Scientific publications

Names in bold are names of researchers associated with the Language in Interaction consortium. This page is updated on a yearly basis when the Annual Report is published.

Online publications, research data and other research products can also be found on the Language in Interaction project page on OpenAIRE. Publications including the Language in Interaction funder ID are automatically added to the OpenAIRE project page. Missing publications can be linked manually to the project by researchers through this link. An OpenAIRE account is required, this is free and easy to create.

NrReferenceDOI
1Abnar, S., & Zuidema, W. (2020). Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928.
2Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. (2017). Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity. arXiv preprint arXiv:1711.09285.
3Abnar, S., Dehghani, M., & Zuidema, W. (2020). Transferring inductive biases through knowledge distillation. arXiv preprint arXiv:2006.00555.
4Abrahamse, R., Beynon, A., & Piai, V. (2021). Long-term auditory processing outcomes in early implanted young adults with cochlear implants: The mismatch negativity vs. P300 response. Clinical Neurophysiology, 132(1), 258-268.https://doi.org/10.1016/j.clinph.2020.09.022
5Adolfi, F., Wareham, T., & van Rooij, I.
(2022). A computational complexity perspective on segmentation as a cognitive subcomputation. Topics in Cognitive Science.		10.1111/tops.12629 
6Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition: the R&R model. In the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 1531-1536). Cognitive Science Society.
7Alhama, 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.
8Ambrogioni, L., Berezutskaya, J., Güçlü, U., van den Borne, E. W., Güçlütürk, Y., van Gerven, M. A., & Maris, E. (2017). Bayesian model ensembling using meta-trained recurrent neural networks. In Workshop on Meta-Learning (MetaLearn 2017)-Neural Information Processing Systems (NIPS 2017) (pp. 1-5). [Sl: sn].
9Amelink, J. S., Postema, M. C., Kong, X. Z., Schijven, D., Castillo, A. C., Soheili-Nezhad, S., Sha, Z., Molz, B., Joliot, M., Fisher, S.E. & Francks, C. (2023). Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness. bioRxiv, 2023-11.https://doi.org/10.1101/2023.11.22.568256
10Arana, S., Marquand, A., Hultén, A., Hagoort, P., & Schoffelen, J. M. (2020). Sensory modality-independent activation of the brain network for language. Journal of Neuroscience, 40(14), 2914-2924.https://doi.org/10.1523/JNEUROSCI.2271-19.2020
11Arana, S., Pesnot Lerousseau, J., & Hagoort, P. (2023). Deep learning models to study sentence comprehension in the human brain. Language, Cognition and Neuroscience, 1-19.https://doi.org/10.1080/23273798.2023.2198245
12Arana, S., Hagoort, P., Schoffelen, J. M., & Rabovsky, M. (2024). Perceived similarity as a window into representations of integrated sentence meaning. Behavior Research Methods, 56(3), 2675-2691.https://doi.org/10.3758/s13428-023-02129-x
13Araújo, S., Huettig, F., & Meyer, A. (2020). What underlies the deficit in rapid automatized naming (RAN) in adults with dyslexia? Evidence from eye movements. Scientific Studies of Reading, 1-16.https://doi.org/10.1080/10888438.2020.1867863
14Armeni, K., Willems, R. M., & Frank, S. L. (2017). Probabilistic language models in cognitive neuroscience: Promises and pitfalls. Neuroscience & Biobehavioral Reviews, 83, 579-588.
15Baas, M., Boot, N., van Gaal, S., de Dreu, C. K., & Cools, R. (2020). Methylphenidate does not affect convergent and divergent creative processes in healthy adults. NeuroImage, 205, 116279. https://doi.org/10.1016/j.neuroimage.2019.116279https://doi.org/10.1016/j.neuroimage.2019.116279
16Beinborn, L., & Choenni, R. (2020). Semantic drift in multilingual representations. Computational Linguistics, 46, 571–603.https://doi.org/10.1162/coli_a_00382
17Berezutskaya, J. (2020). Data-driven modeling of the neural dynamics underlying language processing (Doctoral dissertation, Utrecht University).https://doi.org/10.33540/103
18Berezutskaya, J., Ambrogioni, L., Ramsey, N. F., & van Gerven, M. A. (2022). Towards naturalistic speech decoding from intracranial brain data. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3100-3104). IEEE.10.1109/EMBC48229.2022.9871301
19Berezutskaya, J., Baratin, C., Freudenburg, Z. V., & Ramsey, N. F. (2020). High‐density intracranial recordings reveal a distinct site in anterior dorsal precentral cortex that tracks perceived speech. Human brain mapping, 41(16), 4587-4609.https://doi.org/10.1371/journal.pcbi.1007992
20Berezutskaya, J., Freudenburg, Z. V., Ambrogioni, L., Güçlü, U., van Gerven, M. A., & Ramsey, N. F. (2020). Cortical network responses map onto data-driven features that capture visual semantics of movie fragments. Scientific reports, 10(1), 1-21.https://doi.org/10.1038/s41598-020-68853-y
21Berezutskaya, J., Freudenburg, Z. V., Güçlü, U., van Gerven, M. A., & Ramsey, N. F. (2020). Brain-optimized extraction of complex sound features that drive continuous auditory perception. PLoS computational biology, 16(7), e1007992.https://doi.org/10.1371/journal.pcbi.1007992
22Berezutskaya, J., Freudenburg, Z. V., Vansteensel, M. J., Aarnoutse, E. J., Ramsey, N. F., & van Gerven, M. A.
(2022). Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models. bioRxiv.		10.1101/2022.08.02.502503 
23Berezutskaya, J., Freudenburg, Z., Güçlü, U., van Gerven, M., & Ramsey, N. (2017). Neural tuning to low-level features of speech throughout the perisylvian cortex. The Journal of Neuroscience, 37(33):7906–7920.https://doi.org/10.1523/JNEUROSCI.0238-17.2017
24Berezutskaya, J., Freudenburg, Z., Ramsey, N., Güçlü, U., van Gerven, M., Duivesteijn, W., … & Postma, E. (2017). Modeling brain responses to perceived speech with LSTM networks. In Benelearn (pp. 149-153).
25Berezutskaya, J., Saive, A. L., Jerbi, K., & van Gerven, M. (2022). How does artificial intelligence contribute to iEEG research? In: Axmacher (Ed.), Intracranial EEG for cognitive neuroscientists, Springerhttps://doi.org/10.48550/arXiv.2207.13190
26Berezutskaya, J., Vansteensel, M. J., Aarnoutse, E. J., Freudenburg, Z. V., Piantoni, G., Branco, M. P., & Ramsey, N. F. (2022). Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. Scientific Data, 9(1), 1-13.
27Berezutskaya, J., Vansteensel, M. J., Aarnoutse, E.J., Freudenburg, Z.V., Pianotoni, G., Branco M.P., & Ramsey, N.F. (2021). Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003688.v1.0.6https://doi.org/10.18112/openneuro.ds003688.v1.0.6
28Blazquez Freches, G., Haak, K. V., Beckmann, C. F., & Mars, R. B. (2021). Connectivity gradients on tractography data: Pipeline and example applications. Human brain mapping, 42(18), 5827-5845.https://doi.org/10.1002/hbm.25623
29Blok, L. S., Hiatt, S. M., Bowling, K. M., Prokop, J. W., Engel, K. L., Cochran, J. N., … & Cooper, G. M. (2018). De novo mutations in MED13, a component of the Mediator complex, are associated with a novel neurodevelopmental disorder. Human genetics, 137(5), 375-388.https://doi.org/10.1007/s00439-018-1887-y
30Blok, L. S., Rousseau, J., Twist, J., Ehresmann, S., Takaku, M., Venselaar, H., … & Campeau, P. M. (2018). CHD3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language. Nature communications, 9(1), 1-12.https://doi.org/10.1038/s41467-018-06014-6
31Blok, L. S., Vino, A., Den Hoed, J., Underhill, H. R., Monteil, D., Li, H., … & Fisher, S. E. (2020). Heterozygous variants that disturb the transcriptional repressor activity of FOXP4 cause a developmental disorder with speech/language delays and multiple congenital abnormalities. Genetics in Medicine, 1-9.https://doi.org/10.1038/s41436-020-01016-6
32Blokpoel, M. & van Rooij, I. (2021). Theoretical modeling for cognitive science and psychology (Open, interactive book). https://computationalcognitivescience.github.io/lovelace/home
33Blokpoel, M. (2018). Sculpting Computational‐Level Models. Topics in cognitive science, 10(3), 641-648.https://doi.org/10.1111/tops.12282
34Blokpoel, M., Dingemanse, M., Woensdregt, M., Kachergis, G., Bögels, S., Toni, I., & van Rooij, I. (2019, November 7). Pragmatic communicators can overcome asymmetry by exploiting ambiguity. https://doi.org/10.31219/osf.io/q56xshttps://doi.org/10.31219/osf.io/q56xs
35Bögels, S., & Levinson, S. C. (2017). The brain behind the response: Insights into turn-taking in conversation from neuroimaging. Research on Language and Social Interaction, 50(1), 71-89.https://doi.org/10.1080/08351813.2017.1262118
36Bögels, S., Casillas, M., & Levinson, S. C. (2018). Planning versus comprehension in turn-taking: Fast responders show reduced anticipatory processing of the question. Neuropsychologia, 109, 295-310.https://doi.org/10.1016/j.neuropsychologia.2017.12.028
37Bögels, S., Kendrick, K. H., & Levinson, S. C. (2020). Conversational expectations get revised as response latencies unfold. Language, Cognition and Neuroscience, 35(6), 766-779.https://doi.org/10.1080/23273798.2019.1590609
38Bögels, S., Milvojevic, B., De Haas, N., Döller, C., Rasenberg, M., Ozyurek, A., … & Toni, I. (2018). Creating shared conceptual representations. In the 10th Dubrovnik Conference on Cognitive Science.http://hdl.handle.net/21.11116/0000-0003-F389-0
39Bosker, H. R. (2014). The processing and evaluation of fluency in native and non-native speech. PhD Thesis, Utrecht University, Utrecht.
40Bosker, H. R. (2016). Our own speech rate influences speech perception. In Speech Prosody 2016 (pp. 227-231).
41Bosker, H. R. (2017). Accounting for rate-dependent category boundary shifts in speech perception. Attention, Perception & Psychophysics, 79, 333-343. doi:10.3758/s13414-016-1206-4.https://doi.org/10.3758/s13414-016-1206-4.
42Bosker, H. R. (2017). Accounting for rate-dependent category boundary shifts in speech perception. Attention, Perception, & Psychophysics, 79(1), 333-343.
43Bosker, H. R. (2017). How our own speech rate influences our perception of others. Journal of Experimental Psychology: Learning, Memory, and Cognition,43(8), 1225-1238. doi:10.1037/xlm0000381.https://doi.org/10.1037/xlm0000381.
44Bosker, H. R. (2017). How your own speech rate can change how you listen to others. In the Abstraction, Diversity and Speech Dynamics Workshop.
45Bosker, H. R. (2017). Neural entrainment persists after stimulation, guiding temporal sampling of subsequent speech. In the Neural Oscillations in Speech and Language Processing symposium.
46Bosker, H. R. (2017). The role of temporal amplitude modulations in the political arena: Hillary Clinton vs. Donald Trump. In Proceedings of Interspeech 2017(pp. 2228-2232). doi:10.21437/Interspeech.2017-142.https://doi.org/10.21437/Interspeech.2017-142.
47Bosker, H. R. (2017). The role of temporal amplitude modulations in the political arena: Hillary Clinton vs. Donald Trump. In Interspeech 2017 (pp. 2228-2232).
48Bosker, H. R. (2018). Putting Laurel and Yanny in context. The Journal of the Acoustical Society of America, 144(6), EL503-EL508.https://doi.org/10.1121/1.5070144
49Bosker, H. R. (2021). The Contribution of Amplitude Modulations in Speech to Perceived Charisma. In Voice Attractiveness (pp. 165-181). Springer, Singapore.https://doi.org/10.1007/978-981-15-6627-1_10
50Bosker, H. R., & Cooke, M. (2017). Comparing the rhythmic properties of plain and Lombard speech. In the Abstraction, Diversity and Speech Dynamics Workshop.
51Bosker, H. R., & Cooke, M. (2017). Rhythm in plain and Lombard speech. In the 9th Speech in Noise Workshop.
