Alhama, R. G., & Alishahi, A. (2025). Computational Models of Language Learning. In M. C. Frank, & A. Majid (Eds.), Open Encyclopedia of Cognitive Science MIT Press. https://oecs.mit.edu/pub/hexmhaj8/release/1
Brandes, T. N. H. R., Groot, J. J., & Alhama, R. G. (2025). CNNs Generalize Numerosity Across Naturalistic Stimuli Without Single-Unit Selectivity. In D. Barner, N. R. Bramley, A. Ruggeri, & C. M. Walker (Eds.), 47th Annual Meeting of the Cognitive Science Society (CogSci 2025) (pp. 4934-4940). (Proceedings of the Annual Meeting of the Cognitive Science Society; Vol. 47). Cognitive Science Society. https://escholarship.org/uc/item/8791m5qc
Klamra, C., Keur, F., & Alhama, R. G. (2025). Noise May Drown Out Words but Foster Compositionality: The Advantage of the Erasure and Deletion Noisy Channels on Emergent Communication. In K. Inui, S. Sakti, H. Wang, D. F. Wong, P. Bhattacharyya, B. Banerjee, A. Ekbal, T. Chakraborty, & D. P. Singh (Eds.), The 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: proceedings of the conference : IJCNLP-AACL 2025 : December 20-24, 2025 (Vol. 1, pp. 3141-3166). Association for Computational Linguistics. https://aclanthology.org/2025.ijcnlp-long.168/[details]
Pestel, J., Bloem, J., & Alhama, R. G. (2025). Evaluating Dutch Speakers and Large Language Models on Standard Dutch: a grammatical Challenge Set based on the Algemene Nederlandse Spraakkunst. Computational Linguistics in the Netherlands Journal, 14, 555-582. https://www.clinjournal.org/clinj/article/view/216[details]
Akkerman, D., Le, P., & Alhama, R. G. (2024). The Emergence of Compositional Languages in Multi-entity Referential Games: from Image to Graph Representations. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2024 : November 12-16, 2024 (pp. 18713-18723). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1042, https://doi.org/10.18653/v1/2024.emnlp-main.1042[details]
Alhama, R. G., Foushee, R., Byrne, D., Ettinger, A., Alishahi, A., & Goldin-Meadow, S. (2024). Using computational modeling to validate the onset of productive determiner-noun combinations in English-learning children. Proceedings of the National Academy of Sciences, 121(50), Article e2316527121. https://doi.org/10.1073/pnas.2316527121[details]
Delcaro, N., Onnis, L., & Alhama, R. G. (2024). Predict but Also Integrate: an Analysis of Sentence Processing Models for English and Hindi. In T. Kuribayashi, G. Rambelli, E. Takmaz, P. Wicke, & Y. Oseki (Eds.), The 13th edition of the Workshop on Cognitive Modeling and Computational Linguistics : proceedings of the workshop: CMCL 2024 : August 15, 2024 (pp. 101-108). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.cmcl-1.9[details]
Alhama, R. G., Foushee, R., Byrne, D., Ettinger, A., Goldin-Meadow, S., & Alishahi, A. (2023). Linguistic Productivity: the Case of Determiners in English. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Nusa Dua, Bali (Vol. 1, pp. 330-343). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.ijcnlp-main.21
Alhama, R. G., Rowland, C. F., & Kidd, E. (2023). How does linguistic context influence word learning? Journal of Child Language, 50(6), 1374-1393. https://doi.org/10.1017/S0305000923000302
Alhama, R. G. (2022). Word Segmentation as Unsupervised Constituency Parsing. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), The 60th Annual Meeting of the Association for Computational Linguistics: ACL 2022 : proceedings of the conference : May 22-27, 2022 (Vol. 1, pp. 4103-4112). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.283
Elazar, A., Alhama, R. G., Bogaerts, L., Siegelman, N., Baus, C., & Frost, R. (2022). When the “Tabula” is Anything but “Rasa:” What Determines Performance in the Auditory Statistical Learning Task? Cognitive Science, 46(2), Article e13102. https://doi.org/10.1111/cogs.13102
Vanmassenhove, E., De Sisto, M., Alhama, R. G., Lentz, T. O., Engelen, J., & Shterionov, D. (2022). Preface. Computational Linguistics in the Netherlands Journal, 12, 3-5. https://clinjournal.org/clinj/article/view/143
Alhama, R. G., Rowland, C., & Kidd, E. (2021). How Much Context is Helpful for Noun and Verb Acquisition? In T. C. Stewart (Ed.), Proceedings of ICCM 2021 - 19th International Conference on Cognitive Modelling (pp. 8-9). (Proceedings of ICCM 2021 - 19th International Conference on Cognitive Modelling). Applied Cognitive Science Lab, Penn State.
