Hostname: page-component-74656f9884-8r8nv Total loading time: 0 Render date: 2026-11-07T17:52:05.121Z Has data issue: false hasContentIssue false

The role of constituent order and level of embedding in cross-linguistic structural priming*

Published online by Cambridge University Press:  19 August 2016

GUNNAR JACOB
Affiliation:
Potsdam Research Institute for Multilingualism, University of Potsdam
KALLIOPI KATSIKA
Affiliation:
University of Kaiserslautern
NEILOUFAR FAMILY
Affiliation:
University of Kaiserslautern
SHANLEY E. M. ALLEN*
Affiliation:
University of Kaiserslautern
*
Address for correspondence: Prof. Dr. Shanley Allen, Department of Social Sciences, University of Kaiserslautern, Erwin-Schrödinger-Strasse 57/409, 67663 Kaiserslautern, Germanyallen@sowi.uni-kl.de
Rights & Permissions [Opens in a new window]

Abstract

In two cross-linguistic priming experiments with native German speakers of L2 English, we investigated the role of constituent order and level of embedding in cross-linguistic structural priming. In both experiments, significant priming effects emerged only if prime and target were similar with regard to constituent order and also situated on the same level of embedding. We discuss our results on the basis of two current theoretical accounts of cross-linguistic priming, and conclude that neither an account based on combinatorial nodes nor an account assuming that constituent order is directly responsible for the priming effect can fully explain our data pattern. We suggest an account that explains cross-linguistic priming through a hierarchical tree representation. This representation is computed during processing of the prime, and can influence the formulation of a target sentence only when the structural features specified in it are grammatically correct in the target sentence.

Figure 0

Table 1. Frequencies of completions by conditions in Experiment 1

Figure 1

Figure 1. Mean proportion of PO completions by condition in Experiment 1. Error bars represent 95% confidence intervals.

Figure 2

Table 2. Fixed and simple effects from the logit mixed effects model for Experiment 1.

Figure 3

Table 3. Frequencies of completions by conditions in Experiment 2

Figure 4

Figure 2. Mean proportions of PO completions by clause type and prime type in Experiment 2. Error bars represent 95% confidence intervals.

Figure 5

Table 4. Fixed and simple effects from the logit mixed effects model for Experiment 2.

Figure 6

Table 5. Fixed effects from the logit mixed effects model for the cross-experiment comparison (main clause to main clause priming vs. main clause to subordinate clause priming).

Figure 7

Figure 3. Hierarchical tree representation for a main clause PO prime and its target fragment.