52Bosker, H. R., & Cooke, M. (2018). Talkers produce more pronounced amplitude modulations when speaking in noise. The Journal of the Acoustical Society of America, 143(2), EL121-EL126. doi:10.1121/1.5024404.https://doi.org/10.1121/1.5024404
53Bosker, H. R., & Ghitza, O. (2018). Entrained theta oscillations guide perception of subsequent speech: Behavioral evidence from rate normalization. Language, Cognition and Neuroscience, 33(8), 955-967. doi:10.1080/23273798.2018.1439179.https://doi.org/10.1080/23273798.2018.1439179
54Bosker, H. R., & Kösem, A. (2017). An entrained rhythm’s frequency, not phase, influences temporal sampling of speech. In Proceedings of Interspeech 2017(pp. 2416-2420). doi:10.21437/Interspeech.2017-73.https://doi.org/10.21437/Interspeech.2017-73.
55Bosker, H. R., & Kösem, A. (2017). An entrained rhythm’s frequency, not phase, influences temporal sampling of speech. In Interspeech 2017 (pp. 2416-2420).
56Bosker, H. R., & Reinisch, E. (2015). Normalization for speechrate in native and nonnative speech. In 18th International Congress of Phonetic Sciences (ICPhS 2015). International Phonetic Association.
57Bosker, H. R., & Reinisch, E. (2016). Testing the ‘Gabbling Foreigner Illusion’: Do foreign languages sound fast?. In the 2nd Workshop on Psycholinguistic Approaches to Speech Recognition in Adverse Conditions (PASRAC).
58Bosker, H. R., & Reinisch, E. (2017). Foreign languages sound fast: evidence from implicit rate normalization. Frontiers in Psychology, 8: 1063. doi:10.3389/fpsyg.2017.01063.https://doi.org/10.3389/fpsyg.2017.01063.
59Bosker, H. R., Peeters, D., & Holler, J. (2020). How visual cues to speech rate influence speech perception. Quarterly Journal of Experimental Psychology, 73(10), 1523-1536.https://doi.org/10.1177/1747021820914564
60Bosker, H. R., Quené, H., Sanders, T. J. M., & de Jong, N. H. (2014). Native ‘um’s elicit prediction of low-frequency referents, but non-native ‘um’s do not.Journal of Memory and Language, 75, 104-116. doi:10.1016/j.jml.2014.05.004.https://doi.org/10.1016/j.jml.2014.05.004
61Bosker, H. R., Quené, H., Sanders, T. J. M., & de Jong, N. H. (2014). The perception of fluency in native and non-native speech. Language Learning, 64, 579-614. doi:10.1111/lang.12067.https://doi.org/10.1111/lang.12067
62Bosker, H. R., Reinisch, E., & Sjerps, M. J. (2016). Listening under cognitive load makes speech sound fast. In H. van den Heuvel, B. Cranen, & S. Mattys (Eds.), Proceedings of the Speech Processing in Realistic Environments [SPIRE] Workshop (pp. 23-24).
63Bosker, H. R., Reinisch, E., & Sjerps, M. J. (2017). Cognitive load makes speech sound fast, but does not modulate acoustic context effects. Journal of Memory and Language, 94, 166-176. doi:10.1016/j.jml.2016.12.002.https://doi.org/10.1016/j.jml.2016.12.002.
64Bosker, H. R., Reinisch, E., & Sjerps, M. J. (2017). Cognitive load makes speech sound fast, but does not modulate acoustic context effects. Journal of Memory and Language, 94, 166-176.https://doi.org/10.1016/j.jml.2016.12.002
65Bosker, H. R., Tjiong, V., Quené, H., Sanders, T., & De Jong, N. H. (2015). Both native and non-native disfluencies trigger listeners’ attention. In Disfluency in Spontaneous Speech: DISS 2015: An ICPhS Satellite Meeting. Edinburgh: DISS2015.
66Braunsdorf, M., Freches, G. B., Roumazeilles, L., Eichert, N., Schurz, M., Uithol, S., … & Mars, R. B. (2021). Does the temporal cortex make us human? A review of structural and functional diversity of the primate temporal lobe. Neuroscience & Biobehavioral Reviews, 131, 400-410.https://doi.org/10.1016/j.neubiorev.2021.08.032
67Brysbaert, M., Sui, L., Dirix, N., & Hintz, F. (2020). Dutch author recognition test. Journal of cognition, 3(1).https://doi.org/10.5334/joc.95
68Burgering, M. A., Ten Cate, C., & Vroomen, J. (2018). Mechanisms underlying speech sound discrimination and categorization in humans and zebra finches. Animal cognition, 21(2), 285-299.https://doi.org/10.1007/s10071-018-1165-3
69Burgering, M. A., van Laarhoven, T., Baart, M., & Vroomen, J. (2020). Fluidity in the perception of auditory speech: Cross-modal recalibration of voice gender and vowel identity by a talking face. Quarterly Journal of Experimental Psychology, 73(6), 957-967.https://doi.org/10.1177/1747021819900884
70Burgering, M. A., Vroomen, J., & ten Cate, C. (2018, September 20). Zebra Finches (Taeniopygia guttata) Can Categorize Vowel-Like Sounds on Both the Fundamental Frequency (“Pitch”) and Spectral Envelope. Journal of Comparative Psychology. Advance online publication. http://dx.doi.org/10.1037/com0000143http://dx.doi.org/10.1037/com0000143
71Burgering, M. (2021). The multidimensionality of speech categorization: Exploring shared mechanisms in songbirds together with audiovisual and neural mechanisms in humans. [Dissertation]
72Camerino, I., Ferreira, J., Vonk, J. M., Kessels, R. P., de Leeuw, F. E., Roelofs, A., David Copland, & Piai, V. (2024). Systematic Review and Meta-Analyses of Word Production Abilities in Dysfunction of the Basal Ganglia: Stroke, Small Vessel Disease, Parkinson’s Disease, and Huntington’s Disease. Neuropsychology Review, 1-26.https://doi.org/10.1007/s11065-022-09570-3
73Camerino, I., Sierpowska, J., Reid, A., Meyer, N. H., Tuladhar, A. M., Kessels, R. P., … & Piai, V. (2021). White matter hyperintensities at critical crossroads for executive function and verbal abilities in small vessel disease. Human Brain Mapping, 42(4), 993-1002.https://doi.org/10.1002/hbm.25273
74Cao, Y., Oostenveld, R., Alday, P. M., & Piai, V.
(2022). Are alpha and beta oscillations spatially dissociated over the cortex in context‐driven spoken‐word production?. Psychophysiology, 59(6), e13999.		https://doi.org/10.1111/psyp.13999
75Carota, F., Schoffelen, J. M., Oostenveld, R., & Indefrey, P. (2022). The time course of language production as revealed by pattern classification of MEG sensor data. Journal of Neuroscience, 42(29), 5745-5754.https://doi.org/10.1523/JNEUROSCI.1923-21.2022 
76Carota, F., Schoffelen, J.-M., R. Oostenveld, Indefrey, P. (2022). The neural dynamics of language production as revealed by pattern classification of MEG data. Journal of Neuroscience.
77Chu, M., Tobin, P., Ioannidou, F., & Bašnáková, J. (2023). Encoding and decoding hidden meanings in face-to-face communication: Understanding the role of verbal and nonverbal behaviors in indirect replies. Journal of Experimental Psychology: General, 152(4), 1030.
78Chupina, I., Sierpowska, J., Zheng, X. Y., Dewenter, A., Piastra, M. C., & Piai, V. (2022). Time course of right‐hemisphere recruitment during word production following left‐hemisphere damage: A single case of young stroke. European Journal of Neuroscience, 56(8), 5235-5259.10.1111/ejn.15813 
79Cools, R. (2016). The costs and benefits of brain dopamine for cognitive control. Wiley Interdisciplinary Reviews: Cognitive Science, 7(5), 317-329.
80Cools, R. (2019). Chemistry of the Adaptive Mind: Lessons from Dopamine. Neuron, 104(1), 113-131. https://doi.org/10.1016/j.neuron.2019.09.035https://doi.org/10.1016/j.neuron.2019.09.035
81Cools, R., Froböse, M., Aarts, E., & Hofmans, L. (2019). Dopamine and the motivation of cognitive control. In Handbook of clinical neurology (Vol. 163, pp. 123-143). Elsevier. https://doi.org/10.1016/B978-0-12-804281-6.00007-0https://doi.org/10.1016/B978-0-12-804281-6.00007-0
82Coopmans, C. W., De Hoop, H., Hagoort, P., & Martin, A. E. (2021). Cortical tracking and the relationship between structure and meaning. In the 13th Annual Meeting of the Society for the Neurobiology of Language (SNL 2021 Virtual Edition).http://hdl.handle.net/21.11116/0000-0009-5887-C
83Coopmans, C. W., De Hoop, H., Hagoort, P., & Martin, A. E. (2022). Effects of structure and meaning on cortical tracking of linguistic units in naturalistic speech. Neurobiology of Language, 3(3), 386-412.https://doi.org/10.1162/nol_a_00070
84Coopmans, C. W., De Hoop, H., Kaushik, K., Hagoort, P., & Martin, A. E. (2021, February). Structure-(in) dependent Interpretation of Phrases in Humans and LSTMs. In Proceedings of the Society for Computation in Linguistics 2021 (pp. 459-463).
85Coopmans, C. W., De Hoop, H., Kaushik, K., Hagoort, P., & Martin, A. E. (2021). Hierarchy in language interpretation: Evidence from behavioural experiments and computational modelling. Language, Cognition and Neuroscience. Advance online publication.https://doi.org/10.1080/23273798.2021.1980595
86Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2021). Cosine Contours: a Multipurpose Representation for Melodies. ISMIR. In Proceedings of the 22th International Conference on Music Information Retrieval.
87Corps, R. E., Knudsen, B., & Meyer, A. S. (2022). Overrated gaps: Inter-speaker gaps provide limited information about the timing of turns in conversation. Cognition, 223: 105037. doi:10.1016/j.cognition.2022.105037.https://doi.org/10.1016/j.cognition.2022.105037
88Cosma, R.A., Knobel, L., De Heer Kloots, M., & Van der Wal, O. (2022).Seeing the bigger picture: Can deep neural agents learn higher-level concepts in crossmodal referential games? Proceeding ML4Evolang 2022 Workshop.
89Cutler, A. (2017). Converging evidence for abstract phonological knowledge in speech processing. In the 39th annual conference of the Cognitive Science Society (CogSci 2017) (pp. 1447-1448). Cognitive Science Society.
90Cutler, A., & Farrell, J. (2018). Listening in first and second language. The TESOL Encyclopedia of English Language Teaching, 1-7.https://doi.org/10.1002/9781118784235.eelt0583
91Cutler, A., & Jesse, A. (2021). Word stress in speech perception. The Handbook of Speech Perception, 239-265.
92Cutler, A., Junge, C., Spokes, T. & Kidd, E. (2018). Phonological acquisition: Stress-based segmentation in English.  Abstracts of Laboratory Phonology 16, Lisbon; pp. 22-23
93Cutter, M. G., Martin, A. E., & Sturt, P. (2020). Capitalization interacts with syntactic complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(6), 1146.https://doi.org/10.1037/xlm0000780
94Cutter, M. G., Martin, A. E., & Sturt, P. (2020). Readers detect an low-level phonological violation between two parafoveal words. Cognition, 204, 104395.https://doi.org/10.1016/j.cognition.2020.104395
95Cutter, M. G., Martin, A. E., & Sturt, P. (2020). The activation of contextually predictable words in syntactically illegal positions. Quarterly Journal of Experimental Psychology, 73(9), 1423-1430.https://doi.org/10.1177/1747021820911021
96Dai, B., McQueen, J. M., Terporten, R., Hagoort, P., & Kösem, A. (2022). Distracting Linguistic Information Impairs Neural Tracking of Attended Speech. Current Research in Neurobiology, 3: 100043.https://doi.org/10.1016/j.crneur.2022.100043
97de Lange, F. P., & Ekman, M. (2018). Vision: Framing orientation selectivity. Elife, 7, e39762.https://doi.org/10.7554/eLife.39762
98De Wit, L., Kessels, R. P. C., Kurasz, A. M., Amofa, P., Sr., O’Shea, D., Marsiske, M., Chandler, M. J., et al. (2022). Declarative Learning, Priming, and Procedural Learning Performances comparing Individuals with Amnestic Mild Cognitive Impairment, and Cognitively Unimpaired Older Adults. Journal of the International Neuropsychological Society. doi:10.1017/s135561772200002910.1017/S1355617722000029
99de Zubicaray, G. I., & Piai, V. (2019). Investigating the spatial and temporal components of speech production. The Oxford handbook of neurolinguistics, 471-497.
