@article{NicenboimVasishthGatteietal.2015,
  author    = {Nicenboim, Bruno and Vasishth, Shravan and Gattei, Carolina and Sigman, Mariano and Kliegl, Reinhold},
  title     = {Working memory differences in long-distance dependency resolution},
  series = {Frontiers in psychology},
  volume    = {6},
  journal   = {Frontiers in psychology},
  number    = {312},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2015.00312},
  pages     = {16},
  year      = {2015},
  abstract  = {There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.},
  language  = {en}
}
@misc{NicenboimVasishthGatteietal.2015,
  author    = {Nicenboim, Bruno and Vasishth, Shravan and Gattei, Carolina and Sigman, Mariano and Kliegl, Reinhold},
  title     = {Working memory differences in long-distance dependency resolution},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-75694},
  pages     = {16},
  year      = {2015},
  abstract  = {There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.},
  language  = {en}
}
@misc{StoneNicenboimVasishthetal.2022,
  author    = {Stone, Kate and Nicenboim, Bruno and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Understanding the effects of constraint and predictability in ERP},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  number    = {829},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-58759},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-587594},
  pages     = {71},
  year      = {2022},
  abstract  = {Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.},
  language  = {en}
}
@article{StoneNicenboimVasishthetal.2023,
  author    = {Stone, Kate and Nicenboim, Bruno and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Understanding the effects of constraint and predictability in ERP},
  series = {Neurobiology of language},
  volume    = {4},
  journal   = {Neurobiology of language},
  number    = {2},
  publisher = {MIT Press},
  address   = {Cambridge, MA, USA},
  issn      = {2641-4368},
  doi       = {10.1162/nol_a_00094},
  pages     = {221 -- 256},
  year      = {2023},
  abstract  = {Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.},
  language  = {en}
}
@article{SchadBetancourtVasishth2021,
  author    = {Schad, Daniel and Betancourt, Michael and Vasishth, Shravan},
  title     = {Toward a principled Bayesian workflow in cognitive science},
  series = {Psychological methods},
  volume    = {26},
  journal   = {Psychological methods},
  number    = {1},
  publisher = {American Psychological Association},
  address   = {Washington},
  issn      = {1082-989X},
  doi       = {10.1037/met0000275},
  pages     = {103 -- 126},
  year      = {2021},
  abstract  = {Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise. Even with powerful software, the analyst is responsible for verifying the utility of their model. To demonstrate this point, we introduce a principled Bayesian workflow (Betancourt, 2018) to cognitive science. Using a concrete working example, we describe basic questions one should ask about the model: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks. The running example for demonstrating the workflow is data on reading times with a linguistic manipulation of object versus subject relative clause sentences. This principled Bayesian workflow also demonstrates how to use domain knowledge to inform prior distributions. It provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model. Given the increasing use of Bayesian methods, we aim to discuss how these methods can be properly employed to obtain robust answers to scientific questions.},
  language  = {en}
}
@article{SchadVasishth2022,
  author    = {Schad, Daniel and Vasishth, Shravan},
  title     = {The posterior probability of a null hypothesis given a statistically significant result},
  series = {The quantitative methods for psychology},
  volume    = {18},
  journal   = {The quantitative methods for psychology},
  number    = {2},
  publisher = {University of Montreal, Department of Psychology},
  address   = {Montreal},
  issn      = {1913-4126},
  doi       = {10.20982/tqmp.18.2.p011},
  pages     = {130 -- 141},
  year      = {2022},
  abstract  = {When researchers carry out a null hypothesis significance test, it is tempting to assume that a statistically significant result lowers Prob(H0), the probability of the null hypothesis being true. Technically, such a statement is meaningless for various reasons: e.g., the null hypothesis does not have a probability associated with it. However, it is possible to relax certain assumptions to compute the posterior probability Prob(H0) under repeated sampling. We show in a step-by-step guide that the intuitively appealing belief, that Prob(H0) is low when significant results have been obtained under repeated sampling, is in general incorrect and depends greatly on: (a) the prior probability of the null being true; (b) type-I error rate, (c) type-II error rate, and (d) replication of a result. Through step-by-step simulations using open-source code in the R System of Statistical Computing, we show that uncertainty about the null hypothesis being true often remains high despite a significant result. To help the reader develop intuitions about this common misconception, we provide a Shiny app (https://danielschad.shinyapps.io/probnull/). We expect that this tutorial will help researchers better understand and judge results from null hypothesis significance tests.},
  language  = {en}
}
@article{BostonHaleKliegletal.2008,
  author    = {Boston, Marisa Ferrara and Hale, John and Kliegl, Reinhold and Vasishth, Shravan},
  title     = {Surprising parser actions and reading difficulty},
  year      = {2008},
  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}
