On the persistence of Structural priming: mechanisms of decay and Influence of word-Forms

On the Persistence of Structural Priming: Mechanisms of Decay and Influence of Word-Forms Gaurav Malhotra Martin Pickering Holly Branigan James A. Bednar (gaurav.malhotra@ed.ac.uk) (martin.pickering@ed.ac.uk) (holly.branigan@ed.ac.uk) (james.bednar@ed.ac.uk) University of Edinburgh, Edinburgh, EH8 9JZ, UK Abstract in the system. Pickering and Branigan (1998) proposed that this trailing activation links structural options to individual lexical items, causing lexical context to influence structural priming (thereby explaining the lexical-boost effect). When people speak, they have to choose a syntactic structure for their utterances. Research shows that this choice is not made independently for each utterance, and that it is influenced by properties of recent utterances (Bock, 1986). But the learn- ing mechanisms responsible for this influence, and its duration are not completely clear. Pickering and Branigan (1998) sug- gest that structural repetition is due to trailing-activation in the language production system. However, this account lacks a formal implementation. Chang, Dell, and Bock (2006) have developed a computational model that tries to explain struc- tural priming based on error-based learning. In this paper we report a formal model that relies on an unsupervised learning mechanism and use this to replicate behavioral data for both structural priming and lexical influence on this priming. We use this model to investigate the factors that lead to these prim- ing effects and its rate of decay. The alternative error-based learning account is a super- vised learning account. It proposes that listeners actively pre- dict what they will hear and use the error between prediction and actual outcome to adjust their structural-decision rules; this error-based learning is hypothesized to underlie structural priming (Chang et al., 2006). Such an account therefore em- phasises that structural repetition is a byproduct of a larger function of human cognition: language acquisition. Both error-based and trailing-activation accounts are able to explain data from structural priming. Since the latter account is based on learning associations between lexical and structural constructs, it is more compatible with the data showing lexical boost. But the trailing-activation ac- count lacks a formal implementation. Also, some recent studies have found no lexical boost in long-term structural priming (Kaschak & Borreggine, 2008; Hartsuiker, Bernolet, Schoonbaert, Speybroeck, & Vanderelst, 2008) leading them to conclude that listeners store structural information in long- term memory after discarding its lexical context. These data are consistent with Chang et al’s model, which assumes that an independent syntax module abstracts structural informa- tion from utterances through error-based learning. Introduction A key goal of cognitive science is to understand the processes underlying language production. This entails understanding the mechanisms underlying lexical, structural and semantic choice during sentence formulation. Research shows that the choice of syntactic structure is not made independently for each episode of language production. Rather, recent utter- ances that the speaker has heard, or produced, influence this choice – i.e. language production shows structural priming (Bock, 1986; Branigan, Pickering, & Cleland, 2000). Furthermore, Pickering and Branigan (1998) found that structural priming increases when speakers use the same verbs in prime and target utterances. Consider the following Prime and Target pairs for a sentence-completion task: In contrast, we present a nonlinear dynamical system that explains such data in terms of trailing activation. This com- putational model does not rely on a strategic learning process. Instead, each episode of training leaves a memory trace based on the units that it activates, recorded as a fixed amount of ad- justment to the system. The structural choices made by the system during subsequent episodes are dependent on these memory traces. In addition to this, the architecture of the system assumes a considerable degree of similarity in lexical and structural information processing. As a consequence, it allows these kinds of processes to closely inform each other. Prime1 The teacher gave the girl the book. Prime2 The teacher gave the book to the girl. Prime3 The teacher showed the girl the book. Prime4 The teacher showed the book to the girl. Target The patient gave . The prime sentences use either a direct-object ( DO ) or prepositional-object ( PO ) syntactic structure. The target sen- tence can also be completed using either a DO or a PO phrase. Pickering and Branigan (1998) show that subjects are more likely to repeat the structure of prime sentence if the prime and target use the same verb – i.e. Prime1 and Prime2 elicit more priming than Prime3 and Prime4 . This phenomenon is called the lexical-boost effect. There are two existing accounts of structural priming. The trailing-activation account attributes priming to unsuper- vised, associative learning which leads to traces of activation We show that this system can successfully model short- term effects of both structural priming and its lexical- enhancement. We also use this system to model results from Kaschak and Borreggine (2008), and Hartsuiker et al. (2008) – two studies which fail to find lexical-enhancement on long- term structural priming. We use these results to investigate the time-course of structural priming and the mechanisms by which lexical representations can influence this priming.