Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study

Jäger, Lena A.; Mertzen, Daniela; Dyke, Julie A. Van; Vasishth, Shravan

doi:10.1016/j.jml.2019.104063

Cited by 106 publications

(164 citation statements)

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“…However, there is no evidence that the intercept and slope adjustments are correlated: the estimated correlation is -0.14, with 95% credible interval [-0.64,0.44]. In a larger-sample replication attempt of this experiment [85], which had 181 participants instead of the original sample size of 40, the estimated correlation is -0.24, 95% credible interval [-0.71, 0.38]. Of course, this absence of a clear correlation does not imply there is none; it just means that we could not find support for the correlation explanation for the varying magnitudes of the facilitatory interference effect.…”

Section: Modeling Individual Differencesmentioning

confidence: 83%

“…The participant-level estimates of the interference effect were computed from a hierarchical Bayesian model [86]. The average facilitatory interference effect is shown as a 95% credible interval (labeled Observed effect), and the range of predicted values from the activation model are also shown as a 95% credible interval (labeled Model prediction); the model predictions are computed using Approximate Bayesian Computation [85,87,88]. We see the predicted facilitatory interference effect even at the individual participant level, but different participants show varying magnitudes.…”

Section: Discussionmentioning

confidence: 99%

“…Despite these concerns, there is work suggesting that domain-general working memory capacity differences may play a role in determining individual-level comprehension difficulty [77,82]. However, this evidence for a capacity-based view only reports statistical associations between behavioral measures and working memory measures which may or may not replicate in confirmatory analyses, especially given the fact that many psycholinguistic studies tend to be underpowered [15,41,83,84,85].…”

Section: Modeling Individual Differencesmentioning

confidence: 99%

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Computational Models of Retrieval Processes in Sentence Processing

Vasishth

Nicenboim

Engelmann

et al. 2019

Trends in Cognitive Sciences

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Sentence comprehension requires that the comprehender work out who did what to whom. This process has been characterized as retrieval from memory. This review summarizes the quantitative predictions and empirical coverage of the two existing computational models of retrieval, and shows how the predictive performance of these two competing models can be tested against a benchmark data-set. We also show how computational modeling can help us better understand sources of variability in both unimpaired and impaired sentence comprehension.

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Section: Modeling Individual Differencesmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Modeling Individual Differencesmentioning

confidence: 99%

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Computational Models of Retrieval Processes in Sentence Processing

Vasishth

Nicenboim

Engelmann

et al. 2019

Trends in Cognitive Sciences

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“…Furthermore, although the N400 has been generally characterized as purely semantic, there is evidence that the mismatch in grammatical features (such as argument structure) can elicit an N400 effect (Chow et al, 2016); the N400 has also been found when syntactic reanalysis occurs (Bornkessel, McElree, Schlesewsky, & Friederici, 2004). A theoretical advantage of assuming that feature activation is not restricted to semantic features is that the extension of features allows for a single mechanism responsible for the access to memory for prediction generation and for the creation of dependencies with previous words in the sentence (as in Lewis & Vasishth, 2005;Engelmann, Jäger, & Vasishth, 2019;Jäger, Mertzen, Van Dyke, & Vasishth, 2019;Vasishth, Nicenboim, Engelmann, & Burchert, 2019, 11).…”

Section: Discussionmentioning

confidence: 99%

Are words pre-activated probabilistically during sentence comprehension? Evidence from new data and a Bayesian random-effects meta-analysis using publicly available data

Nicenboim¹,

Vasishth²,

Rösler³

2019

Preprint

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Several studies (e.g., DeLong et al., 2005) have shown that readers use information from the sentential context to predict nouns (or some of their features), and that predictability effects can be inferred from the EEG signal in determiners or adjectives appearing before the predicted noun. While these findings provide evidence for the pre-activation proposal, recent replication attempts together with inconsistencies in the results from the literature cast doubt on the robustness of this phenomenon. Our study presents the first attempt to use the effect of gender on predictability in German to study the pre-activation hypothesis, capitalizing on the fact that all German nouns have a gender and that their preceding determiners can show an unambiguous gender marking when the noun phrase has accusative case. Despite having a relatively large sample size (of 120 subjects), both our preregistered and exploratory analyses failed to yield conclusive evidence for or against an effect of pre-activation. The sign of the effect is, however, in the expected direction: the more unexpected the gender of the determiner, the larger the negativity. The recent, inconclusive replication attempts by Nieuwland et al. (2018) and others also show effects with signs in the expected direction. We conducted a Bayesian random-effects meta-analysis using our data and the publicly available data from these recent replication attempts. Our meta-analysis shows a relatively clear but very small effect that is consistent with the pre-activation account and demonstrates a very important advantage of the Bayesian data analysis methodology: we can incrementally accumulate evidence to obtain increasingly precise estimates of the effect of interest.

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“…The interference effect considered here is a semantic one; future work will determine the extent to which this method can be applied to purely morphosyntactic interference effects, e.g., in subject-verb number agreement and reflexive binding (Dillon et al, 2013;Jäger et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

A principled approach to feature selection in models of sentence processing

Smith¹,

Vasishth²

2019

Preprint

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Among theories of human language comprehension, cue-based memory retrieval has proven to be a useful framework for understanding when and how processing difficulty arises in the resolution of long-distance dependencies. Most previous work in this area has assumed that very general retrieval cues like [+subject] or [+singular] do the work of identifying (and sometimes misidentifying) a retrieval target in order to establish a dependency between words. However, recent work suggests that general, hand-picked retrieval cues like these may not be enough to explain illusions of plausibility (Cunnings & Sturt, 2018), which can arise in sentences like The letter next to the porcelain plate shattered. Capturing such retrieval interference effects requires lexically specific features and retrieval cues, but hand-picking the features is hard to do in a principled way and greatly increases modeler degrees of freedom. To remedy this, we use word embeddings, a well-established method for creating distributed feature representations, for lexical features and retrieval cues. We show that the similarity between the features and the cues (a measure of plausibility) predicts total reading times in Cunnings and Sturt’s eye-tracking data. The features can easily be plugged into existing parsing models (including cue-based retrieval and self-organized parsing), putting very different models on more equal footing and facilitating future quantitative comparisons. In addition to this methodological contribution, our results suggest that, contrary to Cunnings and Sturts’ original conclusions, focused words might be more prominent in memory, making them less susceptible to interference, as predicted by a recent extension to ACT-R (Engelmann, Jäger, & Vasishth, 2019).

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Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study

Cited by 106 publications

References 53 publications

Computational Models of Retrieval Processes in Sentence Processing

Computational Models of Retrieval Processes in Sentence Processing

Are words pre-activated probabilistically during sentence comprehension? Evidence from new data and a Bayesian random-effects meta-analysis using publicly available data

A principled approach to feature selection in models of sentence processing

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