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Amsterdam Brain and Cognition (ABC) ABC

Dr. W.H. (Jelle) Zuidema

Faculty of Science
ILLC
Visiting address
  • Science Park 900
  • Room number: L6.54
Postal address
  • Postbus 94242
    1090 GE Amsterdam
Contact details
  • Personal website
    Personal website
  • Publications

    2024

    • van der Wal, O., Bachmann, D., Leidinger, A., van Maanen, L., Zuidema, W., & Schulz, K. (2024). Undesirable Biases in NLP: Addressing Challenges of Measurement. Journal of Artificial Intelligence Research, 79, 1-40. https://doi.org/10.1613/jair.1.15195 [details]

    2023

    • Chintam, A., Beloch, R., Zuidema, W., Hanna, M., & van der Wal, O. (2023). Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language Model. In Y. Belinkov, S. Hao, J. Jumelet, N. Kim, A. McCarthy, & H. Mohebbi (Eds.), BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: Proceedings of the Sixth Workshop : EMNLP 2023 : December 7, 2023 (pp. 379-394). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.blackboxnlp-1.29 [details]
    • Jumelet, J., & Zuidema, W. (2023). Feature Interactions Reveal Linguistic Structure in Language Models. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Findings of the Association for Computational Linguistics: ACL 2023: July 9-14, 2023 (pp. 8697–8712). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.554 [details]
    • Jumelet, J., & Zuidema, W. (2023). Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 4354–4369). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.288 [details]
    • Mohebbi, H., Chrupała, G., Zuidema, W., & Alishahi, A. (2023). Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers. In H. Bouamar, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 8249-8260). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.513 [details]
    • Mohebbi, H., Zuidema, W., Chrupała, G., & Alishahi, A. (2023). Quantifying Context Mixing in Transformers. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2023 : proceedings of the conference : May 2-6, 2023 (pp. 3378-3400). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.245 [details]
    • Vélez Vásquez, M. A., Baelemans, M., Driedger, J., Zuidema, W., & Burgoyne, J. A. (2023). Quantifying the ease of playing song chords on the guitar. In A. Sarti, F. Antonacci, M. Sandler, P. Bestagini, S. Dixon, B. Liang, G. Richard, & J. Pauwels (Eds.), Proceedings of the 24th International Society for Music Information Retrieval Conference: Milan, Italy, November 5-9, 2023 (pp. 725-732). ISMIR. https://doi.org/10.5281/zenodo.10265391 [details]

    2022

    • Sinclair, A., Jumelet, J., Zuidema, W., & Fernández, R. (2022). Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations. Transactions of the Association of Computational Linguistics, 10, 1031–1050. Advance online publication. https://doi.org/10.1162/tacl_a_00504 [details]

    2021

    2020

    • Abnar, S., & Zuidema, W. (2020). Quantifying Attention Flow in Transformers. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), The 58th Annual Meeting of the Association for Computational Linguistics: ACL 2020 : Proceedings of the Conference : July 5-10, 2020 (pp. 4190-4197). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.385 [details]
    • Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2020). Mode Classification and Natural Units in Plainchant. In J. Cuming, J. H. Lee, B. McFee, M. Schedl, J. Devaney, C. McKay, E. Zangerle, & T. de Reuse (Eds.), Proceedings of the 21st International Society for Music Information Retrieval Conference: ISMIR MTL2020, Montréal, Québec, Canada, Virtual Conference, 11 to 16 October 2020 (pp. 869-875). ISMIR. https://doi.org/10.5281/zenodo.4245572 [details]
    • Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2020). Studying large plainchant corpora using chant21. In Proceedings of DLfM 2020: the 7th International Conference on Digital Libraries for Musicology : 16th October 2020, McGill University, Montréal, QC, Canada (pp. 40-44). (ACM international conference proceedings series). The Association for Computing Machinery. https://doi.org/10.1145/3424911.3425514 [details]
    • Dekker, P., & Zuidema, W. (2020). Word prediction in computational historical linguistics. Journal of Language Modelling, 8(2), 295-336. https://doi.org/10.15398/JLM.V8I2.268 [details]
    • Le, P., & Zuidema, W. (2020). DoLFIn: Distributions over Latent Features for Interpretability. In D. Scott, N. Bel, & C. Zong (Eds.), The 28th International Conference on Computational Linguistics: COLING 2020 : Proceedings of the Conference : December 8-13, 2020, Barcelona, Spain (Online) (pp. 1468-1474). International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.127 [details]
    • Uddén, J., Dias Martins, M. J., Zuidema, W., & Fitch, W. T. (2020). Hierarchical Structure in Sequence Processing: How to Measure It and Determine Its Neural Implementation. Topics in Cognitive Science, 12(3), 910-924. https://doi.org/10.1111/tops.12442 [details]
    • 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]
    • ten Cate, C., Gervain, J., Levelt, C. C., Petkov, C. I., & Zuidema, W. (2020). Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross-Species, and Computational Approaches. Topics in Cognitive Science, 12(3), 804-814. https://doi.org/10.1111/tops.12493 [details]