100Dekker, P., & Zuidema, W. (2020). Word prediction in computational historical linguistics. Journal of Language Modelling, 8(2), 295-3https://doi.org/10.15398/jlm.v8i2.268
101Den Hoed, J., & Fisher, S. E. (2020). Genetic pathways involved in human speech disorders. Current Opinion in Genetics & Development, 65, 103-111.https://doi.org/10.1016/j.gde.2020.05.012
102Dideriksen, C., Christiansen, M. H., Tylén, K., Dingemanse, M.
, & Fusaroli, R. (2022). Quantifying the interplay of conversational devices in building mutual understanding. Journal of Experimental Psychology: General.		https://doi.org/10.1037/xge0001301
101Dingemanse, M., Liesenfeld, A., & Woensdregt, M. (2022). Convergent cultural evolution of continuers (mhmm). In A. Ravignani, R. Asano, D. Valente, F. Ferretti, S. Hartmann, M. Hayashi, et al. (Eds.), The Evolution of Language: Proceedings of the Joint Conference on Language Evolution (JCoLE) (pp. 160-167). Nijmegen: Joint Conference on Language Evolution (JCoLE). doi:10.31234/osf.io/65c79.https://doi.org/10.31234/osf.io/65c79
103Dingemanse, M., Liesenfeld, A., Rasenberg, M., Albert, S., Ameka, F. K., Birhane, A., … & Wiltschko, M. (2023). Beyond Single‐Mindedness: A Figure‐Ground Reversal for the Cognitive Sciences. Cognitive science, 47(1), e13230.https://doi.org/10.1111/cogs.13230
104Doumas, L. A., & Martin, A. E. (2021). A model for learning structured representations of similarity and relative magnitude from experience. Current Opinion in Behavioral Sciences, 37, 158-166.https://doi.org/10.1016/j.cobeha.2021.01.001
105Doumas, L. A., Puebla, G., Martin, A. E., & Hummel, J. E. (2019). Relation learning in a neurocomputational architecture supports cross-domain transfer. arXiv preprint arXiv:1910.05065.
106Drijvers, L. (2019). On the oscillatory dynamics underlying speech-gesture integration in clear and adverse listening conditions (Doctoral dissertation, [Sl: sn]).https://hdl.handle.net/2066/202981
107Drijvers, L., & Ozyurek, A. (2016). Visible speech enhanced: What do gestures and lip movements contribute to degraded speech comprehension?. In the 8th Speech in Noise Workshop (SpiN 2016).
108Drijvers, L., & Ozyurek, A. (2016). What do iconic gestures and visible speech contribute to degraded speech comprehension?. In the Nijmegen Lectures 2016.
109Drijvers, L., & Özyürek, A. (2017). Visual context enhanced: The joint contribution of iconic gestures and visible speech to degraded speech comprehension. Journal of Speech, Language, and Hearing Research, 60(1), 212-222.
110Drijvers, L., & Özyürek, A. (2018). Native language status of the listener modulates the neural integration of speech and iconic gestures in clear and adverse listening conditions. Brain and language, 177, 7-17.https://doi.org/10.1016/j.bandl.2018.01.003
111Drijvers, L., & Özyürek, A. (2019). Non-native listeners benefit less from gestures and visible speech than native listeners during degraded speech comprehension. Language and Speech, 0023830919831311.
112Drijvers, L., & Trujillo, J. P. (2018). Commentary: Transcranial magnetic stimulation over left inferior frontal and posterior temporal cortex disrupts gesture-speech integration. Frontiers in human neuroscience, 12, 256.https://doi.org/10.3389/fnhum.2018.00256
113Drijvers, L., Jensen, O., & Spaak, E. (2021). Rapid invisible frequency tagging reveals nonlinear integration of auditory and visual information. Human Brain Mapping, 42(4), 1138-1152.https://doi.org/10.1002/hbm.25282
114Drijvers, L., Mulder, K., & Ernestus, M. (2016). Alpha and gamma band oscillations index differential processing of acoustically reduced and full forms. Brain and language, 153, 27-37.
115Drijvers, L., Ozyurek, A., & Jensen, O. (2017). Alpha and beta oscillations in the language network, motor and visual cortex index semantic congruency between speech and gestures in clear and degraded speech. In the 47th Annual Meeting of the Society for Neuroscience (SfN).http://hdl.handle.net/21.11116/0000-0002-6979-1
116Drijvers, L., Ozyurek, A., & Jensen, O. (2017). Low-and high-frequency oscillations predict the semantic integration of speech and gestures in clear and degraded speech. In the Neural Oscillations in Speech and Language Processing symposium.http://hdl.handle.net/21.11116/0000-0002-6971-9
117Drijvers, L., Ozyurek, A., & Jensen, O. (2018). Alpha and beta oscillations predict the semantic integration of degraded speech and gestures. Journal of Cognitive Neuroscience, 30 (8), 1086-1097.https://doi.org/10.1162/jocn_a_01301
118Drijvers, L., Ozyurek, A., & Jensen, O. (2018). Hearing and seeing meaning in noise: Alpha, beta and gamma oscillations predict gestural enhancement of degraded speech comprehension. Human Brain Mapping, 39(5), 2075-2087.https://doi.org/10.1002/hbm.23987
119Drijvers, L., Zaadnoordijk, L., & Dingemanse, M. (2015). Sound-symbolism is disrupted in dyslexia: Implications for the role of cross-modal abstraction processes. In 37th Annual Meeting of the Cognitive Science Society (CogSci 2015) (pp. 602-607). Cognitive Science Society.
120Drjivers, L., Ozyurek, A., & Jensen, O. (2016). Gestural enhancement of degraded speech comprehension engages the language network, motor and visual cortex as reflected by a decrease in the alpha and beta band. In the Eighth Annual Meeting of the Society for the Neurobiology of Language (SNL 2016).
121Dubois, Y., Dagan, G., Hupkes, D., & Bruni, E. (2019). Location attention for extrapolation to longer sequences. arXiv preprint arXiv:1911.03872.
122Eijk, L., Ernestus, M., & Schriefers, H. (2019). Alignment of Pitch and Articulation Rate. In Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne.https://hdl.handle.net/2066/208198
123Eijk, L., Fletcher, A., McAuliffe, M., & Janse, E. (2020). The Effects of Word Frequency and Word Probability on Speech Rhythm in Dysarthria. Journal of Speech, Language, and Hearing Research, 63(9), 2833-2845.https://doi.org/10.1044/2020_JSLHR-19-00389
124Eijk, L., Rasenberg, M., Arnese, F., Blokpoel, M., Dingemanse, M., Doeller, C. F., Ernestus, M., Holler, J., Milivojevic, B., Özyürek, A., Pouw, W., van Rooij, I., Schriefers, H., Toni, I., Trujillo, J., & Bögels, S. (2022). The CABB dataset: A multimodal corpus of communicative interactions for behavioural and neural analyses. Neuroimage, 264, [119734]. https://doi.org/10.1016/j.neuroimage.2022.119734https://doi.org/10.1016/j.neuroimage.2022.119734
125Eijk, L., Schriefers, H., & Ernestus, M. (2022). Phonetic alignment to regional variants in interaction. The Journal of the Acoustical Society of America, 151(4), A62-A62.
126Eijk, L.D. (2023) Linguistic alignment. The syntactic, prosodic, and segmental phonetic levels Radboud University Nijmegen – Handle: https://hdl.handle.net/2066/291375 https://hdl.handle.net/2066/291375
127Eising, E., Carrion Castillo, A., Vino, A., Strand, E. A., Jakielski, K. J., Scerri, T. S., Hildebrand, M. S., Webster, R., Ma, A., Mazoyer, B., Francks, C., Bahlo, M., Scheffer, I. E., Morgan, A. T., Shriberg, L. D., & Fisher, S. E. (2019). A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. Molecular Psychiatry, 24(7), 1065-1078. https://doi.org/10.1038/s41380-018-0020-xhttps://doi.org/10.1038/s41380-018-0020-x
128Eising, E., Mirza-Schreiber, N., De Zeeuw, E. L., Wang, C. A., Truong, D. T., …, St Pourcain, B., Francks, C., & Fisher, S. E. (2022). Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people. Proceedings of the National Academy of Sciences of the United States of America, 119(35): e2202764119. https://doi.org/10.1073/pnas.2202764119
129Eisner, F., & McQueen, J. M. (2018). Speech perception. Stevens’ handbook of experimental psychology and cognitive neuroscience, 3, 1-46.
130Eisner, F., Kumar, U., Mishra, R. K., Nand Tripathi, V., Guleria, A., Prakash Singh, J., & Huettig, F. (2016). Literacy acquisition drives hemispheric lateralization of reading. In the 31st International Congress of Psychology (ICP2016).
131Ekerdt, C., Takashima, A., & McQueen, J. M. (2023). Memory consolidation in second language neurocognition. In The Routledge Handbook of Second Language Acquisition and Neurolinguistics (pp. 353-367). Routledge.
132Ernestus, M., Dikmans, M. E., & Giezenaar, G. (2017). Advanced second language learners experience difficulties processing reduced word pronunciation variants. Dutch Journal of Applied Linguistics, 6(1), 1-20.https://doi.org/10.1075/dujal.6.1.01ern
133Favero, P., Berezutskaya, J., Ramsey, N. F., Nazarov, A., & Freudenburg, Z. V. (2022). Mapping acoustics to articulatory gestures in Dutch: Relating speech gestures, acoustics and neural data. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 802-806). IEEE.
134Favier, S., Wright, A., Meyer, A., & Huettig, F. (2019). Proficiency modulates between-but not within-language structural priming. Journal of Cultural Cognitive Science, 3(1), 105-124. https://doi.org/10.1007/s41809-019-00029-1https://doi.org/10.1007/s41809-019-00029-1
135Ferreira, J., Roelofs, A., & Piai, V. (2020). The role of domain-general inhibition in inflectional encoding: Producing the past tense. Cognition, 200, 104235.https://doi.org/10.1016/j.cognition.2020.104235
136Fisher, S. E. (2017). Evolution of language: Lessons from the genome. Psychonomic bulletin & review, 24(1), 34-40.
137Fisher, S. E. (2019). Human genetics: The evolving story of FOXP2. Current Biology, 29(2), R65-R67.https://doi.org/10.1016/j.cub.2018.11.047
138Fisher, S. E. (2019). Key issues and future directions: Genes and language. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 609-620). Cambridge, MA: MIT Press.
139Fisher, S. E., & Tilot, A. K. (2019). Bridging senses: Novel insights from synaesthesia. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences. Advance online publication. https://doi.org/10.1098/rstb.2019.0022https://doi.org/10.1098/rstb.2019.0022
140Fitz, H., & Chang, F. (2015). Prediction in error-based learning explains sentence-level ERP effects. In the 21st Architectures and Mechanisms for Language Processing Conference (AMLaP 2015).
141Fitz, H., & Chang, F. (2017). Meaningful questions: The acquisition of auxiliary inversion in a connectionist model of sentence production. Cognition, 166, 225-250.https://doi.org/10.1016/j.cognition.2017.05.008
142Fitz, H., & Chang, F. (2019). Language ERPs reflect learning through prediction error propagation. Cognitive psychology, 111, 15-52. https://doi.org/10.1016/j.cogpsych.2019.03.002https://doi.org/10.1016/j.cogpsych.2019.03.002
143Fitz, H., Uhlmann, M., Van den Broek, D., Duarte, R., Hagoort, P., & Petersson, K. M. (2020). Neuronal spike-rate adaptation supports working memory in language processing. Proceedings of the National Academy of Sciences, 117(34), 20881-20889.https://doi.org/10.1073/pnas.2000222117
144Fitz, H., Hagoort, P., & Petersson, K. M. (2024). Neurobiological causal models of language processing. Neurobiology of Language, 5(1), 225-247.https://doi.org/10.1162/nol_a_00133
145Francks, C. (2019). In search of the biological roots of typical and atypical human brain asymmetry. Physics of life reviews, 30, 22-24.https://doi.org/10.1016/j.plrev.2019.07.004
146Frank, S. (2021). Cross-language structural priming in recurrent neural network language models. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
147Frank, S. L. (2017). Word embedding distance does not predict word reading time.
