Cross‐Linguistic Differences in Processing Double‐Embedded Relative Clauses: Working‐Memory Constraints or Language Statistics?

Abstract

An English double‐embedded relative clause from which the middle verb is omitted can often be processed more easily than its grammatical counterpart, a phenomenon known as the grammaticality illusion. This effect has been found to be reversed in German, suggesting that the illusion is language specific rather than a consequence of universal working memory constraints. We present results from three self‐paced reading experiments which show that Dutch native speakers also do not show the grammaticality illusion in Dutch, whereas both German and Dutch native speakers do show the illusion when reading English sentences. These findings provide evidence against working memory constraints as an explanation for the observed effect in English. We propose an alternative account based on the statistical patterns of the languages involved. In support of this alternative, a single recurrent neural network model that is trained on both Dutch and English sentences is shown to predict the cross‐linguistic difference in the grammaticality effect.

Noisy-context surprisal as a human sentence processing cost model

“…In this model, which we call noisy-context surprisal, the processing cost of a word is the surprisal of the word given a noisy representation of the preceding context. We show that this model accounts for an outstanding puzzle in sentence comprehension, language-dependent structural forgetting effects (Gibson and Thomas, 1999;Vasishth et al, 2010;Frank et al, 2016), which are previously not well modeled by either expectation-based or memory-based approaches. Additionally, we show that this model derives and generalizes locality effects (Gibson, 1998;Demberg and Keller, 2008), a signature prediction of memory-based models.…”

“…The only previous formalized account of language-specific verb forgetting, Frank et al (2016), showed that Simple Recurrent Networks (SRNs) trained on English and Dutch data partly reproduce the verb forgetting effect in the surprisals they assign to the final verb. Our model provides an explanation of the SRN's behavior.…”

Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

“…We have used this approach to draw inferences from data in previously published work (e.g., Frank, Trompenaars, and Vasishth, 2015, Hofmeister and Vasishth, 2014, Safavi, Husain, and Vasishth, 2016. There are of course other approaches possible for carrying out inference.…”

The subject-relative advantage in Chinese: Evidence for expectation-based processing

“…Staub (2010) has shown that this prediction is correct. The surprisal metric introduced by Hale (2001) thus formalizes the well-established idea of reanalysis that dates back to the early days of psycholinguistics (Frazier, 1979). Surprisal assumes a ranked-parallel parser that ranks the available parses by their conditional probabilities.…”

“…It is particularly important to stress-test the expectation-based account given that its predictions sometimes go directly against another, well-established class of explanation, the memory-based accounts of sentence comprehension. Such models attribute processing difficulty to limitations of memory resources (Clifton & Frazier, 1989;Frazier, 1979;Frazier & Fodor, 1978;Gibson, 1998;Gibson, 2000;Just & Carpenter, 1992;Lewis & Vasishth, 2005;Lewis, Vasishth, & Van Dyke, 2006;Miller & Chomsky, 1963). A prominent example for this class of account is the Dependency Locality Theory (DLT) (Gibson, 1998;Gibson, 2000).…”