Encoding and Retrieval Interference in Sentence Comprehension: Evidence from Agreement

Abstract

Long-distance verb-argument dependencies generally require the integration of a fronted argument when the verb is encountered for sentence interpretation. Under a parsing model that handles long-distance dependencies through a cue-based retrieval mechanism, retrieval is hampered when retrieval cues also resonate with non-target elements (retrieval interference). However, similarity-based interference may also stem from interference arising during the encoding of elements in memory (encoding interference), an effect that is not directly accountable for by a cue-based retrieval mechanism. Although encoding and retrieval interference are clearly distinct at the theoretical level, it is difficult to disentangle the two on empirical grounds, since encoding interference may also manifest at the retrieval region. We report two self-paced reading experiments aimed at teasing apart the role of each component in gender and number subject-verb agreement in Italian and English object relative clauses. In Italian, the verb does not agree in gender with the subject, thus providing no cue for retrieval. In English, although present tense verbs agree in number with the subject, past tense verbs do not, allowing us to test the role of number as a retrieval cue within the same language. Results from both experiments converge, showing similarity-based interference at encoding, and some evidence for an effect at retrieval. After having pointed out the non-negligible role of encoding in sentence comprehension, and noting that Lewis and Vasishth’s (2005) ACT-R model of sentence processing, the most fully developed cue-based retrieval approach to sentence processing does not predict encoding effects, we propose an augmentation of this model that predicts these effects. We then also propose a self-organizing sentence processing model (SOSP), which has the advantage of accounting for retrieval and encoding interference with a single mechanism.

Object attraction and the role of structural hierarchy: Evidence from Persian

“…that the correct verb be selected. Experimental evidence has shown that this procedure replicates feature mismatch as well as structural effects found with the sentence completion procedure (Franck et al 2015;Villata et al 2018;Villata & Franck 2019). In addition to accuracy, the task allows collecting response times.…”

The grammaticality asymmetry in agreement attraction reflects response bias: Experimental and modeling evidence

“…In both eye-tracking and self-paced reading, ungrammatical verbs are associated with longer reading times (and more regressive saccades) at the critical verb or the word following the verb (Deevy, 1999;Clifton, Frazier & Deevy, 1999;Pearlmutter et al, 1999). Agreement attraction effects appear as either faster reading times at ungrammatical verbs in the attractor mismatch condition compared to the match condition, or less commonly (due to the purported grammaticality asymmetry) as slower reading times at grammatical verbs in the mismatch condition compared to the match (Pearlmutter et al 1999;Wagers et al, 2009;Dillon, Mishler, Sloggett & Phillips, 2013;Lago et al 2015;Tucker, Idrissi & Almeida, 2015;Franck, Colllona, & Rizzi, 2015;Enochson & Culbertson, 2015;Patson & Husband, 2016;Parker & Phillips, 2017;Villata, Tabor, & Franck, 2018;Schlueter et al, 2018). These effects have been shown not only with PP and relative clause modifiers, where the attractor noun linearly intervenes between the subject and the critical verb, but also at the embedded verb of an object relative clause, where the attractor noun does not linearly intervene (e.g.…”

A principled approach to feature selection in models of sentence processing

“…However, the question arises: should there be ±rigid, ±breakable, and Additional evidence that lexically specific features are needed in sentence comprehension comes from garden-path effects, which can often be reduced in magnitude or eliminated when plausibility favors the correct parse during a temporary ambiguity (Trueswell et al, 1994;Trueswell et al, 1993). Similarly, encoding interference effects, where similarity-based interference arises due to features that are not relevant for retrieval (Gordon et al, 2001;Hofmeister & Vasishth, 2014;Smith et al, submitted;Villata et al, 2018) 2018, which uses distinct feature vectors for different classes of nouns; however, even this model does not go to the level of individual lexical items. This paper takes that step and presents a method for transparently generating lexical features and retrieval cues that can be used as a common baseline when comparing models of sentence processing.…”