Publications

Morgan, E. & Levy, R. (preprint) Generative knowledge and item-specific knowledge trade off as a function of frequency in multiword expression processing. https://psyarxiv.com/bduyv/

Jesse, K., Ahmed, T., Devanbu, P. & Morgan, E. (2023). Large Language Models and Simple, Stupid Bugs. To appear in: Proceedings of the 20th International Conference on Mining Software Repositories (MSR 2023). http://arxiv.org/abs/2303.11455

Chantavarin, S., Morgan, E. & Ferreira, F. (2022) Robust processing advantage for binomial phrases with variant conjunctions. Cognitive Science, 46(9). https://doi.org/10.1111/cogs.13187 [postprint]

Dodd, N. & Morgan, E. (2022). Expectations and Noisy-Channel Processing of Relative Clauses in Arabic. In: Proceedings of the 44th Annual Conference of the Cognitive Science Society, pp. 3391-3397. https://cognitivesciencesociety.org/wp-content/uploads/2022/07/CogSci2022Proceedings-1.pdf

Verosky, N. & Morgan, E. (2021). Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations. Cognitive Science, 45(10). https://doi.org/10.1111/cogs.13037 [postprint]

Fernandez Mira, P., Morgan, E., Davidson, S., Yamada, A., Carando, A., Sagae, K., & Sánchez-Gutiérrez (2021). Lexical Diversity in an L2 Spanish Learner Corpus: The Effect of Topic-Related Variables. International Journal of Learner Corpus Research, 7:2. https://doi.org/10.1075/ijlcr.7.2

Casalnuovo, C., Lee, K., Wang, H., Devanbu, P., & Morgan, E. (2020). Do Programmers Prefer Predictable Expressions in Code? Cognitive Science, 44. http://dx.doi.org/10.1111/cogs.12921 [postprint]

Gonnering , B. & Morgan, E. (2020). Uniform processing difficulty is a poor predictor of cross-linguistic word order frequency. In: Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL). https://www.aclweb.org/anthology/2020.conll-1.18.pdf

Casalnuovo, C., Devanbu, P., & Morgan, E. (2020). Does Language Model Surprisal Measure Code Comprehension?. In: Proceedings of the 42nd Annual Conference of the Cognitive Science Society, pp. 564-570. https://cogsci.mindmodeling.org/2020/papers/0102/0102.pdf

Liu, Z. & Morgan, E. (2020). Frequency-dependent Regularization in Constituent Ordering Preferences. In: Proceedings of the 42nd Annual Conference of the Cognitive Science Society, pp. 2990-2996. https://cogsci.mindmodeling.org/2020/papers/0750/0750.pdf

Casalnuovo, C., Barr, E., Dash, S., Devanbu, P., & Morgan, E. (2020). A Theory of Dual Channel Constraints (NIER track). In: 2020 42nd International Conference on Software Engineering (ICSE). https://dl.acm.org/doi/pdf/10.1145/3377816.3381720

Morgan, E., Fogel, A., Nair, A., & Patel, A. D. (2019). Statistical learning and Gestalt-like principles predict melodic expectations. Cognition, 189, 23–34. http://doi.org/10.1016/j.cognition.2018.12.015 [postprint]

Delaney-Busch, N., Morgan, E., Lau, E., & Kuperberg, G. R. (2019). Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming. Cognition, 187, 10–20. http://doi.org/10.1016/j.cognition.2019.01.001 [pdf]

Patel, A.D. and Morgan, E. (2017). Exploring cognitive relations between prediction in language and music. Cognitive Science, 41(Suppl. 2), 303-320. https://doi.org/10.1111/cogs.12411

Delaney-Busch, N., Morgan, E., Lau, E., & Kuperberg, G. (2017). Comprehenders Rationally Adapt Semantic Predictions to the Statistics of the Local Environment: a Bayesian Model of Trial-by-Trial N400 Amplitudes. In: Proceedings of the 39th Annual Conference of the Cognitive Science Society, pp. 283-288. [pdf]

Morgan, E. and Levy, R. (2016). Abstract knowledge versus direct experience in processing of binomial expressions. Cognition, 157, 382-402. http://dx.doi.org/10.1016/j.cognition.2016.09.011

Fogel, A.R., Morgan, E., & Patel, A.D. (2016). Measuring and modeling melodic expectation: A new approach. In: Proceedings of the 14th International Conference on Music Perception & Cognition (ICMPC10), July 2016, San Francisco. Adelaide: Causal Productions, pp. 87–90. [pdf]

Morgan E. and Levy R. (2016). Frequency-Dependent Regularization in Iterated Learning. In S.G. Roberts, C. Cuskley, L. McCrohon, L. Barceló-Coblijn, O. Fehér & T. Verhoef (eds.) The Evolution of Language: Proceedings of the 11th International Conference (EVOLANG11). [pdf]

Morgan, E. and Levy, R. (2015). Modeling idiosyncratic preferences: How generative knowledge and expression frequency jointly determine language structure. Proceedings of the 37th Annual Conference of the Cognitive Science Society, pages 1649-1654. [pdf]

Morgan, E., Keller, F., and Steedman, M. (2010). A Bottom-up Parsing Model of Local Coherence Effects. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society, pp. 1559- 1564.