TY - JOUR A1 - Vasishth, Shravan A1 - Nicenboim, Bruno A1 - Beckman, Mary E. A1 - Li, Fangfang A1 - Kong, Eun Jong T1 - Bayesian data analysis in the phonetic sciences BT - a tutorial introduction JF - Journal of phonetics N2 - This tutorial analyzes voice onset time (VOT) data from Dongbei (Northeastern) Mandarin Chinese and North American English to demonstrate how Bayesian linear mixed models can be fit using the programming language Stan via the R package brms. Through this case study, we demonstrate some of the advantages of the Bayesian framework: researchers can (i) flexibly define the underlying process that they believe to have generated the data; (ii) obtain direct information regarding the uncertainty about the parameter that relates the data to the theoretical question being studied; and (iii) incorporate prior knowledge into the analysis. Getting started with Bayesian modeling can be challenging, especially when one is trying to model one’s own (often unique) data. It is difficult to see how one can apply general principles described in textbooks to one’s own specific research problem. We address this barrier to using Bayesian methods by providing three detailed examples, with source code to allow easy reproducibility. The examples presented are intended to give the reader a flavor of the process of model-fitting; suggestions for further study are also provided. All data and code are available from: https://osf.io/g4zpv. KW - Bayesian data analysis KW - Linear mixed models KW - Voice onset time KW - Gender effects KW - Vowel duration Y1 - 2018 U6 - https://doi.org/10.1016/j.wocn.2018.07.008 SN - 0095-4470 VL - 71 SP - 147 EP - 161 PB - Elsevier CY - London ER - TY - JOUR A1 - Nicenboim, Bruno A1 - Roettger, Timo B. A1 - Vasishth, Shravan T1 - Using meta-analysis for evidence synthesis BT - the case of incomplete neutralization in German JF - Journal of phonetics N2 - Within quantitative phonetics, it is common practice to draw conclusions based on statistical significance alone Using incomplete neutralization of final devoicing in German as a case study, we illustrate the problems with this approach. If researchers find a significant acoustic difference between voiceless and devoiced obstruents, they conclude that neutralization is incomplete, and if they find no significant difference, they conclude that neutralization is complete. However, such strong claims regarding the existence or absence of an effect based on significant results alone can be misleading. Instead, the totality of available evidence should be brought to bear on the question. Towards this end, we synthesize the evidence from 14 studies on incomplete neutralization in German using a Bayesian random-effects meta-analysis. Our meta-analysis provides evidence in favor of incomplete neutralization. We conclude with some suggestions for improving the quality of future research on phonetic phenomena: ensure that sample sizes allow for high-precision estimates of the effect; avoid the temptation to deploy researcher degrees of freedom when analyzing data; focus on estimates of the parameter of interest and the uncertainty about that parameter; attempt to replicate effects found; and, whenever possible, make both the data and analysis available publicly. (c) 2018 Elsevier Ltd. All rights reserved. KW - Meta-analysis KW - Incomplete neutralization KW - Final devoicing KW - German KW - Bayesian data analysis Y1 - 2018 U6 - https://doi.org/10.1016/j.wocn.2018.06.001 SN - 0095-4470 VL - 70 SP - 39 EP - 55 PB - Elsevier CY - London ER - TY - JOUR A1 - Nicenboim, Bruno A1 - Vasishth, Shravan T1 - Models of retrieval in sentence comprehension BT - a computational evaluation using Bayesian hierarchical modeling JF - Journal of memory and language N2 - Research on similarity-based interference has provided extensive evidence that the formation of dependencies between non-adjacent words relies on a cue-based retrieval mechanism. There are two different models that can account for one of the main predictions of interference, i.e., a slowdown at a retrieval site, when several items share a feature associated with a retrieval cue: Lewis and Vasishth’s (2005) activation-based model and McElree’s (2000) direct-access model. Even though these two models have been used almost interchangeably, they are based on different assumptions and predict differences in the relationship between reading times and response accuracy. The activation-based model follows the assumptions of the ACT-R framework, and its retrieval process behaves as a lognormal race between accumulators of evidence with a single variance. Under this model, accuracy of the retrieval is determined by the winner of the race and retrieval time by its rate of accumulation. In contrast, the direct-access model assumes a model of memory where only the probability of retrieval can be affected, while the retrieval time is drawn from the same distribution; in this model, differences in latencies are a by-product of the possibility of backtracking and repairing incorrect retrievals. We implemented both models in a Bayesian hierarchical framework in order to evaluate them and compare them. The data show that correct retrievals take longer than incorrect ones, and this pattern is better fit under the direct-access model than under the activation-based model. This finding does not rule out the possibility that retrieval may be behaving as a race model with assumptions that follow less closely the ones from the ACT-R framework. By introducing a modification of the activation model, i.e., by assuming that the accumulation of evidence for retrieval of incorrect items is not only slower but noisier (i.e., different variances for the correct and incorrect items), the model can provide a fit as good as the one of the direct-access model. This first ever computational evaluation of alternative accounts of retrieval processes in sentence processing opens the way for a broader investigation of theories of dependency completion. KW - Cognitive modeling KW - Sentence processing KW - Working memory KW - Cue-based retrieval KW - Similarity-based interference KW - Bayesian hierarchical modeling Y1 - 2018 U6 - https://doi.org/10.1016/j.jml.2017.08.004 SN - 0749-596X SN - 1096-0821 VL - 99 SP - 1 EP - 34 PB - Elsevier CY - San Diego ER - TY - JOUR A1 - Nicenboim, Bruno A1 - Vasishth, Shravan A1 - Engelmann, Felix A1 - Suckow, Katja T1 - Exploratory and confirmatory analyses in sentence processing BT - a case study of number interference in German JF - Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society N2 - Given the replication crisis in cognitive science, it is important to consider what researchers need to do in order to report results that are reliable. We consider three changes in current practice that have the potential to deliver more realistic and robust claims. First, the planned experiment should be divided into two stages, an exploratory stage and a confirmatory stage. This clear separation allows the researcher to check whether any results found in the exploratory stage are robust. The second change is to carry out adequately powered studies. We show that this is imperative if we want to obtain realistic estimates of effects in psycholinguistics. The third change is to use Bayesian data-analytic methods rather than frequentist ones; the Bayesian framework allows us to focus on the best estimates we can obtain of the effect, rather than rejecting a strawman null. As a case study, we investigate number interference effects in German. Number feature interference is predicted by cue-based retrieval models of sentence processing (Van Dyke & Lewis, 2003; Vasishth & Lewis, 2006), but it has shown inconsistent results. We show that by implementing the three changes mentioned, suggestive evidence emerges that is consistent with the predicted number interference effects. KW - Exploratory and confirmatory analyses KW - Sentence processing KW - Bayesian hierarchical modeling KW - Cue-based retrieval KW - Working memory KW - Similarity-based interference KW - Number interference KW - German Y1 - 2018 U6 - https://doi.org/10.1111/cogs.12589 SN - 0364-0213 SN - 1551-6709 VL - 42 SP - 1075 EP - 1100 PB - Wiley CY - Hoboken ER -