148Frank, S. L. (2021). Toward computational models of multilingual sentence processing. Language Learning, 71(S1), 193-218.https://doi.org/10.1111/lang.12406
149Frank, S.L., Aumeistere, A. (2022). EEG+ET sentence reading. Version 1. Radboud University. (dataset).http://doi.org/10.34973/3g69-ha39
150Frank, S. L., & Christiansen, M. H. (2018). Hierarchical and sequential processing of language: A response to: Ding, Melloni, Tian, and Poeppel (2017). Rule-based and word-level statistics-based processing of language: insights from neuroscience. Language, Cognition and Neuroscience. Language, Cognition and Neuroscience, 33(9), 1213-1218.https://doi.org/10.1080/23273798.2018.1424347
151Frank, S. L., & Ernst, P. (2019). Judgements about double-embedded relative clauses differ between languages. Psychological research, 83(7), 1581-1593. https://doi.org/10.1007/s00426-018-1014-7https://doi.org/10.1007/s00426-018-1014-7
152Frank, S. L., & Fitz, H. (2016). Reservoir computing and the Sooner-is-Better bottleneck [Commentary on Christiansen & Slater]. Behavioral and Brain Sciences, 39.
153Frank, S. L., & Willems, R. M. (2017). Word predictability and semantic similarity show distinct patterns of brain activity during language comprehension. Language, Cognition and Neuroscience, 32(9), 1192-1203.https://doi.org/10.1080/23273798.2017.1323109
154Frank, S. L., & Yang, J. (2018). Lexical representation explains cortical entrainment during speech comprehension. PloS one, 13(5), e0197304.https://doi.org/10.1371/journal.pone.0197304
155Frank, S. L., Ernst, P., Thompson, R. L., & Cozijn, R. (2021). The missing-VP effect in readers of English as a second language. Memory & Cognition, 49(6), 1204-1219.https://doi.org/10.3758/s13421-021-01159-0
156Frank, S. L., Monaghan, P., & Tsoukala, C. (2019). Neural network models of language acquisition and processing.  In: P. Hagoort (Ed.), Human Language: from Genes and Brains to Behavior (pp. 277-291). Cambridge, MA: The MIT Press.https://hdl.handle.net/2066/213704
157Frank, S. L., Trompenaars, T., & Vasishth, S. (2016). Cross‐linguistic differences in processing double‐embedded relative clauses: Working‐memory constraints or language statistics?. Cognitive Science, 40(3), 554-578.
158Frank, S.L. (2017). Word embedding distance does not predict word reading time. In G. Gunzelmann, A. Howes, T. Tenbrink, & E.J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 385-390). Austin, TX: Cognitive Science Society.https://hdl.handle.net/2066/180490
159Frank, S.L., & Hoeks, J.C.J. (2019). The interaction between structure and meaning in sentence comprehension: Recurrent neural networks and reading times. In Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 337-343).https://hdl.handle.net/2066/213785
160Frank, S.L., Monaghan, P., & Tsoukala, C. (2019). Neural network models of language acquisition and processing. In: P. Hagoort (Ed.), Human Language: from Genes and Brains to Behavior (pp. 277-291). Cambridge, MA: The MIT Press.https://hdl.handle.net/2066/213704
161Franken, M. K., Acheson, D. J., McQueen, J. M., Hagoort, P., & Eisner, F. (2019). Consistency influences altered auditory feedback processing. Quarterly Journal of Experimental Psychology, 72(10), 2371-2379. https://doi.org/10.1177/1747021819838939https://doi.org/10.1177/1747021819838939
162Freches, G. B., Haak, K. V., Bryant, K. L., Schurz, M., Beckmann, C. F., & Mars, R. B. (2020). Principles of temporal association cortex organisation as revealed by connectivity gradients. Brain Structure and Function, 1-16.https://doi.org/10.1007/s00429-020-02047-0
163Gibson, M., & Bosker, H. R. (2016). Over vloeiendheid in spraak. Tijdschrift Taal, 7(10), 40-45.
164Giglio, L., Ostarek, M., & Hagoort, P. (2021). Decoding the scope of planning in sentence production. In the 13th Annual Meeting of the Society for the Neurobiology of Language (SNL 2021 Virtual Edition).
165Giglio, L., Ostarek, M., Weber, K., & Hagoort, P. (2021). Commonalities and asymmetries in the neurobiological infrastructure for language production and comprehension. Cerebral Cortex. Advance online publication.https://doi.org/10.1093/cercor/bhab287
166Grey, S., Schubel, L. C., McQueen, J. M., & Van Hell, J. G. (2019). Processing foreign-accented speech in a second language: Evidence from ERPs during sentence comprehension in bilinguals. Bilingualism: Language and Cognition, 22(5), 912-929. https://doi.org/10.1017/S1366728918000937https://doi.org/10.1017/S1366728918000937
167Guimarães, J., Sprooten, E., Beckmann, C. F., Franke, B., & Bralten, J. (2022). Shared genetic influences on resting‐state functional networks of the brain. Human Brain Mapping, 43(6), 1787-1803.10.1002/hbm.25712 
168Gunz, P., Tilot, A. K., Wittfeld, K., Teumer, A., Shapland, C. Y., Van Erp, T. G. M., Dannemann, M., Vernot, B., Neubauer, S., Guadalupe, T., Fernandez, G., Brunner, H., Enard, W., Fallon, J., Hosten, N., Völker, U., Profico, A., Di Vincenzo, F., Manzi, G., Kelso, J. and 7 more (2019). Neandertal introgression sheds light on modern human endocranial globularity. Current Biology, 29(1), 120-127. https://doi.org/10.1016/j.cub.2018.10.065https://doi.org/10.1016/j.cub.2018.10.065
169Haak, K. V., Marquand, A. F., & Beckmann, C. F. (2018). Connectopic mapping with resting-state fMRI. Neuroimage, 170, 83-94.https://doi.org/10.1016/j.neuroimage.2017.06.075
170Hagoort, P. (2014). Nodes and networks in the neural architecture for language: Broca’s region and beyond. Current opinion in Neurobiology, 28, 136-141.
171Hagoort, P. (2017). Don’t forget neurobiology: An experimental approach to linguistic representation. Commentary on Branigan and Pickering” An experimental approach to linguistic representation”. Behavioral and Brain Sciences, 40.https://doi.org/10.1017/S0140525X17000401
172Hagoort, P. (2017). The core and beyond in the language-ready brain. Neuroscience & Biobehavioral Reviews, 81, 194-204.https://doi.org/10.1016/j.neubiorev.2017.01.048
173Hagoort, P. (2017). The neural basis for primary and acquired language skills. In Developmental perspectives in written language and literacy: In Honor of LudoVerhoeven (pp. 17-27). John Benjamins Publishing Company.https://doi.org/10.1075/z.206.02hag
174Hagoort, P. (2018). Prerequisites for an evolutionary stance on the neurobiology of language. Current opinion in behavioral sciences, 21, 191-194.https://doi.org/10.1016/j.cobeha.2018.05.012
175Hagoort, P. (2019). Introduction. In P. Hagoort (Ed.), Human language: From genes and brains to behavior (pp. 1-6). Cambridge, MA: MIT Press.
176Hagoort, P. (2019). The neurobiology of language beyond single word processing. Science, 366(6461), 55-58. DOI: 10.1126/science.aax0289https://doi.org/10.1126/science.aax0289
177Hagoort, P. (2020). Taal. In O. Van den Heuvel, Y. Van der Werf, B. Schmand, & B. Sabbe (Eds.), Leerboek neurowetenschappen voor de klinische psychiatrie (pp. 234-239). Amsterdam: Boom Uitgevers.
178Hagoort, P. (2020). The meaning-making mechanism (s) behind the eyes and between the ears. Philosophical Transactions of the Royal Society B, 375(1791), 20190301.https://doi.org/10.1098/rstb.2019.0301
179Hagoort, P. (2023). The language marker hypothesis. Cognition, 230, 105252.https://doi.org/10.1016/j.cognition.2022.105252
180Hagoort, P. (Ed.). (2019). Human language: From genes and brains to behavior. Cambridge, MA: MIT Press.
181Hagoort, P., & Beckmann, C. F. (2019). Key issues and future directions: The neural architecture for language. In: P. Hagoort (Ed.), Human Language: from Genes and Brains to Behavior (pp. 527-532). Cambridge, MA: The MIT Press.
182Hagoort, P., & Indefrey, P. (2014). The neurobiology of language beyond single words. Annual review of neuroscience, 37, 347-362.
183Heesen, R., Fröhlich, M., Sievers, C., Woensdregt, M., & Dingemanse, M. (2022). Coordinating social action: a primer for the cross-species investigation of communicative repair. Philosophical Transactions of the Royal Society B, 377(1859), 20210110.https://doi.org/10.1098/rstb.2021.0110
184Heidlmayr, K., Takashima, A., Hagoort, P., & Milivojevic, B. (2020). The neural correlates of schema-dependent representational geometries during naturalistic discourse: Text-based and experiential approaches. In the Twelfth Annual (Virtual) Meeting of the Society for the Neurobiology of Language (SNL 2020).http://hdl.handle.net/21.11116/0000-0007-4A19-B
185Heidlmayr, K., Weber, K., Takashima, A., & Hagoort, P. (2020). No title, no theme: The joined neural space between speakers and listeners during production and comprehension of multi-sentence discourse. cortex, 130, 111-126.https://doi.org/10.1016/j.cortex.2020.04.035
186Heilbron, M., Armeni, K., Schoffelen, J., Hagoort, P., & de Lange, F. P. (2022). A hierarchy of linguistic predictions during natural language comprehension. Proceedings of the National Academy of Sciences of the United States of America, 119(32): e2201968119.https://doi.org/10.1073/pnas.2201968119
187Heilbron, M., Ehinger, B., Hagoort, P., & De Lange, F. P. (2019). Tracking naturalistic linguistic predictions with deep neural language models. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience (pp. 424-427).
188Heilbron, M., Ehinger, B., Hagoort, P., & De Lange, F. P. (2019). Tracking naturalistic linguistic predictions with deep neural language models. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience (pp. 424-427).https://doi.org/10.34973/t894-sz74
189Heilbron, M., Richter, D., Ekman, M., Hagoort, P., & De Lange, F. P. (2020). Word contexts enhance the neural representation of individual letters in early visual cortex. Nature communications, 11(1), 1-11.https://doi.org/10.1038/s41467-019-13996-4
190Heim, F., Fisher, S. E., Scharff, C., Ten Cate, C., & Riebel, K. (2023). Effects of cortical FoxP1 knockdowns on learned song preference in female zebra finches. eneuro, 10(3).https://doi.org/10.1523/ENEURO.0328-22.2023
191Heim, F., Scharff, C., Fisher, S. E., Riebel, K., & Ten Cate, C. (2024). Auditory discrimination learning and acoustic cue weighing in female zebra finches with localized FoxP1 knockdowns. Journal of Neurophysiology, 131(5), 950-963.https://doi.org/10.1152/jn.00228.2023
192Henke, L., Lewis, A. G., & Meyer, L. (2023). Fast and slow rhythms of naturalistic reading revealed by combined eye-tracking and electroencephalography. Journal of Neuroscience, 43(24), 4461-4469.https://doi.org/10.1523/JNEUROSCI.1849-22.2023
193Heyselaar, E., Peeters, D., & Hagoort, P. (2020). Do we predict upcoming speech content in naturalistic environments?. Language, Cognition and Neuroscience, 1-22.https://doi.org/10.1080/23273798.2020.1859568
194Hintz, F., Dijkhuis, M., van‘t Hoff, V., McQueen, J. M., & Meyer, A. S. (2020). A behavioural dataset for studying individual differences in language skills. Scientific data, 7(1), 1-18.https://doi.org/10.1038/s41597-020-00758-x
195Hintz, F., Jongman, S. R., Dijkhuis, M., van’t Hoff, V., McQueen, J. M., & Meyer, A. S. (2019). Shared lexical access processes in speaking and listening? An individual differences study. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/xlm0000768https://doi.org/10.1037/xlm0000768
196Hintz, F., Meyer, A. S., & Huettig, F. (2017). Predictors of verb-mediated anticipatory eye movements in the visual world. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(9), 1352.http://dx.doi.org/10.1037/xlm0000388
197Hintz, F., Meyer, A. S., & Huettig, F. (2020). Activating words beyond the unfolding sentence: Contributions of event simulation and word associations to discourse reading. Neuropsychologia, 141, 107409.https://doi.org/10.1016/j.neuropsychologia.2020.107409
198Hintz, F., Meyer, A. S., & Huettig, F. (2020). Visual context constrains language-mediated anticipatory eye movements. Quarterly Journal of Experimental Psychology, 73(3), 458-467.https://doi.org/10.1177/1747021819881615
199Hintz, F., Voeten, C. C., Isakoglou, C., McQueen, J. M., & Meyer, A. S. (2021, March). Individual differences in language ability: Quantifying the relationships between linguistic experience, general cognitive skills and linguistic processing skills. In the 34th Annual CUNY Conference on Human Sentence Processing (CUNY 2021).
