisions. When asked (using a passive voice construction): "Was Nero assassinated?", you will likely reply: "I don't think Nero was killed by anyone", but probably not "I don't think anyone killed Nero".
The phenomenon often called "priming" allows insights into the various structural units involved in natural language processing. Priming seems to be controlled by various factors, such as cognitive load and intended audience. In my thesis, I explore these factors and use them to build a model of linguistic choice-making. I present evidence for the hypothesis that priming is a result of two basic cognitive principles: learning and contextualization.
Structural priming (Bock 1986) is the tendency to repeat syntactic and other linguistic choices, rather than to make such choices from scratch. Priming is an effect of preceding context. I have developed a method to measure two kinds of structural priming effects from corpus data: a short-term effect, which decays within a few seconds, and a long term adapatation effect.
I show that priming levels differ under different circumstances, and that has grave consequences for theory of language production (and comprehension).
First, short-term priming is stronger in task-oriented dialogue than in spontaneous conversation, and secondly, stronger long-term priming is correlated with task success: pairs of speakers who are more successful at a task that requires communication, adapt more to each other's linguistic choices. Both of these findings are explained by Interactive Alignment Theory (Pickering and Garrod, 2004), whcih claims that interlocutors build a common situation model based on lower-level linguistic priming. Without the task, situation models and linguistic priming are not crucial to the dialogue.
The next step is to examine the role of syntax: what exactly is primed? Recent syntactic theories propose to encode the majority of language-specific information in the lexicon. For structural priming, this is an attractive model, in particular since lexical and syntactic priming have been shown to interact (e.g., Branigan and Pickering 1998). If priming is able to distinguish lexical (learned) categories from phrasal ones, we can support such models.
Syntactic frameworks make explicit assumptions about the structures used to process sentences. Linguists commonly evaluate grammar theory by its accuracy in predicting whether a sentence is acceptable (or grammatical). The psycholinguistic reality of grammar theories can now be tested using large collections of data (corpora) and methods such as structural priming, which can be assumed to directly apply to processing units, i.e. parts of syntactic structures predicted by a theory.
I evaluate priming with respect to phrase-structure grammar and to a lexicalized formalism, Combinatory Categorial Grammar (CCG, Steedman 2000). I find that priming can be modeled computationally as enhanced lexical access to syntactic categories, which specify subcategorization frames. This holds even if the processor is assumed to proceed incrementally, which is an important property when it comes to the psycholinguistic realism of such a formalism. Secondly, however, I find empirical evidence in corpora that transition-based models (on bigram levels) are insufficient to capture priming effects: priming is sensitive to syntactic structure, and not just to transitions between word categories.
In consequence, I spell out my view of structural priming in language production in a model within the general cognitive framework of ACT-R (e.g., Anderson, 1993). The model generates basic sentences ("The policeman gave a flower to the girl"), given a semantic description. It decides which syntactic structures to combine to come up with these sentences, using CCG as syntactic framework. Priming is modeled as a combination of spreading activation (contextualization), causing short-term priming, and base-level learning of syntactic nodes, causing long-term adaptation. The evaluation shows that priming in the ACT-R model mimics that found in corpora, also with respect to frequency effects (rare material primes more) and decay.
(December 2007) (With Frank Keller and Johanna Moore.)