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Interpretation preferences in contexts with three antecedents: examining the role of prominence in German pronouns

Published online by Cambridge University Press:  31 August 2021

Clare Patterson*
Affiliation:
Department of German Language and Literature I, Linguistics, University of Cologne, Germany
Petra B. Schumacher
Affiliation:
Department of German Language and Literature I, Linguistics, University of Cologne, Germany
*
*Corresponding author. Email: cpatters@uni-koeln.de
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Abstract

This paper focuses on the relational notion of prominence, in which entities of equal type are ranked according to certain prominence-lending features. In German two demonstrative forms, “der” and “dieser”, can function like personal pronouns in English. It has been proposed that processing “der” involves computing a prominence hierarchy of the prior referents, and excluding the referent with the highest prominence rank. The demonstrative “dieser” has not been extensively tested. In the current study, personal and demonstrative pronominal forms were investigated following ditransitive contexts, where three potential antecedents are available, in two rating experiments. The personal pronoun showed flexibility in that it received equally high ratings for all three antecedents in canonical configurations. The ratings for dieser followed a graded sensitivity to thematic role prominence, with lowest scores when referring to prominent antecedents (agents) and the highest scores for the least prominent antecedents (patients), with scores for the medium prominence candidate (recipients) differing from both. Der followed a similar but not identical pattern, with a less marked difference between lower prominence candidates. Positional information also has a strong influence on demonstratives. In sum, final interpretation is sensitive to fine-grained differences in prominence hierarchies.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re- use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press
Figure 0

Table 1. Materials for Experiment 1a (nine conditions)

Figure 1

Table 2. Mean raw ratings and mean z-scores per Pronoun and Referent for Experiment 1a. Standard deviation (SD) are shown in parentheses

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Figure 1. Mean z-scores by condition set for Experiment 1a. Error bars show 95% confidence intervals, adjusted for within-subject designs as per Morey (2008).

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Table 3. Fixed-effect model outputs by condition set, treatment coded (simple effects) for Experiment 1a. Baseline = ER

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Table 4. Fixed-effect model output of demonstratives (der and dieser) across Referent levels Recipient and Patient, treatment coding for Experiment 1a. Baseline = DER and Recipient

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Table 5. Fixed-effect model output of demonstratives (DER and DIESER) across condition sets, forward-contrast coding for Experiment 1a. Baseline = DIESER

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Table 6. Materials for Experiment 1b (nine conditions)

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Figure 2. Mean z-scores by condition set for Experiment 1b. Error bars show 95% confidence intervals, adjusted for within-subject designs as per Morey (2008).

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Table 7. Sample materials in nine conditions for Experiment 2

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Table 8. Mean raw scores and mean z-scores per pronoun and antecedent for Experiment 2. Standard deviation (SD) are shown in parentheses

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Figure 3. Mean z-scores by condition set for Experiment 2. Error bars show 95% confidence intervals, adjusted for within-subject designs as per Morey (2008).

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Table 9. Fixed-effect model outputs by condition set (Agent, Patient, and Recipient), treatment coded (simple effects) for Experiment 2. Baseline = ER

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Table 10. Fixed-effect model output of demonstratives (DER and DIESER) across all antecedent levels, treatment coding for Experiment 2. Baselines = DER; AGENT

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Table 11. Fixed-effect model output of demonstratives (DER and DIESER) across condition sets with forward-contrast coding for Experiment 2. Baseline = DER