Smith, G., & contributors. (2023a). BinaryHypervectors.jl (Version v0.1.1). https://doi.org/10.5281/zenodo.8192200
Smith, G., & contributors. (2023c). Uniformization.jl (Version v0.1.0). https://doi.org/10.5281/zenodo.7584378
Smith, G., & contributors. (2023b). FirstPassageTools.jl (Version v0.2.1). https://doi.org/10.5281/zenodo.6539033
Smith, G. (2021). Fptools: First-passage time tools for small- to medium-sized continuous-time, discrete-state systems. https://github.com/garrett-m-smith/fptools. https://doi.org/10.5281/zenodo.4964256
Paape, D., Smith, G., & Vasishth, S. (2023). Do local coherence effects exist in english reduced relative clauses? https://doi.org/10.31219/osf.io/wpke4
Smith, G., & Vasishth, S. (2021). A software toolkit for modeling human sentence parsing: An approach using continuous-time, discrete-state stochastic dynamical systems. https://psyarxiv.com/dtazq/
Tabor, W., Smith, G., & Dankowicz, H. (2024). Escape from fraught states in a coordination game. Royal Society Open Science, 11(231314). https://doi.org/10.1098/rsos.231314.
Yadav, H., Smith, G., Reich, S., & Vasishth, S. (2023). Number feature distortion modulates cue-based retrieval in reading. Journal of Memory and Language, 129(104400). https://doi.org/10.1016/j.jml.2022.104400. Data and code.
Yadav, H., Paape, D., Smith, G., Dillon, B. W., & Vasishth, S. (2022). Individual differences in cue weighting in sentence somprehension: An evaluation using approximate Bayesian computation. Open Mind, 6, 1–24. https://doi.org/10.1162/opmi_a_00052. Data and code.
Smith, G., Franck, J., & Tabor, W. (2021). Encoding interference effects support self-organized sentence processing. Cognitive Psychology, 124(101356). https://doi.org/10.1016/j.cogpsych.2020.101356. Data and code.
Smith, G., & Vasishth, S. (2020). A principled approach to feature selection in models of sentence processing. Cognitive Science, 44(12), e12918. https://doi.org/10.1111/cogs.12918. Data and code.
Fuhrmeister, P., Smith, G., & Myers, E. B. (2020). Overlearning of non-native speech sounds does not result in superior consolidation after a period of sleep. Journal of Acoustical Society of America Express Letters, 147(3), EL289–294. https://doi.org/10.1121/10.0000943. Data and code.
Smith, G., Franck, J., & Tabor, W. (2018). A self-organizing approach to subject-verb number agreement. Cognitive Science, 42(S4), 1043–1074. https://doi.org/10.1111/cogs.12591. Data and code.
Fang, S.-Y., Smith, G., & Tabor, W. (2017). The importance of situation-specific encodings: Analysis of a simple connectionist model of letter transposition effects. Connection Science, 30(2), 135–159. https://doi.org/10.1080/09540091.2016.1272097
Yadav, H., Smith, G., Mertzen, D., Engbert, R., & Vasishth, S. (2022). Individuals differ cross-linguistically in cue weighting: A computational evaluation of cue-based retrieval in sentence processing. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th annual meeting of the cognitive science society.
Yadav, H., Smith, G., & Vasishth, S. (2021b). Is similarity-based interference caused by lossy compression or cue-based retrieval? A computational evaluation. Proceedings of the 19th International Conference on Cognitive Modelling.
Yadav, H., Smith, G., & Vasishth, S. (2021a). Feature encoding modulates cue-based retrieval: Modeling interference effects in both grammatical and ungrammatical sentences. Proceedings of the Annual Meeting of the Cognitive Science Society, 43, 202–208.
Pankratz, E., Yadav, H., Smith, G., & Vasishth, S. (2021). Statistical properties of the speed-accuracy trade-off (SAT) paradigm in sentence processing. Proceedings of CogSci 2021, 2176–2182. https://cognitivesciencesociety.org/wp-content/uploads/2021/07/cogsci21_proceedings_v3.pdf#page=2254
Smith, G., & Tabor, W. (2018). Toward a theory of timing effects in self-organized sentence processing. In I. Juvina, J. Houpt, & C. Myers (Eds.), Proceedings of the 16th international conference on cognitive modeling (pp. 138–143). University of Wisconsin.