@article{FrankTrompenaarsVasishth2016, author = {Frank, Stefan L. and Trompenaars, Thijs and Vasishth, Shravan}, title = {Cross-Linguistic Differences in Processing Double-Embedded Relative Clauses: Working-Memory Constraints or Language Statistics?}, series = {Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society}, volume = {40}, journal = {Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0364-0213}, doi = {10.1111/cogs.12247}, pages = {554 -- 578}, year = {2016}, 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.}, language = {en} } @article{VasishthNicenboim2016, author = {Vasishth, Shravan and Nicenboim, Bruno}, title = {Statistical Methods for Linguistic Research: Foundational Ideas - Part I}, series = {Language and linguistics compass}, volume = {10}, journal = {Language and linguistics compass}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1749-818X}, doi = {10.1111/lnc3.12201}, pages = {349 -- 369}, year = {2016}, abstract = {We present the fundamental ideas underlying statistical hypothesis testing using the frequentist framework. We start with a simple example that builds up the one-sample t-test from the beginning, explaining important concepts such as the sampling distribution of the sample mean, and the iid assumption. Then, we examine the meaning of the p-value in detail and discuss several important misconceptions about what a p-value does and does not tell us. This leads to a discussion of Type I, II error and power, and Type S and M error. An important conclusion from this discussion is that one should aim to carry out appropriately powered studies. Next, we discuss two common issues that we have encountered in psycholinguistics and linguistics: running experiments until significance is reached and the 'garden-of-forking-paths' problem discussed by Gelman and others. The best way to use frequentist methods is to run appropriately powered studies, check model assumptions, clearly separate exploratory data analysis from planned comparisons decided upon before the study was run, and always attempt to replicate results.}, language = {en} } @article{SorensenHohensteinVasishth2016, author = {Sorensen, Tanner and Hohenstein, Sven and Vasishth, Shravan}, title = {Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists}, series = {Tutorials in Quantitative Methods for Psychology}, volume = {12}, journal = {Tutorials in Quantitative Methods for Psychology}, publisher = {University of Montreal, Department of Psychology}, address = {Montreal}, issn = {2292-1354}, doi = {10.20982/tqmp.12.3.p175}, pages = {175 -- 200}, year = {2016}, abstract = {With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, using a two-condition repeated measures self-paced reading study.}, language = {en} } @misc{PatilVasishthLewis2016, author = {Patil, Umesh and Vasishth, Shravan and Lewis, Richard L.}, title = {Retrieval interference in syntactic processing}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {493}, issn = {1866-8364}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407987}, pages = {18}, year = {2016}, abstract = {It has been proposed that in online sentence comprehension the dependency between a reflexive pronoun such as himself/herself and its antecedent is resolved using exclusively syntactic constraints. Under this strictly syntactic search account, Principle A of the binding theory which requires that the antecedent c-command the reflexive within the same clause that the reflexive occurs in constrains the parser's search for an antecedent. The parser thus ignores candidate antecedents that might match agreement features of the reflexive (e.g., gender) but are ineligible as potential antecedents because they are in structurally illicit positions. An alternative possibility accords no special status to structural constraints: in addition to using Principle A, the parser also uses non-structural cues such as gender to access the antecedent. According to cue -based retrieval theories of memory (e.g., Lewis and Vasishth, 2005), the use of non-structural cues should result in increased retrieval times and occasional errors when candidates partially match the cues, even if the candidates are in structurally illicit positions. In this paper, we first show how the retrieval processes that underlie the reflexive binding are naturally realized in the Lewis and Vasishth (2005) model. We present the predictions of the model under the assumption that both structural and non-structural cues are used during retrieval, and provide a critical analysis of previous empirical studies that failed to find evidence for the use of non-structural cues, suggesting that these failures may be Type II errors. We use this analysis and the results of further modeling to motivate a new empirical design that we use in an eye tracking study. The results of this study confirm the key predictions of the model concerning the use of non-structural cues, and are inconsistent with the strictly syntactic search account. These results present a challenge for theories advocating the infallibility of the human parser in the case of reflexive resolution, and provide support for the inclusion of agreement features such as gender in the set of retrieval cues.