    2019

    • Abnar, S., Beinborn, L., Choenni, R., & Zuidema, W. (2019). Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains. In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: ACL 2019 : proceedings of the Second Workshop : August 1, 2019, Florence, Italy (pp. 191-203). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4820 [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]
    • Jumelet, J., Zuidema, W., & Hupkes, D. (2019). Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. In M. Bansal, & A. Villavicencio (Eds.), The 23rd Conference on Computational Natural Language Learning: CoNLL 2019 : proceedings of the conference : November 3-4, 2019, Hong Kong, China (pp. 1-11). The Association for Computational Linguistics. https://doi.org/10.18653/v1/K19-1001 [details]

    2018

    • Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. (2018). Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity. In Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018): January 7, 2018 (pp. 57-66). Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-0107 [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]
    • Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. In T. Linzen, G. Chrupała, & A. Alishahi (Eds.), The 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: EMNLP 2018 : proceedings of the First Workshop : November 1, 2018, Brussels, Belgium (pp. 240–248). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-5426 [details]
    • Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907-926. https://doi.org/10.1613/jair.1.11196 [details]
    • Merker, B., Morley, I., & Zuidema, W. (2018). Five fundamental constraints on theories of the origins of music. In H. Honing (Ed.), The Origins of Musicality (pp. 49-80). MIT Press. http://cognet.mit.edu/pdfviewer/book/9780262344548/chap3 [details]
    • Zuidema, W. H., & Veldhoen, S. F. (2018). Can Neural Networks learn Logical Reasoning? In Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017) Centre for Linguistic Theory and Studies in Probability (CLASP). https://gupea.ub.gu.se/handle/2077/54911
    • Zuidema, W., & de Boer, B. (2018). The evolution of combinatorial structure in language. Current Opinion in Behavioral Sciences, 21, 138-144. https://doi.org/10.1016/j.cobeha.2018.04.011 [details]
    • Zuidema, W., Hupkes, D., Wiggins, G. A., Scharff, C., & Rohrmeirer, M. (2018). Formal Models of Structure Building in Music, Language, and Animal Song. In H. Honing (Ed.), The Origins of Musicality (pp. 253-286). MIT Press. http://cognet.mit.edu/pdfviewer/book/9780262344548/chap11 [details]

    2017

    • 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://cogsci.mindmodeling.org/2017/papers/0300/index.html [details]

    2016

    • 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]
    • Le, P., & Zuidema, W. (2016). Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs. In P. Blunsom, K. Cho, S. Cohen, E. Grefenstette, K. M. Hermann, L. Rimell, J. Weston, & S. W. Yih (Eds.), The 54th Annual Meeting of the Association for Computational Linguistics. Proceedings of the 1st Workshop on Representation Learning for NLP: ACL 2016 : August 11th, 2016, Berlin, Germany (pp. 87-93). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-1610 [details]
    • Veldhoen, S., Hupkes, D., & Zuidema, W. (2016). Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure. 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 6 (CEUR Workshop Proceedings; Vol. 1773). CEUR-WS. http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper6.pdf [details]

    2015

    • Le, P., & Zuidema, W. (2015). Compositional Distributional Semantics with Long Short Term Memory. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics: *SEM 2015 : June 4-5, 2015, Denver, Colorado, UAA (pp. 10-19). The *SEM 2015 Organizing Committee. http://aclweb.org/anthology/S/S15/S15-1002.pdf [details]
    • Le, P., & Zuidema, W. (2015). The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization. In L. Márquez, C. Callison-Burch, & J. Su (Eds.), EMNLP 2015 Lisbon : conference proceedings: September 17-21 : Conference on Empirical Methods in Natural Language Processing (pp. 1155-1164). The Association for Computational Linguistics. https://aclweb.org/anthology/D/D15/D15-1137.pdf [details]
    • Le, P., & Zuidema, W. (2015). Unsupervised Dependency Parsing: Let's Use Supervised Parsers. In R. Mihalcea, J. Chai, & A. Sarkar (Eds.), NAACL HLT 2015: The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Conference : May 31-June 5, 2015, Denver, Colorado, USA (pp. 651-661). The Association for Computational Linguistics. http://aclweb.org/anthology/N/N15/N15-1067.pdf [details]
    • Merker, B., Morley, I., & Zuidema, W. (2015). Five fundamental constraints on theories of the origins of music. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1664), Article 20140095. https://doi.org/10.1098/rstb.2014.0095 [details]
    • Rohrmeier, M., Zuidema, W., Wiggins, G. A., & Scharff, C. (2015). Principles of structure building in music, language and animal song. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1664), Article 20140097. https://doi.org/10.1098/rstb.2014.0097 [details]

