@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}
}
@article{MassonKliegl2013,
  author    = {Masson, Michael E. J. and Kliegl, Reinhold},
  title     = {Modulation of additive and interactive effects in lexical decision by Trial History},
  series = {Journal of experimental psychology : Learning, memory, and cognition},
  volume    = {39},
  journal   = {Journal of experimental psychology : Learning, memory, and cognition},
  number    = {3},
  publisher = {American Psychological Association},
  address   = {Washington},
  issn      = {0278-7393},
  doi       = {10.1037/a0029180},
  pages     = {898 -- 914},
  year      = {2013},
  abstract  = {Additive and interactive effects of word frequency, stimulus quality, and semantic priming have been used to test theoretical claims about the cognitive architecture of word-reading processes. Additive effects among these factors have been taken as evidence for discrete-stage models of word reading. We present evidence from linear mixed-model analyses applied to 2 lexical decision experiments indicating that apparent additive effects can be the product of aggregating over- and underadditive interaction effects that are modulated by recent trial history, particularly the lexical status and stimulus quality of the previous trial's target. Even a simple practice effect expressed as improved response speed across trials was powerfully modulated by the nature of the previous target item. These results suggest that additivity and interaction between factors may reflect trial-to-trial variation in stimulus representations and decision processes rather than fundamental differences in processing architecture.},
  language  = {en}
}