}
@misc{BostonHaleKliegletal.2008,
  author    = {Boston, Marisa Ferrara and Hale, John and Kliegl, Reinhold and Patil, Umesh and Vasishth, Shravan},
  title     = {Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-57139},
  year      = {2008},
  abstract  = {The surprisal of a word on a probabilistic grammar constitutes a promising complexity metric for human sentence comprehension difficulty. Using two different grammar types, surprisal is shown to have an effect on fixation durations and regression probabilities in a sample of German readers' eye movements, the Potsdam Sentence Corpus. A linear mixed-effects model was used to quantify the effect of surprisal while taking into account unigram and bigram frequency, word length, and empirically-derived word predictability; the so-called "early" and "late" measures of processing difficulty both showed an effect of surprisal. Surprisal is also shown to have a small but statistically non-significant effect on empirically-derived predictability itself. This work thus demonstrates the importance of including parsing costs as a predictor of comprehension difficulty in models of reading, and suggests that a simple identification of syntactic parsing costs with early measures and late measures with durations of post-syntactic events may be difficult to uphold.},
  language  = {en}
}
@article{BostonHaleKliegletal.2008,
  author    = {Boston, Marisa Ferrara and Hale, John and Kliegl, Reinhold and Patil, Umesh and Vasishth, Shravan},
  title     = {Parsing costs as predictors of reading difficulty : an evaluation using the Potsdam Sentence Corpus},
  issn      = {1995-8692},
  year      = {2008},
  language  = {en}
}
@misc{BostonHaleVasishthetal.2011,
  author    = {Boston, Marisa Ferrara and Hale, John T. and Vasishth, Shravan and Kliegl, Reinhold},
  title     = {Parallel processing and sentence comprehension difficulty},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-57159},
  year      = {2011},
  abstract  = {Eye fixation durations during normal reading correlate with processing difficulty but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers' eyefixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated, and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehension.},
  language  = {en}
}
@article{BostonHalbeVasishthetal.2011,
  author    = {Boston, Marisa Ferrara and Halbe, John T. and Vasishth, Shravan and Kliegl, Reinhold},
  title     = {Parallel processing and entence comprehension difficulty},
  doi       = {10.1080/01690965.2010.492228},
  year      = {2011},
  abstract  = {Eye fixation durations during normal reading correlate with processing difficulty, but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers' eye fixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated; and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehension.},
  language  = {en}
}
@article{HusainVasishthSrinivasan2015,
  author    = {Husain, Samar and Vasishth, Shravan and Srinivasan, Narayanan},
  title     = {Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus},
  series = {Journal of Eye Movement Research},
  volume    = {8},
  journal   = {Journal of Eye Movement Research},
  number    = {2},
  publisher = {International Group for Eye Movement Research},
  address   = {Bern},
  issn      = {1995-8692},
  pages     = {12},
  year      = {2015},
  abstract  = {This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences. We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence comprehension difficulty, integration and storage cost, have an effect on reading difficulty. Integration cost (Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost (Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures. This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence reading difficulty.},
  language  = {en}
}
@article{SchadVasishthHohensteinetal.2020,
  author    = {Schad, Daniel and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold},
  title     = {How to capitalize on a priori contrasts in linear (mixed) models},
  series = {Journal of memory and language},
  volume    = {110},
  journal   = {Journal of memory and language},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0749-596X},
  doi       = {10.1016/j.jml.2019.104038},
  pages     = {40},
  year      = {2020},
  abstract  = {Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each other, a priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary 'omnibus' F test" (Hays, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, polynomial, custom, nested, interaction contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more useful confirmatory tests of experimental hypotheses than standard omnibus F-tests. Reproducible code is available from https://osf.io/7ukf6/.},
  language  = {en}
}
@article{StoneVasishthMalsburg2022,
  author    = {Stone, Kate and Vasishth, Shravan and Malsburg, Titus von der},
  title     = {Does entropy modulate the prediction of German long-distance verb particles?},
  series = {PLOS ONE},
  journal   = {PLOS ONE},
  publisher = {PLOS ONE},
  address   = {San Francisco, California, US},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0267813},
  pages     = {1 -- 25},
  year      = {2022},
