Monday, August 31, 2015

New paper: Locality and expectation in separable Persian complex predicates

Here's a new paper by Molood Sadat Safavi, Samar Husain, and Shravan Vasishth, which shows evidence from two self-paced reading studies in Persian against one of the key predictions of the expectation accounts (Hale 2001, Levy 2008).

Locality and expectation in Persian complex predicates

In sentence comprehension, it is well-known that processing cost increases with dependency distance (Gibson 2000, Lewis and Vasishth 2005); this often referred to as the locality effect. However, the expectation-based account (Hale 2001, Levy 2008) predicts that delaying the appearance of a verb renders it more predictable and therefore easier to process. Following up on previous work (Husain et al 2014), we investigated whether strengthening the expectation can increase facilitation at the verb even further. We operationalize strong expectation as prediction of the lexical entry for the verb; by contrast, weak expectation refers to the prediction of some upcoming verb phrase (these are the cases discussed by Levy 2008). We used Persian for this investigation. This language has a special construction called complex predicates, which are separable Noun-Verb configurations in which the verb (the precise lexical item) is highly predictable given the noun.  In two self-paced reading experiments, we delayed the appearance of the verb by interposing a relative clause (Expt 1, 42 subjects) or a long PP (Expt 2, 40 subjects). As a control, we included a simple predicate (Noun-Verb) configuration; the same distance manipulation was applied here as for complex predicates, but here, the exact lexical entry for the verb is not predicted but rather a verb phrase is predicted. Thus, we had a 2x 2 design, with Expectation Strength (Strong/Weak) and Distance (Short/Long). Based on the Husain et al  study, which had a similar design using Hindi complex predicates, we expected a slowdown in the weak expectation condition (i.e., locality effects), but a facilitation in the strong expectation conditions (i.e., expectation effects). Surprisingly, both experiments showed clear effects of locality in both the strong and weak expectation conditions. We also find evidence that could be consistent with expectation effects: the high-predictable verbs are read faster than the low-predictable verbs. However, this result is difficult to interpret because the verbs used in the strong and weak expectation conditions are different. In sum, these studies show strong and unequivocal evidence in favor of argument-verb dependency distance influencing integration processes at the verb, falsifying a key prediction of the expectation based account of Levy 2008.

Tuesday, August 25, 2015

New paper (Engelmann, Jäger, Vasishth)

Here is a new paper by Felix Engelmann and Lena Jäger and myself that people interested in sentence comprehension processes may be interested in.

The determinants of retrieval interference in dependency resolution: Review and computational modeling

We report a comprehensive literature review of retrieval interference in reflexive-antecedent dependencies, number agreement, and non-agreement subject-verb dependencies, and computationally evaluate the predictions of cue-based retrieval theory with reference to published results. A novel finding from the review and modeling is that, contrary to claims in the literature, results on number agreement are not entirely compatible with cue-based retrieval theory. We also show that the cue-based retrieval account in its current form cannot explain several reported interference effects, such as (i) speed-ups observed in presence of a syntactically unlicensed distractor when the correct dependent is a full match to the retrieval cues and (ii) slow-downs when the correct dependent only partially matches the retrieval cues.  We demonstrate that these effects can be explained by two theoretical and independently motivated constructs: distractor prominence and cue confusion. The cue-based retrieval model is therefore extended to incorporate distractor prominence and cue confusion, and quantitative predictions are derived from this extended model. We show that the extended cue-based retrieval model provides a better explanation of published results than the classical retrieval account.

The pdf is here: