Statistical methods for linguistic research: Foundational Ideas - Part II B Nicenboim, S Vasishth Language and Linguistics Compass 10 (11), 591--613, 2016 | 199* | 2016 |
Bayesian data analysis in the phonetic sciences: A tutorial introduction S Vasishth, B Nicenboim, ME Beckman, F Li, EJ Kong Journal of phonetics 71, 147-161, 2018 | 186 | 2018 |
Are words pre-activated probabilistically during sentence comprehension? Evidence from new data and a Bayesian random-effects meta-analysis using publicly available data B Nicenboim, S Vasishth, F Rösler Neuropsychologia 142, 107427, 2020 | 94 | 2020 |
Exploratory and confirmatory analyses in sentence processing: A case study of number interference in German B Nicenboim, S Vasishth, F Engelmann, K Suckow Cognitive science 42, 1075-1100, 2018 | 91 | 2018 |
Statistical Methods for Linguistic Research: Foundational Ideas – Part I S Vasishth, B Nicenboim Language and Linguistics Compass 10 (8), 349–369, 2016 | 86* | 2016 |
Workflow techniques for the robust use of bayes factors. DJ Schad, B Nicenboim, PC Bürkner, M Betancourt, S Vasishth Psychological Methods, 2022 | 81 | 2022 |
Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling B Nicenboim, S Vasishth Journal of Memory and Language 99, 1-34, 2018 | 80 | 2018 |
Working memory differences in long-distance dependency resolution B Nicenboim, S Vasishth, C Gattei, M Sigman, R Kliegl Frontiers in Psychology 6, 126597, 2015 | 80 | 2015 |
Computational models of retrieval processes in sentence processing S Vasishth, B Nicenboim, F Engelmann, F Burchert Trends in cognitive sciences 23 (11), 968-982, 2019 | 74 | 2019 |
When high-capacity readers slow down and low-capacity readers speed up: Working memory and locality effects B Nicenboim, P Logačev, C Gattei, S Vasishth Frontiers in psychology 7, 150577, 2016 | 58 | 2016 |
Using meta-analysis for evidence synthesis: The case of incomplete neutralization in German B Nicenboim, TB Roettger, S Vasishth Journal of Phonetics 70, 39-55, 2018 | 57 | 2018 |
An introduction to Bayesian data analysis for cognitive science B Nicenboim, D Schad, S Vasishth Under contract with Chapman and Hall/CRC statistics in the social and …, 2021 | 54 | 2021 |
A computational evaluation of two models of retrieval processes in sentence processing in aphasia P Lissón, D Pregla, B Nicenboim, D Paape, ML Van het Nederend, ... Cognitive Science 45 (4), e12956, 2021 | 27 | 2021 |
Sample size determination for Bayesian hierarchical models commonly used in psycholinguistics S Vasishth, H Yadav, DJ Schad, B Nicenboim Computational Brain & Behavior 6 (1), 102-126, 2023 | 17 | 2023 |
Feature overwriting as a finite mixture process: Evidence from comprehension data S Vasishth, LA Jäger, B Nicenboim arXiv preprint arXiv:1703.04081, 2017 | 17 | 2017 |
Understanding the effects of constraint and predictability in ERP K Stone, B Nicenboim, S Vasishth, F Rösler Neurobiology of Language 4 (2), 221-256, 2023 | 16 | 2023 |
Does antecedent complexity affect ellipsis processing?: An empirical investigation DLJF Paape, B Nicenboim, S Vasishth | 11 | 2017 |
Modeling sonority in terms of pitch intelligibility with the Nucleus Attraction Principle A Albert, B Nicenboim Cognitive Science 46 (7), e13161, 2022 | 9 | 2022 |
A bayesian approach to german personal and demonstrative pronouns C Patterson, PB Schumacher, B Nicenboim, J Hagen, A Kehler Frontiers in psychology 12, 672927, 2022 | 9 | 2022 |
Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses S Vasishth, N Chopin, R Ryder, B Nicenboim arXiv preprint arXiv:1702.00564, 2017 | 9 | 2017 |