}, language = {en} } @misc{PaapeVasishth2016, author = {Paape, Dario L. J. F. and Vasishth, Shravan}, title = {Local coherence and preemptive digging-in effects in German}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {417}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-405337}, pages = {17}, year = {2016}, abstract = {SOPARSE predicts so-called local coherence effects: locally plausible but globally impossible parses of substrings can exert a distracting influence during sentence processing. Additionally, it predicts digging-in effects: the longer the parser stays committed to a particular analysis, the harder it becomes to inhibit that analysis. We investigated the interaction of these two predictions using German sentences. Results from a self-paced reading study show that the processing difficulty caused by a local coherence can be reduced by first allowing the globally correct parse to become entrenched, which supports SOPARSE's assumptions.}, language = {en} } @article{NicenboimVasishth2016, author = {Nicenboim, Bruno and Vasishth, Shravan}, title = {Statistical methods for linguistic research: Foundational Ideas-Part II}, series = {Language and linguistics compass}, volume = {10}, journal = {Language and linguistics compass}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1749-818X}, doi = {10.1111/lnc3.12207}, pages = {591 -- 613}, year = {2016}, abstract = {We provide an introductory review of Bayesian data analytical methods, with a focus on applications for linguistics, psychology, psycholinguistics, and cognitive science. The empirically oriented researcher will benefit from making Bayesian methods part of their statistical toolkit due to the many advantages of this framework, among them easier interpretation of results relative to research hypotheses and flexible model specification. We present an informal introduction to the foundational ideas behind Bayesian data analysis, using, as an example, a linear mixed models analysis of data from a typical psycholinguistics experiment. We discuss hypothesis testing using the Bayes factor and model selection using cross-validation. We close with some examples illustrating the flexibility of model specification in the Bayesian framework. Suggestions for further reading are also provided.}, language = {en} } @article{PaapeVasishth2016, author = {Paape, Dario and Vasishth, Shravan}, title = {Local Coherence and Preemptive Digging-in Effects in German}, series = {Language and speech}, volume = {59}, journal = {Language and speech}, publisher = {Sage Publ.}, address = {London}, issn = {0023-8309}, doi = {10.1177/0023830915608410}, pages = {387 -- 403}, year = {2016}, abstract = {SOPARSE predicts so-called local coherence effects: locally plausible but globally impossible parses of substrings can exert a distracting influence during sentence processing. Additionally, it predicts digging-in effects: the longer the parser stays committed to a particular analysis, the harder it becomes to inhibit that analysis. We investigated the interaction of these two predictions using German sentences. Results from a self-paced reading study show that the processing difficulty caused by a local coherence can be reduced by first allowing the globally correct parse to become entrenched, which supports SOPARSE's assumptions.}, language = {en} } @article{SafaviHusainVasishth2016, author = {Safavi, Molood S. and Husain, Samar and Vasishth, Shravan}, title = {Dependency Resolution Difficulty Increases with Distance in Persian Separable Complex Predicates}, series = {Frontiers in psychology}, volume = {7}, journal = {Frontiers in psychology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2016.00403}, pages = {1 -- 15}, year = {2016}, abstract = {Delaying the appearance of a verb in a noun-verb dependency tends to increase processing difficulty at the verb; one explanation for this locality effect is decay and/or interference of the noun in working memory. Surprisal, an expectation-based account, predicts that delaying the appearance of a verb either renders it no more predictable or more predictable, leading respectively to a prediction of no effect of distance or a facilitation. Recently, Husain et al. (2014) suggested that when the exact identity of the upcoming verb is predictable (strong predictability), increasing argument-verb distance leads to facilitation effects, which is consistent with surprisal; but when the exact identity of the upcoming verb is not predictable (weak predictability), locality effects are seen. We investigated Husain et al.'s proposal using Persian complex predicates (CPs), which consist of a non-verbal element—a noun in the current study—and a verb. In CPs, once the noun has been read, the exact identity of the verb is highly predictable (strong predictability); this was confirmed using a sentence completion study. In two self-paced reading (SPR) and two eye-tracking (ET) experiments, we delayed the appearance of the verb by interposing a relative clause (Experiments 1 and 3) or a long PP (Experiments 2 and 4). We also included a simple Noun-Verb predicate configuration with the same distance manipulation; here, the exact identity of the verb was not predictable (weak predictability). Thus, the design crossed Predictability Strength and Distance. We found that, consistent with surprisal, the verb in the strong predictability conditions was read faster than in the weak predictability conditions. Furthermore, greater verb-argument distance led to slower reading times; strong predictability did not neutralize or attenuate the locality effects. As regards the effect of distance on dependency resolution difficulty, these four experiments present evidence in favor of working memory accounts of argument-verb dependency resolution, and against the surprisal-based expectation account of Levy (2008). However, another expectation-based measure, entropy, which was computed using the offline sentence completion data, predicts reading times in Experiment 1 but not in the other experiments. Because participants tend to produce more ungrammatical continuations in the long-distance condition in Experiment 1, we suggest that forgetting due to memory overload leads to greater entropy at the verb.