200Hintz, F., Voeten, C. C., McQueen, J. M., & Meyer, A. S. (2022). Quantifying the relationships between linguistic experience, general cognitive skills and linguistic processing skills. In J. Culbertson, A. Perfors, H. Rabagliati, & V. Ramenzoni (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society (CogSci 2022) (pp. 2491-2496). Toronto, Canada: Cognitive Science Society.
201Hintz, F., Voeten, C., & Scharenborg, O. (2022). Recognizing non-native spoken words in background noise increases lexical competition in the non-native and in the native language. Psychonomic Bulletin & Review. Advanced online publication.https://doi.org/10.3758/s13423-022-02233-7
202Hintz, F., McQueen, J. M., & Meyer, A. S. (2024). Using psychometric network analysis to examine the components of spoken word recognition. Journal of Cognition, 7(1).https://doi.org/10.5334/joc.340
203Hoedemaker, R. S., & Meyer, A. S. (2019). Planning and coordination of utterances in a joint naming task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(4), 732. https://doi.org/10.1037/xlm0000603https://doi.org/10.1037/xlm0000603
204Hollenstein, N., Barrett, M., & Beinborn, L. (2020, May). Towards best practices for leveraging human language processing signals for natural language processing. In Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources (pp. 15-27).
205Holler, J. (2022). Visual bodily signals as core devices for coordinating minds in interaction. Philosophical Transactions of the Royal Society B, 377(1859), 20210094.https://doi.org/10.1098/rstb.2021.0094
206Huizeling, E., Alday, P. M., Peeters, D., & Hagoort, P. (2021). Combining EEG and eye-tracking to investigate the prediction of upcoming speech in naturalistic virtual environments: a 3D visual world paradigm. In the 13th Annual Meeting of the Society for the Neurobiology of Language (SNL 2021 Virtual Edition).
207Huizeling, E., Peeters, D., & Hagoort, P. (2021). Prediction of upcoming speech under fluent and disfluent conditions: eye tracking evidence from immersive virtual reality. Language, Cognition and Neuroscience, 1-28.https://doi.org/10.1080/23273798.2021.1994621
208Huizeling, E., Alday, P. M., Peeters, D., & Hagoort, P. (2023). Combining EEG and 3D-eye-tracking to study the prediction of upcoming speech in naturalistic virtual environments: A proof of principle. Neuropsychologia, 191, 108730.https://doi.org/10.1016/j.neuropsychologia.2023.108730
209Hulten, A., Schoffelen, J.-M., Udden, J., Lam, N. H. L., & Hagoort, P. (2019). How the brain makes sense beyond the processing of single words – An MEG study. Neuroimage, 186, 586-594. https://doi.org/10.1016/j.neuroimage.2018.11.035https://doi.org/10.1016/j.neuroimage.2018.11.035
210Hupkes, D. (2020). Hierarchy and interpretability in neural models of language processing. AmsterdamInstitute for Logic, Language and Computation.https://hdl.handle.net/11245.1/2828b6a8-f616-453e-987f-f88288aa1f59
211Hupkes, D., & Zuidema, W. (2017). Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks. In Proceedings Workshop on Interpreting, Explaining and Visualizing Deep Learning (at NIPS2017).
212Hupkes, D., Bouwmeester, S., & Fernández, R. (2018). Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue. arXiv preprint arXiv:1808.09178.
213Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (2020). Compositionality decomposed: how do neural networks generalise?. Journal of Artificial Intelligence Research, 67, 757-795.
214Hupkes, D., Singh, A., Korrel, K., Kruszewski, G., & Bruni, E. (2018). Learning compositionally through attentive guidance. arXiv preprint arXiv:1805.09657.
215Hupkes, D., Singh, A.K., Korrel K., Kruszewski, G., & Bruni, E. (2019). Learning compositionally through attentive guidance. CICLing 2019. arXiv preprint arXiv:1805.09657.
216Hupkes, 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.
217Hupkes, 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.
218Hustá, C., Zheng, X., Papoutsi, C., & Piai, V. (2021). Electrophysiological signatures of conceptual and lexical retrieval from semantic memory. Neuropsychologia, 161, 107988.https://doi.org/10.1016/j.neuropsychologia.2021.107988
219Hustá, C., Zheng, X., Papoutsi, C., & Piai, V. (2021). Electrophysiological signatures of conceptual and lexical retrieval from semantic memory. Neuropsychologia, 161, 107988. [Dataset]https://doi.org/10.34973/rgme-ze16
220Janssen, N. (2020). Staying connected as we speak: Behavioral and tractography evidence from health and neurodegenerative disease (Doctoral dissertation, Radboud University Nijmegen).http://hdl.handle.net/2066/226804
221Janssen, N., Roelofs, A., Mangnus, M., Sierpowska, J., Kessels, R. P., & Piai, V. (2020). How the speed of word finding depends on ventral tract integrity in primary progressive aphasia. NeuroImage: Clinical, 28, 102450.https://doi.org/10.1016/j.nicl.2020.102450
222Janssen, N., Roelofs, A., van den Berg, E., Eikelboom, W. S., Holleman, M. A., in de Braek, D. M., … & Kessels, R. P. (2022). The Diagnostic Value of Language Screening in Primary Progressive Aphasia: Validation and Application of the Sydney Language Battery. Journal of Speech, Language, and Hearing Research, 65(1), 200-214.
223Jongman, S. R., Khoe, Y. H., & Hintz, F. (2020). Vocabulary size influences spontaneous speech in native language users: Validating the use of automatic speech recognition in individual differences research. Language and speech, 0023830920911079.https://doi.org/10.1177/0023830920911079
224Jongman, S. R., Khoe, Y. H., & Hintz, F. (2021). Vocabulary size influences spontaneous speech in native language users: Validating the use of automatic speech recognition in individual differences research. Language and Speech, 64(1), 35-51.https://doi.org/10.1177/0023830920911079
225Jongman, S. R., Piai, V., & Meyer, A. S. (2019). Planning for language production: the electrophysiological signature of attention to the cue to speak. Language, Cognition and Neuroscience, 1-18. https://doi.org/10.1080/23273798.2019.1690153https://doi.org/10.1080/23273798.2019.1690153
226Jongman, S. R., Roelofs, A., & Lewis, A. G. (2020). Attention for speaking: Prestimulus motor-cortical alpha power predicts picture naming latencies. Journal of cognitive neuroscience, 32(5), 747-761.https://doi.org/10.1162/jocn_a_01513
227Jumelet, J., & Hupkes, D. (2018). Do language models understand anything? on the ability of lstms to understand negative polarity items. arXiv preprint arXiv:1808.10627.
228Jumelet, J., Zuidema, W., & Hupkes, D. (2019). Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. CoNLL2019. arXiv preprint arXiv:1909.08975
229Kapteijns, B., & Hintz, F. (2021). Comparing predictors of sentence self-paced reading times: Syntactic complexity versus transitional probability metrics. Plos one, 16(7), e0254546.https://doi.org/10.1371/journal.pone.0254546
230Karaminis, T., Hintz, F., & Scharenborg, O. (2022). The Presence of Background Noise Extends the Competitor Space in Native and Non‐Native Spoken‐Word Recognition: Insights from Computational Modeling. Cognitive Science, 46(2), e13110.https://doi.org/10.1371/journal.pone.0254546
231Khoe, Y. H., Kootstra, G. J., Schoonen, R., & Frank, S. L. (2021). Simulating proficiency and exposure effects on cross-language structural priming in simultaneous bilinguals.
232Khoe, Y.H., Tsoukala, C., Kootstra, G.J., & Frank, S.L. (2020). Modeling cross-language structural priming in sentence production. In: T.C. Stewart (Ed.), Proceedings of the 18th Annual Meeting of the International Conference on Cognitive Modelling (pp. 131-137)
233Koch, S. B., Tyborowska, A., Niermann, H. C., Cillessen, A. H., Roelofs, K., Bašnáková, J., Toni, I. & Stolk, A. (2023). Integrating stereotypes and factual evidence in interpersonal communication. bioRxiv, 2023-05.https://doi.org/10.1101/2023.05.23.540979
234Kochari, A. (2019). Conducting web-based experiments for numerical cognition research. Journal of Cognition, 2(1), 39, 1-21. Doi: 10.5334/joc.85https://doi.org/10.5334/joc.85
235Kochari, A. (2020). Processing symbolic magnitude information conveyed by number words and by scalar adjectives.
236Kochari, A. (2020). Perceiving and communicating magnitudes: Behavioral and electrophysiological studies. AmsterdamInstitute for Logic, Language and Computation.https://hdl.handle.net/11245.1/ffeb87aa-7aea-491d-bfba-9664b8b35449
237Kochari, A. R., & Schriefers, H. (2021). Processing symbolic magnitude information conveyed by number words and by scalar adjectives. Quarterly Journal of Experimental Psychology, 17470218211031158.https://doi.org/10.1177/17470218211031158
238Kochari, A. R., Lewis, A. G., Schoffelen, J. M., & Schriefers, H. (2021). Semantic and syntactic composition of minimal adjective-noun phrases in Dutch: An MEG study. Neuropsychologia, 155, 107754.https://doi.org/10.1016/j.neuropsychologia.2021.107754
239Kochari, A. R., Lewis, A. G., Schoffelen, J. M., & Schriefers, H. (2021). Semantic and syntactic composition of minimal adjective-noun phrases in Dutch: an MEG study. Neuropsychologia, 155, 107754.https://doi.org/10.1016/j.neuropsychologia.2021.107754
240Kochari, A. R., Lewis, A. G., Schoffelen, J. M., & Schriefers, H. J. (2021). Disentangling syntactic and semantic components in adjective-noun phrase composition: an MEG study [dataset].https://doi.org/10.34973/0c20-0j33
241Kochari, A., & Flecken, M. (2019). Lexical prediction in language comprehension: a replication study of grammatical gender effects in Dutch. Language, Cognition and Neuroscience, 34(2), 239-253. Doi: 10.1080/23273798.2018.1524500https://doi.org/10.1080/23273798.2018.1524500
242Kochari, A., Lewis, A. G., Schoffelen, J.-M., & Schriefers, H. (2021, January 19). Semantic and syntactic composition of minimal adjective-noun phrases in Dutch: an MEG study.https://doi.org/10.17605/OSF.IO/KYC4U
243Kochari, A., Van Rooij, R., & Schulz, K. (2020). Generics and Alternatives. Frontiers in Psychology, 11, 1274.https://doi.org/10.3389/fpsyg.2020.01274
244Korrel, K., Hupkes, D., Dankers, V., & Bruni, E. (2019). Transcoding compositionally: using attention to find more generalizable solutions. BlackboxNLP, ACL 2019. arXiv preprint arXiv:1906.01234.
245Kösem, A., Bosker, H. R., Jensen, O., Hagoort, P., & Riecke, L. (2020). Biasing the perception of spoken words with Transcranial Alternating Current Stimulation. Journal of Cognitive Neuroscience, 32(8), 1428-1437.https://doi.org/10.1162/jocn_a_01579
246Kösem, A., Bosker, H. R., Takashima, A., Meyer, A. S., Jensen, O., & Hagoort, P. (2018). Neural entrainment determines the words we hear. Current Biology,28, 2867-2875. doi:10.1016/j.cub.2018.07.023.https://doi.org/10.1016/j.cub.2018.07.023
247Kösem, A., Dai, B., McQueen, J. M., & Hagoort, P. (2023). Neural tracking of speech envelope does not unequivocally reflect intelligibility. NeuroImage, 272, 120040.https://doi.org/10.1016/j.neuroimage.2023.120040
248Krutwig, J., Sadakata, M., Garcia-Cossio, E., Desain, P., & McQueen, J. M. (2017). Perception and production interactions in non-native speech category learning: Between neural and behavioural signatures. In Psycholinguistics in Flanders (PiF 2017).http://hdl.handle.net/21.11116/0000-0004-921F-5
249Levinson, S. C. (2016). Turn-taking in human communication–origins and implications for language processing. Trends in cognitive sciences, 20(1), 6-14.