    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]
    • Borensztajn, G., Zuidema, W., & Bechtel, W. (2014). Systematicity and the need for encapsulated representations. In The Architecture of Cognition: Rethinking Fodor and Pylyshyn's Systematicity Challenge (pp. 165-190). MIT Press. https://direct.mit.edu/books/edited-volume/chapter-pdf/2269886/9780262322461_cai.pdf
    • Le, P., & Zuidema, W. (2014). The Inside-Outside Recursive Neural Network model for Dependency Parsing. In A. Moschitti, B. Pang, & W. Daelemans (Eds.), EMNLP 2014: the 2014 Conference on Empirical Methods in Natural Language Processing: proceedings of the conference: October 25-29, 2014, Doha, Qatar (pp. 729-739). Association for Computational Linguistics. http://www.aclweb.org/anthology/D/D14/D14-1081.pdf [details]

    2013

    • Beekhuizen, B., Bod, R., & Zuidema, W. (2013). Three Design Principles of Language: The Search for Parsimony in Redundancy. Language and Speech, 56(3), 265-290. Advance online publication. https://doi.org/10.1177/0023830913484897 [details]
    • Le, P., Zuidema, W., & Scha, R. (2013). Learning from errors: Using vector-based compositional semantics for parse reranking. In A. Allauzen, H. Larochelle, C. Manning, & R. Socher (Eds.), 51st Annual Meeting of the Association for Computational Linguistics : ACL 2013 : Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality: August 9, 2013, Sofia, Bulgaria (pp. 11-19). The Association for Computational Linguistics. https://aclanthology.org/W13-3202 [details]
    • Zuidema, W. (2013). Context-freeness Revisited. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Cooperative Minds: Social Interaction and Group Dynamics: Proceedings of the 35th Annual Meeting of the Cognitive Science Society : Berlin, Germany, July 31-August 3, 2013 (pp. 1664-1669). Cognitive Science Society. https://escholarship.org/uc/item/6p5781r9 [details]
    • Zuidema, W. H. (2013). Contextfreeness Revisited. In Proceedings of the 35th Annual Conference of the Cognitive Science Society

    2012

    • Le, P., & Zuidema, W. (2012). Learning compositional semantics for open domain semantic parsing. In M. Kay, & C. Boitet (Eds.), 24th International Conference on Computational Linguistics: proceedings of COLING 2012: technical papers: 8-15 December 2012, Mumbai, India (pp. 1535-1551). Indian Institute of Technology Bombay. http://aclweb.org/anthology/C/C12/C12-1094.pdf [details]

    2011

    • Borensztajn, G., & Zuidema, W. (2011). Episodic grammar: a computational model of the interaction between episodic and semantic memory in language processing. In L. Carlson, C. Hoelscher, & T. F. Shipley (Eds.), Expanding the Space of Cognitive Science: proceedings of the 33d Annual Meeting of the Cognitive Science Society: Boston, Massachusetts, July 20-23, 2011 (pp. 507-512). Cognitive Science Society. https://cogsci.mindmodeling.org/2011/papers/0094/index.html [details]
    • Kunert, R., Fernández, R., & Zuidema, W. (2011). Adaptation in Child Directed Speech: Evidence from Corpora. In R. Artstein, M. Core, D. DeVault, K. Georgila, E. Kaiser, & A. Stent (Eds.), SemDial 2011 (Los Angelogue): Proceedings of the 15th Workshop on the Semantics and Pragmatics of Dialogue : September 21-23, 2011, Los Angeles, California (pp. 112-119). (Proceedings SemDial; Vol. 2011). Institute for Creative Technologies, University of Southern California. [details]