}, language = {en} } @misc{NicenboimLogacevGatteietal.2016, author = {Nicenboim, Bruno and Logacev, Pavel and Gattei, Carolina and Vasishth, Shravan}, title = {When High-Capacity Readers Slow Down and Low-Capacity Readers Speed Up}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-90663}, pages = {1 -- 24}, year = {2016}, abstract = {We examined the effects of argument-head distance in SVO and SOV languages (Spanish and German), while taking into account readers' working memory capacity and controlling for expectation (Levy, 2008) and other factors. We predicted only locality effects, that is, a slowdown produced by increased dependency distance (Gibson, 2000; Lewis and Vasishth, 2005). Furthermore, we expected stronger locality effects for readers with low working memory capacity. Contrary to our predictions, low-capacity readers showed faster reading with increased distance, while high-capacity readers showed locality effects. We suggest that while the locality effects are compatible with memory-based explanations, the speedup of low-capacity readers can be explained by an increased probability of retrieval failure. We present a computational model based on ACT-R built under the previous assumptions, which is able to give a qualitative account for the present data and can be tested in future research. Our results suggest that in some cases, interpreting longer RTs as indexing increased processing difficulty and shorter RTs as facilitation may be too simplistic: The same increase in processing difficulty may lead to slowdowns in high-capacity readers and speedups in low-capacity ones. Ignoring individual level capacity differences when investigating locality effects may lead to misleading conclusions.}, language = {en} } @misc{SafaviHusainVasishth2016, author = {Safavi, Molood S. and Husain, Samar and Vasishth, Shravan}, title = {Dependency Resolution Difficulty Increases with Distance in Persian Separable Complex Predicates}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-90728}, pages = {1 -- 15}, year = {2016}, abstract = {Delaying the appearance of a verb in a noun-verb dependency tends to increase processing difficulty at the verb; one explanation for this locality effect is decay and/or interference of the noun in working memory. Surprisal, an expectation-based account, predicts that delaying the appearance of a verb either renders it no more predictable or more predictable, leading respectively to a prediction of no effect of distance or a facilitation. Recently, Husain et al. (2014) suggested that when the exact identity of the upcoming verb is predictable (strong predictability), increasing argument-verb distance leads to facilitation effects, which is consistent with surprisal; but when the exact identity of the upcoming verb is not predictable (weak predictability), locality effects are seen. We investigated Husain et al.'s proposal using Persian complex predicates (CPs), which consist of a non-verbal element—a noun in the current study—and a verb. In CPs, once the noun has been read, the exact identity of the verb is highly predictable (strong predictability); this was confirmed using a sentence completion study. In two self-paced reading (SPR) and two eye-tracking (ET) experiments, we delayed the appearance of the verb by interposing a relative clause (Experiments 1 and 3) or a long PP (Experiments 2 and 4). We also included a simple Noun-Verb predicate configuration with the same distance manipulation; here, the exact identity of the verb was not predictable (weak predictability). Thus, the design crossed Predictability Strength and Distance. We found that, consistent with surprisal, the verb in the strong predictability conditions was read faster than in the weak predictability conditions. Furthermore, greater verb-argument distance led to slower reading times; strong predictability did not neutralize or attenuate the locality effects. As regards the effect of distance on dependency resolution difficulty, these four experiments present evidence in favor of working memory accounts of argument-verb dependency resolution, and against the surprisal-based expectation account of Levy (2008). However, another expectation-based measure, entropy, which was computed using the offline sentence completion data, predicts reading times in Experiment 1 but not in the other experiments. Because participants tend to produce more ungrammatical continuations in the long-distance condition in Experiment 1, we suggest that forgetting due to memory overload leads to greater entropy at the verb.}, language = {en} }