250Levinson, S. C., & Holler, J. (2014). The origin of human multi-modal communication. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1651), 20130302.
251Levshina, N. (2020). Communicative efficiency and differential case marking: A reverse-engineering approach. Linguistics Vanguard.http://hdl.handle.net/21.11116/0000-0007-09ED-5
252Levshina, N. (2020). Conditional inference trees and random forests. In Practical Handbook of Corpus Linguistics. Springer.http://hdl.handle.net/21.11116/0000-0007-09E6-C
253Levshina, N. (2020). Database of Annotated Core Arguments: English, Lao and Russian (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4065523
254Levshina, N. (2020). Efficient trade-offs as explanations in functional linguistics: some problems and an alternative proposal. Revista da ABRALIN 19(3): 50-78.http://hdl.handle.net/21.11116/0000-0007-906C-D
255Levshina, N. (2020). How tight is your language? A semantic typology based on Mutual Information. In Proceedings of the 19th Workshop on Treebanks and Linguistic Theories (pp. 70-78).  https://www.aclweb.org/anthology/2020.tlt-1.7.pdf
256Levshina, N. (2021). Communicative efficiency and differential case marking: a reverse-engineering approach. Linguistics Vanguard, 7(s3).
257Levshina, N. (2021). Corpus-based typology: Applications, challenges and some solutions. Linguistic Typology.http://hdl.handle.net/21.11116/0000-0008-1575-D
258Levshina, N. (2021). Cross-Linguistic Trade-Offs and Causal Relationships Between Cues to Grammatical Subject and Object, and the Problem of Efficiency-Related Explanations. Frontiers in Psychology, 12, 2791.https://doi.org/10.3389/fpsyg.2021.648200
259Levshina, N. (2022). Comparing Bayesian and frequentist models of language variation: The case of help + (to) Infinitive. In O. Schützler, & J. Schlüter (Eds.), Data and methods in corpus linguistics – Comparative Approaches (pp. 224-258). Cambridge: Cambridge University Press.https://doi.org/10.3389/fpsyg.2021.648200
260Levshina, N. (2022). Corpus-based typology: Applications, challenges and some solutions. Linguistic Typology, 26(1), 129-160. doi:10.1515/lingty-2020-0118.
261Levshina, N. (2022). Frequency, informativity and word length: Insights from typologically diverse corpora. Entropy, 24(2), 280.https://doi.org/10.3390/e24020280
262Levshina, N. (2022). Semantic maps of causation: New hybrid approaches based on corpora and grammar descriptions. Zeitschrift für Sprachwissenschaft, 41(1), 179-205. doi:10.1515/zfs-2021-2043.
263Levshina, N. (2022). Testing communicative and learning biases in a causal model of language evolution: A study of cues to Subject and Object. Proceedings of the 23rd Amsterdam Colloquium, 383-387.https://doi.org/10.3390/e24020280
264Levshina, N. (2023). Communicative efficiency: Language structure and use. Cambridge: Cambridge University Press.https://doi.org/10.1515/zfs-2021-2043
265Levshina, N., & Hawkins, J. A. (2022). Verb-argument lability and its correlations with other typological parameters. A quantitative corpus-based study. Linguistic Typology at the Crossroads, 2(1), 94-120. doi:10.6092/issn.2785-0943/13861.https://doi.org/10.6092/issn.2785-0943/13861
266Levshina, N., & Lorenz, D. (2022). Communicative efficiency and the Principle of No Synonymy: Predictability effects and the variation of want to and wanna. Language and Cognition, 14(2), 249-274. doi:10.1017/langcog.2022.7.
267Levshina, N., & Moran, S. (2021). Efficiency in human languages: Corpus evidence for universal principles. Linguistics Vanguard, 7(s3).https://doi.org/10.1515/lingvan-2020-0081
268Levshina, N., & Moran, S. (2021). Efficiency in human languages: Corpus evidence for universal principles. Linguistics Vanguard, 7(s3).http://hdl.handle.net/21.11116/0000-0007-09F0-0
269Lewis, A. G. (2020). Balancing exogenous and endogenous cortical rhythms for speech and language requires a lot of entraining: a commentary on Meyer, Sun & Martin (2020). Language, Cognition and Neuroscience, 35(9), 1133-1137.https://doi.org/10.1080/23273798.2020.1734640
270Lewis, A. G., Schoffelen, J. M., Bastiaansen, M., & Schriefers, H. (2023). Is beta in agreement with the relatives? Using relative clause sentences to investigate MEG beta power dynamics during sentence comprehension. Psychophysiology, 60(10), e14332.https://doi.org/10.1111/psyp.14332
271Liu, R., Bögels, S., Bird, G., Medendorp, P., & Toni, I. (2019). Integrating visuospatial perspective-taking and communicative demands in sensorimotor control of referential pointing.. PsyArXiv.
272Liu, R., Bögels, S., Bird, G., Medendorp, W. P., & Toni, I. (2022). Hierarchical Integration of Communicative and Spatial Perspective‐Taking Demands in Sensorimotor Control of Referential Pointing. Cognitive Science, 46(1), e13084.https://doi.org/10.1080/23273798.2020.1734640
273Liu, R., Stolk, A., de Boer, M., Oostenveld, R. & Toni, I. (2021) Oxytocin facilitates communicative adjustment by upregulating broadband aperiodic neural activity. 12th Society for Social Neuroscience (S4SN) Annual Meeting.
274Liu, Y., Hintz, F., Liang, J., & Huettig, F. (2022). Prediction in challenging situations: Most bilinguals can predict upcoming semantically-related words in their L1 source language when interpreting. Bilingualism: Language and Cognition, 25(5), 801-815.
275Lopopolo, A. (2021). Properties, structures and operations: Studies on language processing in the brain using computational linguistics and naturalistic stimuli (Doctoral dissertation, Radboud University Nijmegen Nijmegen).
276Lopopolo, A., & Rabovsky, M. (2021). Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
277Lopopolo, A., Frank, S. L., van den Bosch, A., & Willems, R. (2019, June). Dependency parsing with your eyes: Dependency structure predicts eye regressions during reading. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 77-85).
278Lopopolo, A., Frank, S. L., Van den Bosch, A., & Willems, R. M. (2017). Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain. PloS one, 12(5), e0177794.https://doi.org/10.1371/journal.pone.0177794
279Lopopolo, A., Frank, S.L., Van den Bosch, A., Nijhof, A., & Willems, R.M. (2018). The Narrative Brain Dataset: an fMRI dataset for the study of natural language processing in the brain. In B. Devereux, E. Shutova, & C.-R. Huang (Eds.), Proceedings of LREC 2018 Workshop “Linguistic and Neuro-Cognitive Resources (LiNCR)” (pp. 8-11).http://hdl.handle.net/2066/193726
280Lopopolo, A., van den Bosch, A., Petersson, K. M., & Willems, R. M. (2021). Distinguishing syntactic operations in the brain: Dependency and phrase-structure parsing. Neurobiology of Language, 2(1), 152-175.https://doi.org/10.1162/nol_a_00029
281Marquand, A. F., Haak, K. V., & Beckmann, C. F. (2017). Functional corticostriatal connection topographies predict goal-directed behaviour in humans. Nature human behaviour, 1(8), 1-9.https://doi.org/10.1038/s41562-017-0146
282Marquand, A. F., Kia, S. M., Zabihi, M., Wolfers, T., Buitelaar, J. K., & Beckmann, C. F. (2019). Conceptualizing mental disorders as deviations from normative functioning. Molecular psychiatry, 24(10), 1415-1424. https://doi.org/10.1038/s41380-019-0441-1https://doi.org/10.1038/s41380-019-0441-1
283Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016). Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biological psychiatry, 80(7), 552-561.
284Martin, A. E. & Baggio, G. (2019). Modelling meaning composition from formalism to mechanism. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1791), 20190298. https://doi.org/10.1098/rstb.2019.0298https://doi.org/10.1098/rstb.2019.0298
285Martin, A. E. (2020). A compositional neural architecture for language. Journal of Cognitive Neuroscience, 32(8), 1407-1427.https://doi.org/10.1162/jocn_a_01552
286Martin, A. E., & Doumas, L. A. (2019). Predicate learning in neural systems: using oscillations to discover latent structure. Current Opinion in Behavioral Sciences, 29, 77-83. https://doi.org/10.1016/j.cobeha.2019.04.008https://doi.org/10.1016/j.cobeha.2019.04.008
287Martin, A. E., & Doumas, L. A. (2020). Tensors and compositionality in neural systems. Philosophical Transactions of the Royal Society B, 375(1791), 20190306. https://doi.org/10.1098/rstb.2019.0306https://doi.org/10.1098/rstb.2019.0306
288Maslowski, M., Meyer, A. S., & Bosker, H. R. (2017). When slow speech sounds fast: How the speech rate of one talker influences perception of another talker. In the IPS workshop: Abstraction, Diversity, and Speech Dynamics.http://hdl.handle.net/11858/00-001M-0000-002D-4EED-1
289Maslowski, M., Meyer, A. S., & Bosker, H. R. (2017). Whether long-term tracking of speech rate affects perception depends on who is talking. In Proceedings of Interspeech 2017 (pp. 586-590). doi:10.21437/Interspeech.2017-1517.https://doi.org/10.21437/Interspeech.2017-1517.
290Maslowski, M., Meyer, A. S., & Bosker, H. R. (2017). Whether long-term tracking of speech rate affects perception depends on who is talking. In Interspeech 2017 (pp. 586-590).https://doi.org/10.21437/Interspeech.2017-1517
291Maslowski, M., Meyer, A. S., & Bosker, H. R. (2019). How the tracking of habitual rate influences speech perception. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(1), 128.https://doi.org/10.1037/xlm0000579
292Maslowski, M., Meyer, A. S., & Bosker, H. R. (2019). Listeners normalize speech for contextual speech rate even without an explicit recognition task. The Journal of the Acoustical Society of America, 146(1), 179-188. https://doi.org/10.1121/1.5116004https://doi.org/10.1121/1.5116004
293Maslowski, M., Meyer, A. S., & Bosker, H. R. (2020). Eye-tracking the time course of distal and global speech rate effects. Ihttps://doi.org/10.1037/xhp0000838
294McQueen, J. M., Eisner, F., & Norris, D. (2016). When brain regions talk to each other during speech processing, what are they talking about? Commentary on Gow and Olson (2015). Language, Cognition and Neuroscience, 31(7), 860-863.
294McQueen, J. M., Eisner, F., Burgering, M. A., & Vroomen, J. (2020). Specialized memory systems for learning spoken words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(1), 189.https://doi.org/10.1037/xlm0000704
295McQueen, J. M., Krutwig, J., Jager, L., Desain, P., Witteman, J., & Schiller, N. O. (2018). Learning foreign-language sounds in adulthood: Listening, speaking, and individual differences. The Journal of the Acoustical Society of America, 144(3), 1716-1716.https://doi.org/10.1121/1.5067608
296McQueen, J.M., & Meyer, A.S. (2019). Key issues and future directions: Towards a comprehensive cognitive architecture for language use. In P. Hagoort (Ed.), Language in Interaction: The human faculty from genes to behaviour (pp. 85-96). Cambridge, MA: MIT Press.
297Menks, W. M., Ekerdt, C., Janzen, G., Kidd, E., Lemhöfer, K., Fernández, G., & McQueen, J. M. (2022). Study protocol: A comprehensive multi-method neuroimaging approach to disentangle developmental effects and individual differences in second language learning. BMC Psychology, 10: 169.
298Menks, W. M., Ekerdt, C., Lemhöfer, K., Kidd, E., Fernández, G., McQueen, J. M., & Janzen, G. (2024). Developmental changes in brain activation during novel grammar learning in 8-25-year-olds. Developmental Cognitive Neuroscience, 66, 101347.https://doi.org/10.1016/j.dcn.2024.101347
299Merkx, D., & Frank, S. L. (2019). Learning semantic sentence representations from visually grounded language without lexical knowledge. Natural Language Engineering, 25(4), 451-466. https://doi.org/10.1017/S1351324919000196https://doi.org/10.1017/S1351324919000196
300Merkx, D., & Frank, S. L. (2020). Human Sentence Processing: Recurrence or Attention?. arXiv preprint arXiv:2005.09471.https://doi.org/10.48550/arXiv.2005.09471
301Merkx, D., Frank, S. L., & Ernestus, M. (2021). Semantic sentence similarity: size does not always matter. arXiv preprint arXiv:2106.08648.https://doi.org/10.48550/arXiv.2106.08648
302Merkx, D., Frank, S.L., & Ernestus, M. (2019). Language learning using speech to image retrieval. In Proceedings of Interspeech 2019, 1841-1845.
