Processing similarities between native speakers and non-balanced bilinguals

Aims and objectives: The study investigates human sentence processing and argues that information from multiple sources is considered equally in native and non-native languages. Non-syntactic information does not overrule the parsing decisions prompted by syntactic cues. Methodology: The experiment used ambiguous relative clauses (RC) in a self-paced reading task with 20 native and 45 non-native adult speakers of English and Russian. The software Linger recorded participants’ answers to comprehension questions and the time they spent reading each word. Data and analysis: Mixed linear analysis performed in R checked for the effect of a matrix verb, RC length, social conventions, the native language and the language of testing on RC processing and interpretation. Findings: Both native and non-native speakers followed social conventions in deciding on the interpretation of the RC. However, this information never overruled the attachment decision prompted by the matrix predicate or by the length of the RC which entails certain sentence prosody. Originality: The study is innovative in investigating the extent to which each factor affected RC processing. It shows that social conventions enhance processing when they conspire with the structural parse prompted by linguistic cues. When they do not, syntactic information governs sentence parsing in both L1 and L2. Significance/implications: The study provides evidence that sentence processing uses linguistic structure as a first parsing hypothesis, which can then be adjusted to incorporate the incoming information from multiple sources. Limitations: The findings need further support from testing L2 learners of Russian in various socio-cultural contexts.

Towards computational models of multilingual sentence processing

Although computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence-processing models can be used to study bi- and multilingualism. Recent results from cognitive modelling and computational linguistics suggest that phenomena specific to bilingualism can emerge from systems that have no dedicated components for handling multiple languages. Hence, accounting for human bi-/multilingualism may not require models that are much more sophisticated than those for the monolingual case.

Comparing gated and simple recurrent neural network architectures as models of human sentence processing

The Simple Recurrent Network (SRN) has a long tradition in cognitive models of language processing. More recently, gated recurrent networks have been proposed that often outperform the SRN on natural language processing tasks. Here, we investigate whether two types of gated networks perform better as cognitive models of sentence reading than SRNs, beyond their advantage as language models.This will reveal whether the filtering mechanism implemented in gated networks corresponds to an aspect of human sentence processing.We train a series of language models differing only in the cell types of their recurrent layers. We then compute word surprisal values for stimuli used in self-paced reading, eye-tracking, and electroencephalography experiments, and quantify the surprisal values' fit to experimental measures that indicate human sentence reading effort.While the gated networks provide better language models, they do not outperform their SRN counterpart as cognitive models when language model quality is equal across network types. Our results suggest that the different architectures are equally valid as models of human sentence processing.

A new argument for co-active parses during language comprehension

One perennially important question for theories of sentence comprehension is whether the human sentence processing mechanism is parallel (i.e. it simultaneously represents multiple syntactic analyses of linguistic input) or serial (i.e. it constructs only a single analysis at a time). Despite its centrality, this question has proven difficult to address for both theoretical and methodological reasons (Gibson & Pearlmutter, 2000; Lewis, 2000). In the present study, we reassess this question from a novel perspective. We investigated the well-known ambiguity advantage effect (Traxler, Pickering & Clifton, 1998) in a speeded acceptability judgment task. We adopted a Signal Detection Theoretic approach to these data, with the goal of determining whether speeded judgment responses were conditioned on one or multiple syntactic analyses. To link these results to incremental parsing models, we developed formal models to quantitatively evaluate how serial and parallel parsing models should impact perceived sentence acceptability in our task. Our results suggest that speeded acceptability judgments are jointly conditioned on multiple parses of the input, a finding that is overall more consistent with parallel parsing models than serial models. Our study thus provides a new, psychophysical argument for co-active parses during language comprehension.