Alhama, R. G., Zermiani, F., & Khaliq, A. (2021). Retrodiction as Delayed Recurrence: the Case of Adjectives in Italian and English. In Proceedings of the 19th Workshop of the Australasian Language Technology Association: ALTA 2021 : 8-10 December, 2021, online (pp. 163-168). ALTA. https://alta2021.alta.asn.au/files/ALTA2021-proceedings-draft.pdf
2020
Alhama, R. G., Rowland, C., & Kidd, E. (2020). Evaluating Word Embeddings for Language Acquisition. In The Workshop on Cognitive Modeling and Computational Linguistics: proceedings of the workshop : CMCL 2020 : November 19, 2020, online event (pp. 38-42). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.cmcl-1.4
Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O'Donnell, T. J., Sainburg, T., & Gentner, T. Q. (2020). Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. Topics in Cognitive Science, 12(3), 925-941. https://doi.org/10.1111/tops.12474[details]
Alhama, R. G., & Zuidema, W. (2019). A review of computational models of basic rule learning: The neural-symbolic debate and beyond. Psychonomic Bulletin and Review, 26(4), 1174-1194. https://doi.org/10.3758/s13423-019-01602-z[details]
Alhama, R. G., Siegelman, N., Frost, R., & Armstrong, B. C. (2019). The Role of Information in Visual Word Recognition: A Perceptually-Constrained Connectionist Account. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Creativity + cognition + computation: 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) : Montreal, Canada, 24-27 July 2019 (Vol. 1, pp. 83-89). Cognitive Science Society. https://cognitivesciencesociety.org/cogsci-2019/[details]
Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules? Journal of Artificial Intelligence Research, 61, 927-946. https://doi.org/10.1613/jair.1.11197[details]
Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition: the R&R model. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), CogSci 2017: proceedings of the 39th Annual Meeting of the Cognitive Science Society : London, UK : 26-29 July 2017 : Computational Foundations of Cognition (Vol. 2, pp. 1531-1536). Cognitive Science Society. https://cognitivesciencesociety.org/wp-content/uploads/2019/01/cogsci17_proceedings.pdf[details]
Stanojević, M., & Alhama, R. G. (2017). Neural Discontinuous Constituency Parsing. In M. Palmer, R. Hwa, & S. Riedel (Eds.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1666-1676). Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1174[details]
Alhama, R. G., & Zuidema, W. (2016). Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules? In T. R. Besold, A. Bordes, A. d'Avila Garcez, & G. Wayne (Eds.), Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016: co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016) : Barcelona, Spain, December 9, 2016 Article 4 (CEUR Workshop Proceedings; Vol. 1773). CEUR-WS. http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper4.pdf[details]
Alhama, R. G., & Zuidema, W. (2016). Generalization in Artificial Language Learning: Modelling the Propensity to Generalize. In A. Korhonen, A. Lenci, B. Murphy, T. Poibeau, & A. Villavicencio (Eds.), The 54th Annual Meeting of the Association for Computational Linguistics: proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning: August 11, 2016, Berlin, Germany (pp. 64-72). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-19[details]
Alhama, R. G., Scha, R., & Zuidema, W. (2015). How should we evaluate models of segmentation in artificial language learning? In N. A. Taatgen, M. K. van Vugt, J. P. Borst, & K. Mehlhorn (Eds.), Proceedings of ICCM 2015 - 13th International Conference on Cognitive Modeling (pp. 172-173). (Proceedings of ICCM 2015 - 13th International Conference on Cognitive Modeling). University of Groningen Press.
2014
Alhama, R. G., Scha, R., & Zuidema, W. (2014). Rule Learning in Humans and Animals. In E. A. Cartmill, S. Roberts, H. Lyn, & H. Cornish (Eds.), The Evolution of Language: proceedings of the 10th International Conference (EVOLANG10), Vienna, Austria, 14-17 April 2014 (pp. 371-372). World Scientific. https://doi.org/10.1142/9789814603638_0049[details]
Le, P., Lindeman, M., & Alhama, R. G. (2025). On the Optimality of Discrete Object Naming: a Kinship Case Study. https://arxiv.org/abs/2511.19120
Nicenboim, B., van Vugt, M., Alhama, R. G., Anderson, B., Bontje, F., Chimento, M., Columbus, S., Dalmaijer, E., Dotlačil10, J., Østergaard, S. M., Thestrup Waade, P., van Maanen, L., K. Ward, E., Winkowski, J., & Fusaroli, R. (2025). It takes a village to model complex behaviour: A community-based approach.
2012
Martí, M. A., Alhama, R. G., & Recasens, M. (2012). Los avances tecnológicos y la ciencia del lenguaje. In T. Jiménez Juliá, B. López Meirama, V. Vázquez Rozas, & A. Veiga (Eds.), Cum corde et in nova grammatica: estudios ofrecidos a Guillermo Rojo (pp. 543-553). (Homenaxes). Universidade de Santiago de Compostela, Servizo de Publicacións e Intercambio Científico.
Garrido Alhama, R. & Zuidema, W. (2019). Best Article Award of Psychonomic Bulletin & Review (2019).
Spreker
Garrido Alhama, R. (speaker) (16-4-2024). Modeling Determiner Productivity: the case of Determiners, Experimental Methods in Language Acquisition Research (EMLAR), Utrecht.
2017
Garrido Alhama, R. (2017). Computational modelling of Artificial Language Learning: Retention, Recognition & Recurrence. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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