303Merkx, D., Frank, S.L., & Ernestus, M. (2022). Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge. Proceedings of the Workshop on Cognitive Modeling and Computational Linguisticshttps://hdl.handle.net/2066/247459
304Merkx, D., Scholten, S., Frank, S.L. et al. (2023) Modelling Human Word Learning and Recognition Using Visually Grounded Speech. Cogn Comput 15, 272–288. https://doi.org/10.1007/s12559-022-10059-7
305Meyer, A. S., Roelofs, A., & Brehm, L. (2019). Thirty years of Speaking: An introduction to the special issue. Language, Cognition and Neuroscience, 34(9), 1073-1084.https://doi.org/10.1080/23273798.2019.1652763
306Milivojevic, B., Varadinov, M., Grabovetsky, A. V., Collin, S. H., & Doeller, C. F. (2016). Coding of event nodes and narrative context in the hippocampus. Journal of Neuroscience, 36(49), 12412-12424.
307Mishra, C., Verdonschot, R., Hagoort, P., & Skantze, G. (2023). Real-time emotion generation in human-robot dialogue using large language models. Frontiers in Robotics and AI, 10, 1271610.https://doi.org/10.3389/frobt.2023.1271610
308Mooijman, S., Schoonen, R., Roelofs, A., & Ruiter, M. B. (2021). Executive control in bilingual aphasia: a systematic review. Bilingualism: Language and Cognition, 1-16.https://doi.org/10.1017/S136672892100047X
309Muysken, P. (2016). Bilingual complexes: the perspective of the Gradient Symbolic Computation framework. Bilingualism, 19(5), 891.https://doi.org/10.1017/S1366728916000031
310Muysken, P. (2016). Creole languages. In Oxford Research Encyclopedia of Linguistics.https://doi.org/10.1093/acrefore/9780199384655.013.68
311Muysken, P. (2016). Fine tuning cross-linguistic interaction: The nuts and bolts. Cognitive Perspectives on Bilingualism, 17, 207.
312Muysken, P. (2016). From Colombo to Athens: Areal and universalist perspectives on bilingual compound verbs. Languages, 1(1), 2.https://doi.org/10.3390/languages1010002
313Muysken, P. (2016). Language contact: Trojan horse or new potential for cross-fertilization?. Linguistic Typology, 20(3), 537-545.https://doi.org/10.1515/lingty-2016-0025
314Neville, D. A., Fitz, H., & Shafto, M. (2021). AgeNet: A Neurobiological Model of Age-related Word Retrieval Deficits. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
315Neville, D. A., Raaijmakers, J. G., & van Maanen, L. (2019). Modulation of the word frequency effect in recognition memory after an unrelated lexical decision task. Journal of Memory and Language, 108, 104026. https://doi.org/10.1016/j.jml.2019.05.004https://doi.org/10.1016/j.jml.2019.05.004
316Noorman, S., Neville, D. A., & Simanova, I. (2018). Words affect visual perception by activating object shape representations. Scientific reports, 8(1), 1-10.https://doi.org/10.1038/s41598-018-32483-2
317Noorman, S., Neville, D. A., & Simanova, I. (2018). Words affect visual perception by activating object shape representations. Scientific Reports, 8(1), 14156https://doi.org/10.34973/0n8v-7d14
318Norris, D., McQueen, J. M., & Cutler, A. (2016). Prediction, Bayesian inference and feedback in speech recognition. Language, cognition and neuroscience, 31(1), 4-18.
319Özyürek, A. (2014). Hearing and seeing meaning in speech and gesture: insights from brain and behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1651), 20130296.
320Özyürek, A. (2017). Function and processing of gesture in the context of language. Why gesture, 39-58.
321Piai, V., & Zheng, X. (2019). Speaking waves: neuronal oscillations in language production. Psychology of Learning and Motivation, 71, 265-302. https://doi.org/10.1016/bs.plm.2019.07.002https://doi.org/10.1016/bs.plm.2019.07.002
322Piai, V., Anderson, K. L., Lin, J. J., Dewar, C., Parvizi, J., Dronkers, N. F., & Knight, R. T. (2016). Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proceedings of the National Academy of Sciences, 113(40), 11366-11371.
323Piai, V., De Witte, E., Sierpowska, J., Zheng, X., Hinkley, L. B., Mizuiri, D., … & Nagarajan, S. S. (2020). Language neuroplasticity in brain tumor patients revealed by magnetoencephalography. Journal of cognitive neuroscience, 32(8), 1497-1507.https://doi.org/10.1162/jocn_a_01561
324Piai, V., Klaus, J., & Rossetto, E. (2020). The lexical nature of alpha-beta oscillations in context-driven word production. Journal of Neurolinguistics, 55, 100905.https://doi.org/10.1016/j.jneuroling.2020.100905
325Piai, V., Prins, J. B., Verdonck-de Leeuw, I. M., Leemans, C. R., Terhaard, C. H., Langendijk, J. A., … & Kessels, R. P. (2019). Assessment of neurocognitive impairment and speech functioning before head and neck cancer treatment. JAMA Otolaryngology–Head & Neck Surgery, 145(3), 251-257. doi: 10.1001/jamaoto.2018.3981https://doi.org/10.1001/jamaoto.2018.3981
326Piai, V., Vos, S. H., Idelberger, R., Gans, P., Doorduin, J., & ter Laan, M. (2019). Awake surgery for a violin player: monitoring motor and music performance, a case report. Archives of Clinical Neuropsychology, 34(1), 132-137. https://doi.org/10.1093/arclin/acy009https://doi.org/10.1093/arclin/acy009
327Piai, V., & Eikelboom, D. (2023). Brain areas critical for picture naming: a systematic review and meta-analysis of lesion-symptom mapping studies. Neurobiology of Language, 4(2), 280-296.https://doi.org/10.1162/nol_a_00097
328Pouw, W., & Fuchs, S. (2022). Origins of vocal-entangled gesture. Neuroscience & Biobehavioral Reviews, 104836.https://doi.org/10.1016/j.jneuroling.2020.100905
329Pouw, W., & Holler, J. (2020). Timing in conversation is dynamically adjusted turn by turn: Evidence for lag-1 negatively autocorrelated turn taking times in telephone conversation.
330Pouw, W., & Holler, J. (2022). Timing in conversation is dynamically adjusted turn by turn in dyadic telephone conversations. Cognition, 222, 105015.https://doi.org/10.1093/arclin/acy009
331Pouw, W., de Wit, J., Bögels, S., Rasenberg, M., Milivojevic, B., & Ozyurek, A. (2021). Semantically related gestures move alike: Towards a distributional semantics of gesture kinematics.https://doi.org/10.31219/osf.io/pgq6m
332Pouw, W., Dingemanse, M., & Ozyurek, A. (2020). Gesture Network Analysis Reveals Structural Properties of Change in Evolving Languages in the Lab.
333Pouw, W., Dingemanse, M., Motamedi, Y., & Ozyurek, A. (2020). Multiscale kinematic analysis reveals structural properties of change in evolving manual languages in the lab.http://hdl.handle.net/21.11116/0000-0006-B338-1
334Pouw, W., Dingemanse, M., Motamedi, Y., & Özyürek, A. (2021). A systematic investigation of gesture kinematics in evolving manual languages in the lab. Cognitive science, 45(7), e13014.https://doi.org/10.1111/cogs.13014
335Pouw, W., Proksch, S., Drijvers, L., Gamba, M., Holler, J., Kello, C., … & Wiggins, G. A. (2021). Multilevel rhythms in multimodal communication. Philosophical Transactions of the Royal Society B, 376(1835), 20200334.https://doi.org/10.1098/rstb.2020.0334
336Przezdzik, I. (2021). Investigations into cognitive systems with resting-state functional connectivity: Connectopic mapping as a way to characterise the organisation underlying complex behaviour and abstract neural functions (Doctoral dissertation, sl: sn).https://hdl.handle.net/2066/233901
337Przezdzik, I. (2021). Investigations into cognitive systems with resting-state functional connectivity: Connectopic mapping as a way to characterise the organisation underlying complex behaviour and abstract neural functions (Doctoral dissertation, sl: sn).
338Przeździk, I., Faber, M., Fernandez, G., Beckmann, C. F., & Haak, K. V. (2019). The functional organisation of the hippocampus along its long axis is gradual and predicts recollection. Cortex, 119, 324-335. https://doi.org/10.1016/j.cortex.2019.04.015https://doi.org/10.1016/j.cortex.2019.04.015
339Przeździk, I., Faber, M., Fernández, G., Beckmann, C. F., & Haak, K. V. (2020). Gradient mapping in the human hippocampus: Reply to Poppenk. Cortex; a journal devoted to the study of the nervous system and behavior, 128, 318-321.https://doi.org/10.1016/j.cortex.2020.04.004
340Quaresima, A., Fitz, H., Duarte, R., van den Broek, D., Hagoort, P., and Petersson, K.M. (2022). The Tripod neuron: A minimal structural reduction of the dendritic tree. Journal of Physiology, 58https://hdl.handle.net/2066/233901
341Quaresima, A., Van den Broek, D., Fitz, H., Duarte, R., & Petersson, K. M. (2020). A minimal reduction of dendritic structure and its functional implication for sequence processing in biological neurons. In the Twelfth Annual (Virtual) Meeting of the Society for the Neurobiology of Language (SNL 2020).http://hdl.handle.net/21.11116/0000-0007-4A9F-4
342Quaresima, A., Fitz, H., Duarte, R., Hagoort, P., & Petersson, K. M. (2023). Dendrites support formation and reactivation of sequential memories through Hebbian plasticity. bioRxiv, 2023-09.https://doi.org/10.1101/2023.09.26.559322
343Rasenberg, M. (2023). Mutual understanding from a multimodal and interactional perspective
Radboud University Nijmegen – Handle: https://hdl.handle.net/2066/289721		https://hdl.handle.net/2066/289721
344Rasenberg, M., Özyürek, A., & Dingemanse, M. (2020). Alignment in multimodal interaction: An integrative framework. Cognitive Science, 44(11), e12911.https://doi.org/10.1111/cogs.12911
345Rasenberg, M., Özyürek, A., & Dingemanse, M. (2021). The use of multimodal resources for joint meaning-making in conversational repair sequences. Integrating Quantitative and Qualitative Methods in the Cognitive and Language Sciences, 15, 69.
346Rasenberg, M., Özyürek, A., Bögels, S., & Dingemanse, M. (2022). The primacy of multimodal alignment in converging on shared symbols for novel referents. Discourse Processes, 59(3), 209-236.https://doi.org/10.1080/0163853X.2021.1992235
347Rasenberg, M., Pouw, W., Özyürek, A., Dingemanse, M. (2022). The multimodal nature of communicative efficiency in social interaction. Sci Rep 12, 19111 . https://doi.org/10.1038/s41598-022-22883-w
348Rasenberg, M.E., Pouw, W.T.J.L., Özyürek, A., & Dingemanse, M (2022): The multimodal nature of communicative efficiency in social interaction. Version 1. Radboud University. (dataset). https://doi.org/10.1080/0163853X.2021.1992235
349Rasenberg, M., Rommers, J. & Bergen, G. van (2019). Anticipating predictability: an ERP investigation of expectation-managing discourse markers in dialogue comprehension. Language, Cognition and Neuroscience, 35(1), 1-16. doi: 10.1080/23273798.2019.1624789https://doi.org/10.1080/23273798.2019.1624789
350Repplinger, M., Beinborn, L. M., & Zuidema, W. H. (2018). Vector-space models of words and sentences. Nieuw Archief voor Wiskunde, 19(3), 167-174.
