Shaohua Fang 10/28/2021
My project will include data from a series of experiments where English native speakers and Chinese learners of English did a self-paced reading (SPR) task in which people controlled the pace of word/phrase presentation for sentences and answer a meaning-based question, and additionally did an acceptability judgement task (AJT) probing how well they understood individual sentences used for SPR. By now, the SPR data from 135 participants have been processed for data wrangling, visualization, and statistical modeling. As for AJT data, they are being processed. In addition to the experimental data, the project plans to include some data from the COCA corpus with the aim of setting up a baseline for how verbs (e.g., remember, accept) in individual experimental sentences are statistically biased towards the type of complement - NP vs Clause. So far, I have been exploring how to extract each instance containing these verbs and how to tag the kind of complement each verb takes.
Since the data aim for a journal publication, I therefore will publicly share 18% of the data (1000/5517) for the project in this class (Link: https://github.com/Data-Sci-2021/Structural-Change-L2-Sentence-Processing/tree/main/Data_processing/SPR), but the presentation will be based on the data in its entirety. Note that the data have been anonymized.
Native speakers (NSs) and non-native speakers (NNSs) have been found to experience garden-path effects when reading a temporarily structurally ambiguous sentence [1, 2]. The extent to which NNSs differ from NSs in their use of various types of linguistic information for structural reanalysis remains a topic of debate [3, 4]. Verb bias as a type of fine-grained lexical information has been demonstrated to influence reanalysis difficulty in both NSs and NNSs [5, 6]. When reading a sentence with a DO (direct object) - biased verb and a sentence with SC (sentential complement) - biased verb, both NSs and NNSs were slow to read the disambiguating regions compared to similar regions in the unambiguous control sentences [5, 7]. This suggests that NSs and NNSs are sensitive to verb bias information for ambiguity resolution.
The current investigation, however, addresses the influence from a verb pertaining to its structural properties rather than to its lexical properties on L2 ambiguity resolution. According to the structural change theory [8], reanalysis is particularly challenging when it involves a major rearrangement of thematic structure. For example, sentences like (1) involve NP/S ambiguities since they can take either a NP or a sentence (S) as the complement; sentences like (2) involve NP/Z ambiguities since they can take either a NP as the complement or no (Zero) complement. This theory predicts that NP/Z reanalysis should be more difficult than NP/S reanalysis because NP/Z reanalysis relative to NP/S reanalysis requires the NP following the verb to be moved out of its thematic domain. Sturt et al. [9] experimentally confirmed this prediction among NSs. This study aims to examine whether NNSs are sensitive to this structural property, thus directly testing this theory in NNSs.
References:
Dussias, P. E., & Scaltz, T. R. C. (2008). Spanish–English L2 speakers’ use of subcategorization bias information in the resolution of temporary ambiguity during second language reading. Acta psychologica, 128(3), 501-513.
Garnsey, S. M., Pearlmutter, N. J., Myers, E., & Lotocky, M. A. (1997). The contributions of verb bias and plausibility to the comprehension of temporarily ambiguous sentences. Journal of memory and language, 37(1), 58-93.
Hopp, H. (2015). Individual differences in the second language processing of object–subject ambiguities. Applied Psycholinguistics, 36(2), 129-173.
Jegerski, J. (2012). The processing of subject–object ambiguities in native and near-native Mexican Spanish. Bilingualism: Language and Cognition, 15(4), 721-735.
Juffs, A. (2004). Representation, processing and working memory in a second language. Transactions of the Philological Society, 102(2), 199-225.
Van Gompel, R. P., Pickering, M. J., & Traxler, M. J. (2001). Reanalysis in sentence processing: Evidence against current constraint-based and two-stage models. Journal of Memory and Language, 45(2), 225-258.
Qian, Z., Lee, E. K., Lu, D. H. Y., & Garnsey, S. M. (2019). Native and non-native (L1-Mandarin) speakers of English differ in online use of verb-based cues about sentence structure. Bilingualism: Language and Cognition, 22(5), 897-911.
Pritchett, B. L. (1992). Grammatical competence and parsing performance. University of Chicago Press.
Sturt, P., Pickering, M. J., & Crocker, M. W. (1999). Structural change and reanalysis difficulty in language comprehension. Journal of Memory and Language, 40(1), 136-150.
I have done the analysis for my experiments for AJT (Link: https://github.com/Data-Sci-2021/Structural-Change-L2-Sentence-Processing/tree/main/Data_Analysis/AJT/L1_L2_Data) and SPR (Link: https://github.com/Data-Sci-2021/Structural-Change-L2-Sentence-Processing/tree/main/Data_Analysis/SPR).
I am still working on the wrangling and analysis of the corpus data.
The most recent progress I have made is to start analyzing corpus data. The analysis is based on the corpus data from COCA.This is primarily to establish the statistical distribution for the kind of complement the verbs (which are manipulated for the main experiments) tend to take.