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Syntactic priming is known to facilitate comprehension of the target sentence if the syntactic structure of the target sentence aligns with the structure of the prime (Branigan et al., 2005; Tooley and Traxler, 2010). Such a processing facilitation is understood to be constrained due to factors such as lexical overlap between the prime and the target, frequency of the prime structure, etc. Syntactic priming in SOV languages is also understood to be influenced by similar constraints (Arai, 2012). Sentence comprehension in SOV languages is known to be incremental and predictive. Such a top-down parsing process involves establishing various syntactic relations based on the linguistic cues of a sentence and the role of preverbal case-markers in achieving this is known to be critical. Given the evidence of syntactic priming during comprehension in these languages, this aspect of the comprehension process and its effect on syntactic priming becomes important. In this work, we show that syntactic priming during comprehension is affected by the probability of using the prime structure while parsing the target sentence. If the prime structure has a low probability given the sentential cues (e.g., nominal case-markers) in the target sentence, then the chances of persisting with the prime structure in the target reduces. Our work demonstrates the role of structural complexity of the target with regard to syntactic priming during comprehension and highlights that syntactic priming is modulated by an overarching preference of the parser to avoid rare structures
Dependency Resolution Difficulty Increases with Distance in Persian Separable Complex Predicates
(2016)
Delaying the appearance of a verb in a noun-verb dependency tends to increase processing difficulty at the verb; one explanation for this locality effect is decay and/or interference of the noun in working memory. Surprisal, an expectation-based account, predicts that delaying the appearance of a verb either renders it no more predictable or more predictable, leading respectively to a prediction of no effect of distance or a facilitation. Recently, Husain et al. (2014) suggested that when the exact identity of the upcoming verb is predictable (strong predictability), increasing argument-verb distance leads to facilitation effects, which is consistent with surprisal; but when the exact identity of the upcoming verb is not predictable (weak predictability), locality effects are seen. We investigated Husain et al.'s proposal using Persian complex predicates (CPs), which consist of a non-verbal element—a noun in the current study—and a verb. In CPs, once the noun has been read, the exact identity of the verb is highly predictable (strong predictability); this was confirmed using a sentence completion study. In two self-paced reading (SPR) and two eye-tracking (ET) experiments, we delayed the appearance of the verb by interposing a relative clause (Experiments 1 and 3) or a long PP (Experiments 2 and 4). We also included a simple Noun-Verb predicate configuration with the same distance manipulation; here, the exact identity of the verb was not predictable (weak predictability). Thus, the design crossed Predictability Strength and Distance. We found that, consistent with surprisal, the verb in the strong predictability conditions was read faster than in the weak predictability conditions. Furthermore, greater verb-argument distance led to slower reading times; strong predictability did not neutralize or attenuate the locality effects. As regards the effect of distance on dependency resolution difficulty, these four experiments present evidence in favor of working memory accounts of argument-verb dependency resolution, and against the surprisal-based expectation account of Levy (2008). However, another expectation-based measure, entropy, which was computed using the offline sentence completion data, predicts reading times in Experiment 1 but not in the other experiments. Because participants tend to produce more ungrammatical continuations in the long-distance condition in Experiment 1, we suggest that forgetting due to memory overload leads to greater entropy at the verb.