@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{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} } @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{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{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{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{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} } @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} }