    2010

    2009

    • Borensztajn, G., Zuidema, W., & Bod, R. (2009). Children's grammars grow more abstract with age—Evidence from an automatic procedure for identifying the productive units of language. Topics in Cognitive Science, 1(1), 175-188. https://doi.org/10.1111/j.1756-8765.2008.01009.x [details]
    • Borensztajn, G., Zuidema, W., & Bod, R. (2009). The hierarchical prediction network: Towards a neural theory of grammar acquisition. In N. A. Taatgen, & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2974-2979). Cognitive Science Society. http://csjarchive.cogsci.rpi.edu/Proceedings/2009/papers/654/index.html [details]
    • Ferdinand, V., & Zuidema, W. (2009). Thomas' theorem meets Bayes' rule: A model of the iterated learning of language. In N. A. Taatgen, & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1786-1791). Cognitive Science Society. http://csjarchive.cogsci.rpi.edu/proceedings/2009/papers/376/index.html [details]
    • Sangati, F., & Zuidema, W. (2009). Unsupervised methods for head assignments. In A. Lascarides, C. Gardent, & J. Nivre (Eds.), Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2009: 30 March-3 April 2009, Megaron Athens International Conference Centre, Athens, Greece (pp. 701-709). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1609067.1609145 [details]
    • Sangati, F., Zuidema, W., & Bod, R. (2009). A generative re-ranking model for dependency parsing. In Proceedings of the 11th International Conference on Parsing Technologies, IWPT-09: 7-9 October 2009, Paris, France (pp. 238-241). Association for Computational Linguistics (ACL). http://portal.acm.org/citation.cfm?id=1697285 [details]
    • Zuidema, W., & de Boer, B. (2009). The evolution of combinatorial phonology. Journal of Phonetics, 37(2), 125-140. https://doi.org/10.1016/j.wocn.2008.10.003 [details]
    • van Heijningen, C. A. A., de Visser, J., Zuidema, W., & ten Cate, C. (2009). Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species. Proceedings of the National Academy of Sciences of the United States of America, 106(48), 20538-20534. https://doi.org/10.1073/pnas.0908113106 [details]

    2008

    • Borensztajn, G., Zuidema, W., & Bod, R. (2008). Children's grammars grow more abstract with age - Evidence from an automatic procedure for identifying the productive units of language. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 47-52). Cognitive Science Society. http://www.cogsci.rpi.edu/CSJarchive/proceedings/2008/pdfs/p47.pdf [details]
    • Sangati, F., & Zuidema, W. (2008). Communication, cooperation and coherence: putting mathematical models into perspective. In A. D. M. Smith, K. Smith, & R. Ferrer i Cancho (Eds.), The evolution of language: proceedings of the 7th International Conference (EVOLANG7), Barcelona, Spain, 12-15 March 2008 (pp. 491-492). World Scientific. https://doi.org/10.1142/9789812776129_0096 [details]

    2007

    • Zuidema, W. H. (2007). Parsimonious Data-Oriented Parsing. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 551-560). Stroudsburg, PA, USA: Association for Computational Linguistics. [details]

    2006

    • Zuidema, W. H. (2006). Theoretical Evaluation of Estimation Methods for Data-Oriented Parsing. In Conference Companion / Proceedings 11th Conference of the European Chapter of the Association for Computational Linguistics (pp. 183-186). Association for Computational Linguistics. [details]
    • Zuidema, W. H. (2006). What are the Productive Units of Natural Language Grammar? A DOP Approach to the Automatic Identification of Constructions. In Proceedings of the Tenth Conference on Computational Natural Language Learning (CONLL-X) (pp. 29-36). Association for Computational Linguistics. [details]
    • Zuidema, W. H., & O'Donnell, T. (2006). Beyond the argument from design. In A. Cangelosi, A. D. M. Smith, & K. Smith (Eds.), Proceedings of the 6th International Conference Evolang6 (pp. 459-460) [details]

    2023

    2022

    2017

    2014

    • Honing, H., & Zuidema, W. (2014). Decomposing dendrophilia: Comment on "Toward a Computational Framework for Cognitive Biology: Unifying approaches from cognitive neuroscience and comparative cognition" by W. Tecumseh Fitch. Physics of Life Reviews, 11(3), 375-376. https://doi.org/10.1016/j.plrev.2014.06.020 [details]

    2013

    2008

    2006

    • Bod, R., Fitz, H., & Zuidema, W. H. (2006). On the Structural Ambiguity in Natural Language that the Neural Architecture Cannot Deal With. Behavioral and Brain Sciences, 29(1), 71-72. [details]

    2018

    2013

    • Zuidema, W. H. (2013). Honderdduizend jaar nuttig geklets. In M. Geels, & T. van Opijnen (Eds.), Nederland in ideeëen: 101 denkers over inzichten en innovaties die ons land verander(d)en Maven Publishing.

    2008

    2007

    2008

    • Zuidema, W. H. (2008). Empirical evidence for recursive hierarchical structure in child language. Paper presented at PsychoCompLA-2008 held in Washington D.C., as part of the 30th meeting of the Cognitive Science Society (CogSci-2008), .

    Prize / grant

    • Cornelissen, B., Zuidema, W. & Burgoyne, A. (2020). Best Multi/Interdisciplinary Research. https://www.ismir2020.net/awards/
    • Garrido Alhama, R. & Zuidema, W. (2019). Best Article Award of Psychonomic Bulletin & Review (2019).
    • Rouw, R., Zuidema, W. & Ridderinkhof, R. (2019). Letter-color correspondences in synesthetes and non-synesthetes across languages..

    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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