  abstract  = {In this paper we examine the effect of uncertainty on readers' predictions about meaning. In particular, we were interested in how uncertainty might influence the likelihood of committing to a specific sentence meaning. We conducted two event-related potential (ERP) experiments using particle verbs such as turn down and manipulated uncertainty by constraining the context such that readers could be either highly certain about the identity of a distant verb particle, such as turn the bed […] down, or less certain due to competing particles, such as turn the music […] up/down. The study was conducted in German, where verb particles appear clause-finally and may be separated from the verb by a large amount of material. We hypothesised that this separation would encourage readers to predict the particle, and that high certainty would make prediction of a specific particle more likely than lower certainty. If a specific particle was predicted, this would reflect a strong commitment to sentence meaning that should incur a higher processing cost if the prediction is wrong. If a specific particle was less likely to be predicted, commitment should be weaker and the processing cost of a wrong prediction lower. If true, this could suggest that uncertainty discourages predictions via an unacceptable cost-benefit ratio. However, given the clear predictions made by the literature, it was surprisingly unclear whether the uncertainty manipulation affected the two ERP components studied, the N400 and the PNP. Bayes factor analyses showed that evidence for our a priori hypothesised effect sizes was inconclusive, although there was decisive evidence against a priori hypothesised effect sizes larger than 1μV for the N400 and larger than 3μV for the PNP. We attribute the inconclusive finding to the properties of verb-particle dependencies that differ from the verb-noun dependencies in which the N400 and PNP are often studied.},
  language  = {en}
}
@article{StoneVasishthvonderMalsburg2022,
  author    = {Stone, Kate and Vasishth, Shravan and von der Malsburg, Titus Raban},
  title     = {Does entropy modulate the prediction of German long-distance verb particles?},
  series = {PLOS ONE},
  volume    = {17},
  journal   = {PLOS ONE},
  number    = {8},
  publisher = {PLOS},
  address   = {San Francisco, California, US},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0267813},
  pages     = {25},
  year      = {2022},
  abstract  = {In this paper we examine the effect of uncertainty on readers' predictions about meaning. In particular, we were interested in how uncertainty might influence the likelihood of committing to a specific sentence meaning. We conducted two event-related potential (ERP) experiments using particle verbs such as turn down and manipulated uncertainty by constraining the context such that readers could be either highly certain about the identity of a distant verb particle, such as turn the bed [...] down, or less certain due to competing particles, such as turn the music [...] up/down. The study was conducted in German, where verb particles appear clause-finally and may be separated from the verb by a large amount of material. We hypothesised that this separation would encourage readers to predict the particle, and that high certainty would make prediction of a specific particle more likely than lower certainty. If a specific particle was predicted, this would reflect a strong commitment to sentence meaning that should incur a higher processing cost if the prediction is wrong. If a specific particle was less likely to be predicted, commitment should be weaker and the processing cost of a wrong prediction lower. If true, this could suggest that uncertainty discourages predictions via an unacceptable cost-benefit ratio. However, given the clear predictions made by the literature, it was surprisingly unclear whether the uncertainty manipulation affected the two ERP components studied, the N400 and the PNP. Bayes factor analyses showed that evidence for our a priori hypothesised effect sizes was inconclusive, although there was decisive evidence against a priori hypothesised effect sizes larger than 1 mu Vfor the N400 and larger than 3 mu V for the PNP. We attribute the inconclusive finding to the properties of verb-particle dependencies that differ from the verb-noun dependencies in which the N400 and PNP are often studied.},
  language  = {en}
}
@article{HofmeisterVasishth2014,
  author    = {Hofmeister, Philip and Vasishth, Shravan},
  title     = {Distinctiveness and encoding effects in online sentence comprehension},
  series = {Frontiers in psychology},
  volume    = {5},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2014.01237},
  pages     = {13},
  year      = {2014},
  abstract  = {In explicit memory recall and recognition tasks, elaboration and contextual isolation both facilitate memory performance. Here, we investigate these effects in the context of sentence processing: targets for retrieval during online sentence processing of English object relative clause constructions differ in the amount of elaboration associated with the target noun phrase, or the homogeneity of superficial features (text color). Experiment 1 shows that greater elaboration for targets during the encoding phase reduces reading times at retrieval sites, but elaboration of non-targets has considerably weaker effects. Experiment 2 illustrates that processing isolated superficial features of target noun phrases-here, a green word in a sentence with words colored white-does not lead to enhanced memory performance, despite triggering longer encoding times. These results are interpreted in the light of the memory models of Nairne, 1990, 2001, 2006, which state that encoding remnants contribute to the set of retrieval cues that provide the basis for similarity-based interference effects.},
  language  = {en}
}
@article{EngbertRabeSchwetlicketal.2022,
  author    = {Engbert, Ralf and Rabe, Maximilian Michael and Schwetlick, Lisa and Seelig, Stefan A. and Reich, Sebastian and Vasishth, Shravan},
  title     = {Data assimilation in dynamical cognitive science},
  series = {Trends in cognitive sciences},
