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The interpretation and prediction of event participants in Mandarin verb-final active and passive sentences

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Abstract

The role of the markers bèi and bǎ for thematic role assignment in Chinese NP1-marker-NP2-V sentences was investigated in adult native speakers. While word order is identical, thematic roles are distributed reversely in these structures [patient-bèi-agent, (passive); agent--patient, (active)]. If Mandarin speakers interpret NP1 as the agent of an event, viewing behavior was expected to differ between conditions for NP1-objects, indicating the revision of initial role assignment in the case of bèi. Given reliability differences between markers for role assignment, differences in anticipatory eye movements to NP2-objects were expected. 16 visual stimuli were combined with 16 sets of sentence pairs; one pair partner featuring a bèi-, the other a bǎ-structure. Growth curve analysis of 28 participants’ eye movements revealed no attention differences for NP1-objects. However, anticipatory eye movements to NP2-objects differed. This suggests that a stable event representation is constructed only after NP1 and the marker have been processed, but before NP2. As a control variable, syntactic/semantic complexity of NP1 was manipulated. The differences obtained indicate that the visual world paradigm is in principle sensitive to detect language-induced processing costs, which was taken to validate the null-finding for NP1. Interestingly, NP1 complexity also modulated predictive processing. Findings are discussed with respect to a differentiation between interpretative and predictive aspects incremental processing.

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Data availability

The dataset analyzed during the current study is available in the heiDATA, V1 repository: https://doi.org/10.11588/data/L7QPUY.

Notes

  1. I will omit the discussion of the morphological/syntactic status of bèi and bǎ in the present paper, as this is irrelevant for our current purposes. Both markers, in the function they have today, historically evolved from verbs. However, in how far this fact contributes to online processing in Modern Chinese is not clear at all.

  2. Curiously, Huang et al. 2013) do mention the work by Philipp et al. (2008), but cite the results when discussing support for an agent-first strategy (p. 591).

  3. Elogits: log((possible number of fixations + 0.5)/(observed number of fixations − possible number of fixations +0.5))).

  4. In order to calculate p values, we used normal approximation: format.pval(2 × (1 − pnorm(abs(coefs$t.value))), digits = 1, eps = 0.0001).

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Correspondence to Johannes Gerwien.

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Appendices

Appendix 1

See Table 2.

Table 2 Stimulus material

Appendix 2

New looks’ analysis (looks to objects preceding markers)

See Tables  3, 4, 5 and 6.

Table 3 Model-selection by-subject aggregated data
Table 4 Full model output by-subject aggregated data
Table 5 Model-selection by-item aggregated data
Table 6 Full model output by-item aggregated data

Attention decay’ (new+old looks to objects preceding markers)

See Tables 7, 8, 9 and 10.

Table 7 Model-selection by-subject aggregated data
Table 8 Full model output by-subject aggregated data
Table 9 Model-selection by-item aggregated data
Table 10 Full model output by-item aggregated data

Prediction-ratios animate/inanimate referents

See Tables 11, 12, 13 and 14.

Table 11 Model-selection by-subject aggregated data
Table 12 Full model output by-subject aggregated data
Table 13 Model-selection by-item aggregated data
Table 14 Full model output by-item aggregated data

‘New looks’ analysis (looks to objects preceding markers) as a function of first phrase type

See Tables 15 and 16.

Table 15 Model-selection by-subject aggregated data
Table 16 Full model output by-subject aggregated data

‘Attention decay’ (new+old looks to objects preceding markers) as a function of first phrase type

See Tables 17 and 18.

Table 17 Model-selection by-subject aggregated data
Table 18 Full model output by-subject aggregated data

Prediction as a function of first phrase type

See Tables 19 and 20.

Table 19 Model-selection by-subject aggregated data
Table 20 Full model output by-subject aggregated data

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Gerwien, J. The interpretation and prediction of event participants in Mandarin verb-final active and passive sentences. J Cult Cogn Sci 3, 257–283 (2019). https://doi.org/10.1007/s41809-019-00049-x

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  • DOI: https://doi.org/10.1007/s41809-019-00049-x

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