Mixed-effects modeling with crossed random effects for subjects and items

By R. Harald Baayen, Douglas J. Davidson, Douglas M. Bates. 2008

8,393 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Developmental and Educational Psychology, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Can language models learn from explanations in context? (2022, methodology): “…estimate the effect of different components of the prompt on performance, we turn to methods of hierarchical/multilevel modeling (e.g. Gelman and Hill, 2006), which are commonly used to handle similar nested dependency patterns in the behavioral sciences (e.g. Baayen et al., 2008; Yarkoni, 2022).”
What do RNN Language Models Learn about Filler–Gap Dependencies? (2018, methodology): “Following standard practice in psycholinguis-tics, we derive the statistical signiﬁcance of the interaction from a mixed-effects linear regression model predicting surprisal given sum-coded conditions (Baayen et al., 2008).”
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies (2019, methodology): “We determine statistical significance using a mixed-effects linear regression model, using sum-coded conditions (Baayen et al., 2008).”
To transform or not to transform: using generalized linear mixed models to analyse reaction time data (2015, background): “LMMs provide a statistical solution to this problem (Baayen et al., 2008), and have become the recommended form of analysis in high impact journals within the field.”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “From a statistical perspective, this tension is also partially reflected in anxiety about the “stimulus-as-fixed-effect” fallacy in psycholinguistics (Coleman, 1964; Clark, 1973; Baayen et al., 2008), social psychology (Brunswik, 1955; Wells & Windschitl, 1999; Judd et al.”
Productivity and priming: Morphemic decomposition in Arabic (2011, positive): “In this second set of analyses, 6 using linear mixed-effect techniques, we fitted different models with log RT as the dependent variable and with participants, prime type, and target items as crossed random effects (Baayen, Davidson, & Bates, 2008 ; Baayen, Tweedie, & Schreuder, 2002 ; Bates, 20”
The simultaneous effects of inflectional paradigms and classes on lexical recognition: Evidence from Serbian (2009, methodology): “We performed a multilevel regression analysis on the log-transformed reaction times, using the linear mixed-effect model technique (Baayen et al., 2008; Bates, 2005, 2006).”
A test of the relation between working-memory capacity and syntactic island effects (2012, methodology): “All p-values were estimated using the MCMC method implemented in the languageR package for R (Baayen 2007, Baayen et al 2008).”
The Enhancing Versus Backfiring Effects of Positive Emotion in Consumer Reviews (2020, result): “…us to make full use of the data without averaging across responses and had the added benefit of providing greater confidence in the generalizability of the results to both readers and reviews not included in the current experiment (Baayen, Davidson, and Bates 2008; Judd, Westfall, and Kenny 2012).”
Development of boldness and docility in yellow-bellied marmots (2013, methodology): “We report full models after extracting parameter estimates and MCMC P values using the pvals.fnc function in the package language R (Baayen et al. 2008) based on a Markov-chain Monte Carlo sampling with 30 000 simulations.”
Neural language models as psycholinguistic subjects: Representations of syntactic state (2019, methodology): “Except where otherwise noted, all statistics are derived from linear mixed-effects models (Baayen et al., 2008) with sum-coded ﬁxed-effect predictors and maximal random slope structure (Barr et al., 2013).”
Predicting syntax: Processing dative constructions in American and Australian varieties of English (2010, methodology): “To attack this problem head-on, we make use of multilevel (also termed mixed effect) regression models, which are of growing importance in the language sciences (Baayen 2008, Johnson 2008, Baayen et al. 2008, Quené and van den Bergh 2008, Jaeger 2008).”