  volume    = {26},
  journal   = {Trends in cognitive sciences},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1364-6613},
  doi       = {10.1016/j.tics.2021.11.006},
  pages     = {99 -- 102},
  year      = {2022},
  abstract  = {Dynamical models make specific assumptions about cognitive processes that generate human behavior. In data assimilation, these models are tested against timeordered data. Recent progress on Bayesian data assimilation demonstrates that this approach combines the strengths of statistical modeling of individual differences with the those of dynamical cognitive models.},
  language  = {en}
}
@article{MetznervonderMalsburgVasishthetal.2015,
  author    = {Metzner, Paul-Philipp and von der Malsburg, Titus Raban and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Brain Responses to World Knowledge Violations: A Comparison of Stimulus- and Fixation-triggered Event-related Potentials and Neural Oscillations},
  series = {Journal of cognitive neuroscience},
  volume    = {27},
  journal   = {Journal of cognitive neuroscience},
  number    = {5},
  publisher = {MIT Press},
  address   = {Cambridge},
  issn      = {0898-929X},
  doi       = {10.1162/jocn_a_00731},
  pages     = {1017 -- 1028},
  year      = {2015},
  abstract  = {Recent research has shown that brain potentials time-locked to fixations in natural reading can be similar to brain potentials recorded during rapid serial visual presentation (RSVP). We attempted two replications of Hagoort, Hald, Bastiaansen, and Petersson [Hagoort, P., Hald, L., Bastiaansen, M., \& Petersson, K. M. Integration of word meaning and world knowledge in language comprehension. Science, 304, 438-441, 2004] to determine whether this correspondence also holds for oscillatory brain responses. Hagoort et al. reported an N400 effect and synchronization in the theta and gamma range following world knowledge violations. Our first experiment (n = 32) used RSVP and replicated both the N400 effect in the ERPs and the power increase in the theta range in the time-frequency domain. In the second experiment (n = 49), participants read the same materials freely while their eye movements and their EEG were monitored. First fixation durations, gaze durations, and regression rates were increased, and the ERP showed an N400 effect. An analysis of time-frequency representations showed synchronization in the delta range (1-3 Hz) and desynchronization in the upper alpha range (11-13 Hz) but no theta or gamma effects. The results suggest that oscillatory EEG changes elicited by world knowledge violations are different in natural reading and RSVP. This may reflect differences in how representations are constructed and retrieved from memory in the two presentation modes.},
  language  = {en}
}
@article{LewisVasishth2005,
  author    = {Lewis, R. L. and Vasishth, Shravan},
  title     = {An activation-based model of sentence processing as skilled memory retrieval},
  issn      = {0364-0213},
  year      = {2005},
  abstract  = {We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought- Rational (ACT-R) architecture, and our process model is realized in ACT-R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model's complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT-R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity},
  language  = {en}
}
@article{EngelmannVasishthEngbertetal.2013,
  author    = {Engelmann, Felix and Vasishth, Shravan and Engbert, Ralf and Kliegl, Reinhold},
  title     = {A framework for modeling the interaction of syntactic processing and eye movement control},
  series = {Topics in cognitive science},
  volume    = {5},
  journal   = {Topics in cognitive science},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1756-8757},
  doi       = {10.1111/tops.12026},
  pages     = {452 -- 474},
  year      = {2013},
  abstract  = {We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, \& Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis \& Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and McConnell (2009), we integrated both measures with the eye movement model EMMA (Salvucci, 2001) inside the cognitive architecture ACT-R (Anderson et al., 2004). We built a reading model that could initiate short Time Out regressions (Mitchell, Shen, Green, \& Hodgson, 2008) that compensate for slow postlexical processing. This simple interaction enabled the model to predict the re-reading of words based on parsing difficulty. The model was evaluated in different configurations on the prediction of frequency effects on the Potsdam Sentence Corpus. The extension of EMMA with postlexical processing improved its predictions and reproduced re-reading rates and durations with a reasonable fit to the data. This demonstration, based on simple and independently motivated assumptions, serves as a foundational step toward a precise investigation of the interaction between high-level language processing and eye movement control.},
  language  = {en}
}
@article{RabeChandraKruegeletal.2021,
  author    = {Rabe, Maximilian Michael and Chandra, Johan and Kr{\"u}gel, Andr{\´e} and Seelig, Stefan A. and Vasishth, Shravan and Engbert, Ralf},
  title     = {A bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts},
  series = {Psychological Review},
  volume    = {128},
  journal   = {Psychological Review},
  number    = {5},
  publisher = {American Psychological Association},
  address   = {Washington},
  issn      = {0033-295X},
  doi       = {10.1037/rev0000268},
  pages     = {803 -- 823},
  year      = {2021},
  abstract  = {In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between- subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions.},
  language  = {en}
}