351Rich, P., Blokpoel, M., de Haan, R., & van Rooij, I. (2020). How intractability spans the cognitive and evolutionary levels of explanation. Topics in cognitive science, 12(4), 1382-1402.https://doi.org/10.1111/tops.12506
352Rich, P., Blokpoel, M., de Haan, R., Otworowska, M., Sweers, M., Wareham, T., & van Rooij, I. (2019). Naturalism, tractability and the adaptive toolbox. Synthese, 1-36. https://doi.org/10.1007/s11229-019-02431-2https://doi.org/10.1007/s11229-019-02431-2
353Rietbergen, M., Roelofs, A., Den Ouden, H., & Cools, R. (2018). Disentangling cognitive from motor control: Influence of response modality on updating, inhibiting, and shifting. Acta psychologica, 191, 124-130.https://doi.org/10.1016/j.actpsy.2018.09.008
354Rodd, J. (2020). How speaking fast is like running: Modelling control of speaking rate (Doctoral dissertation, Radboud University Nijmegen Nijmegen).https://hdl.handle.net/2066/222287
355Rodd, J., & Chen, A. (2016). Pitch accents show a perceptual magnet effect: Evidence of internal structure in intonation categories. In Speech Prosody 2016 (pp. 697-701).
356Rodd, J., Bosker, H. R., Ernestus, M., Alday, P. M., Meyer, A. S., & Ten Bosch, L. (2020). Control of speaking rate is achieved by switching between qualitatively distinct cognitive “gaits”: Evidence from simulation. Psychological review, 127(2), 281.https://doi.org/10.1037/rev0000172
357Rodd, J., Bosker, H. R., Ernestus, M., Meyer, A. S., & Ten Bosch, L. (2017). Simulating speaking rate control: A spreading activation model of syllable timing. In the Workshop Conversational speech and lexical representations.http://hdl.handle.net/11858/00-001M-0000-002D-571E-A
358Rodd, J., Bosker, H. R., Ernestus, M., Ten Bosch, L., & Meyer, A. S. (2017). How we regulate speech rate: phonetic evidence for a’gain strategy’in speech planning. In the Abstraction, Diversity and Speech Dynamics Workshop.
359Rodd, J., Bosker, H. R., Ten Bosch, L., & Ernestus, M. (2019). Deriving the onset and offset times of planning units from acoustic and articulatory measurements. The Journal of the Acoustical Society of America, 145(2), EL161-EL167. https://doi.org/10.1121/1.5089456https://doi.org/10.1121/1.5089456
360Rodd, J., Decuyper, C., Bosker, H. R., & Ten Bosch, L. (2020). A tool for efficient and accurate segmentation of speech data: announcing POnSS. Behavior Research Methods, 1-13.https://doi.org/10.3758/s13428-020-01449-6
361Rodd, J., Decuyper, C., Bosker, H. R., & Ten Bosch, L. (2021). A tool for efficient and accurate segmentation of speech data: announcing POnSS. Behavior Research Methods, 53(2), 744-756.https://doi.org/10.3758/s13428-020-01449-6
362Rodriguez-Fornells, A., León-Cabrera, P., Gabarros, A., & Sierpowska, J. (2021). Inner Speech Brain Mapping. Is It Possible to Map What We Cannot Observe?. In Intraoperative Mapping of Cognitive Networks (pp. 381-409). Springer, Cham.https://doi.org/10.1007/978-3-030-75071-8_23
363Roelofs, A. (2018). A unified computational account of cumulative semantic, semantic blocking, and semantic distractor effects in picture naming. Cognition, 172, 59-72.https://doi.org/10.1016/j.cognition.2017.12.007
364Roelofs, A. (2021). How attention controls naming: Lessons from Wundt 2.0. Journal of Experimental Psychology: General.https://doi.org/10.1037/xge0001030
365Roelofs, A. (2021). Phonological cueing of word finding in aphasia: insights from simulations of immediate and treatment effects. Aphasiology, 35(2), 169-185.https://doi.org/10.1080/02687038.2019.1686748
366Roelofs, A. (2021). Response competition better explains Stroop interference than does response exclusion. Psychonomic Bulletin & Review, 28(2), 487-493.https://doi.org/10.3758/s13423-020-01846-0
367Roelofs, A., & Piai, V. (2017). Distributional analysis of semantic interference in picture naming. Quarterly Journal of Experimental Psychology, 70(4), 782-792.https://doi.org/10.1080/17470218.2016.1165264
368Rojas Berscia, L. M. (2019). From Kawapanan to Shawi: Topics in language variation and change (Doctoral dissertation, Radboud University Nijmegen).http://hdl.handle.net/2066/204333
369Rojas-Berscia, L. M. (2015). Mayna, the lost Kawapanan language. LIAMES,15, 393-407. Retrieved from http://revistas.iel.unicamp.br/index.php/liames/article/view/4549.
370Rojas-Berscia, L. M. (2016). Fritz, Samuel (?). El vocabulario de la lengua xebera, una doctrina cristiana en xebero y quechua, y la gramática de la lengua xebera (siglo XVIII). Alexander-Bakkerus, Astrid (edición y estudio). Madrid/Frankfurt am Main: Iberoamericana Vervuert, 2016. 152 pp. Lexis, 40(2), 479-489.
371Rojas-Berscia, L. M. (2016). Lóxoro, traces of a contemporary Peruvian genderlect. Borealis–An International Journal of Hispanic Linguistics, 5(1), 157-170.https://doi.org/10.7557/1.5.1.3725
372Rojas-Berscia, L. M. (2019). Nominalization in Shawi (Chayahuita). Nominalization in the languages of the Americas, 491-514.
373Rojas-Berscia, L. M., & Bourdeau, C. (2017). Optional or syntactic ergativity in Shawi? Distribution and possible origins. Linguistic discovery, 15(1), 50-65.
374Rojas-Berscia, L. M., & Ghavami Dicker, S. (2015). Teonimia en el Alto Amazonas, el caso de Kanpunama. Escritura y Pensamiento, 18(36), 117-146.
375Rojas-Berscia, L. M., & Shi, J. A. (2017). Hakka as spoken in Suriname. In Boundaries and Bridges (pp. 179-196). De Gruyter Mouton.
376Rojas-Berscia, L. M., Napurí, A., & Wang, L. (2019). Shawi (Chayahuita). Journal of the International Phonetic Association, 1-14.
377Roos, N. M., & Piai, V. (2020). Across‐session consistency of context‐driven language processing: A magnetoencephalography study. European Journal of Neuroscience, 52(5), 3457-3469.https://doi.org/10.1111/ejn.14785
378Roos, N. M., Takashima, A., & Piai, V. (2023). Functional neuroanatomy of lexical access in contextually and visually guided spoken word production. cortex, 159, 254-267. https://doi.org/10.1016/j.cortex.2022.10.014
379Rowland, C. F., & Kidd, E. (2019). Key issues and future directions: How do children acquire language? In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 181-185). Cambridge, MA: MIT Press.
380Ruiter, M., Roelofs, A., Piai, V., Datadien, A., Steenbeek-Planting, E. G., van Engelen, R., & Hendriks, I. (2019). Stoppen met praten op de automatische piloot. Dixit, Tijdschrift over Taal- en Spraaktechnologie, Decembernummer, 34-35.https://hdl.handle.net/2066/218142
381Satizabal, C. L., Adams, H. H., Hibar, D. P., White, C. C., Knol, M. J., Stein, J. L., … & Smith, A. V. (2019). Genetic architecture of subcortical brain structures in 38,851 individuals. Nature genetics, 51(11), 1624-1636. https://doi.org/10.1038/s41588-019-0511-yhttps://doi.org/10.1038/s41588-019-0511-y
382Schijven, D., Soheili-Nezhad, S., Fisher, S. E., & Francks, C. (2024). Exome-wide analysis implicates rare protein-altering variants in human handedness. Nature Communications, 15(1), 2632.https://doi.org/10.1038/s41467-024-46277-w
383Schiller, N.O., & Witteman, J. (2016). Logically equivalent, functionally different: experimental evidence from the processing of non-negated and double-negated constructions. In Reuneker, A., R. Boogaart & S. Lensink (2016). Aries Netwerk: een constructicon (pp. 180-182). Leiden.
384Schoffelen, J.-M., Oostenveld, R., Lam, N. H. L., Udden, J., Hulten, A., & Hagoort, P. (2019). A 204-subject multimodal neuroimaging dataset to study language processing. Scientific Data, 6(1): 17. https://doi.org/10.1038/s41597-019-0020-yhttps://doi.org/10.1038/s41597-019-0020-y
385Schubotz, L., Drijvers, L., Holler, J., & Ozyurek, A. (2016). The cocktail party effect revisited in older and younger adults: When do iconic co-speech gestures help?. In the 8th Speech in Noise Workshop (SpiN 2016).
386Schubotz, L., Holler, J., Drijvers, L., & Özyürek, A. (2021). Aging and working memory modulate the ability to benefit from visible speech and iconic gestures during speech-in-noise comprehension. Psychological research, 85(5), 1997-2011.https://doi.org/10.1007/s00426-020-01363-8
387Seeliger, K., Ambrogioni, L., Güçlütürk, Y., van den Bulk, L. M., Güçlü, U., & van Gerven, M. A. J. (2021). End-to-end neural system identification with neural information flow. PLOS Computational Biology, 17(2), e1008558.https://doi.org/10.1371/journal.pcbi.1008558
388Seijdel, N., Schoffelen, J. M., Hagoort, P., & Drijvers, L. (2024). Attention drives visual processing and audiovisual integration during multimodal communication. Journal of Neuroscience, 44(10).https://doi.org/10.1523/JNEUROSCI.0870-23.2023
389Sharoh, D. L. (2020). Advances in layer specific fMRI for the study of language, cognition and directed brain networks (Doctoral dissertation, [Sl: sn]).https://hdl.handle.net/2066/214840
390Sharoh, D., Van Mourik, T., Bains, L. J., Segaert, K., Weber, K., Hagoort, P., & Norris, D. (2019). Laminar specific fMRI reveals directed interactions in distributed networks during language processing. In Proceedings of the National Academy of Sciences of the United States of America, 116(42), 21185-21190. https://doi.org/10.1073/pnas.1907858116https://doi.org/10.1073/pnas.1907858116
391Sharoh, D., Weber, K., Ruijters, L., Norris, D., & Hagoort, P. (2021). Compositional meaning influences the BOLD response in language critical cortex via interaction between LIFG and LMTG. In the 13th Annual Meeting of the Society for the Neurobiology of Language (SNL 2021 Virtual Edition).
392Shebani, Z., Carota, F., Hauk, O., Rowe, B. J., Tomasello, R., Pulvermüller, F. (2022). Brain correlates of action word memory revealed by fMRI. Sci. Reports 12:16053.https://doi.org/10.1371/journal.pcbi.1008558
393Sierpowska, J., Bryant, K. L., Janssen, N., Blazquez Freches, G., Römkens, M., Mangnus, M., Mars, R., & Piai, V. (2022). Comparing human and chimpanzee temporal lobe neuroanatomy reveals modifications to human language hubs beyond the frontotemporal arcuate fasciculus. Proceedings of the National Academy of Sciences, 119(28), e2118295119.https://hdl.handle.net/2066/214840
394Sierpowska, J., Gabarrós, A., Fernandez-Coello, A., Camins, A., Castaner, S., Juncadella, M., & Rodríguez-Fornells, A. (2019). Semantic processing and ventral pathways for language: intrasurgical and lesion-behavior mapping evidence. Neuroimage Clinical. https://doi.org/10.1016/j.nicl.2019.101704https://doi.org/10.1016/j.nicl.2019.101704
395Sierpowska, J., León-Cabrera, P., Camins, À., Juncadella, M., Gabarrós, A., & Rodríguez-Fornells, A. (2020). The black box of global aphasia: Neuroanatomical underpinnings of remission from acute global aphasia with preserved inner language function. Cortex, 130, 340-350.https://doi.org/10.1016/j.cortex.2020.06.009
396Sierpowska, J., Rofes, A., Dahlslätt, K., Mandonnet, E., ter Laan, M., Połczyńska, M., Hamer, P. D. W., Halaj, M., Spena, G., Meling, T. R., Motomura, K., Reyes, A. F., Campos, A. R., Robe, P. A., Zigiotto, L., Sarubbo, S., Freyschlag, C. F., Broen, M. P. G., Stranjalis, G., … Piai, V. (2022). The Aftercare Survey: Assessment and intervention practices after brain tumor surgery in Europe. Neuro-Oncology Practice, 9(4), 328-337. doi:10.1093/nop/npac029https://doi.org/10.1038/s41598-022-19416-w
397Simanova, I.,Francken, J. C., de Lange, F. P., & Bekkering, H. (2016). Linguistic priors shape categorical perception. Language, Cognition and Neuroscience, 31(1), 159-165.
398Slaats, S., Weissbart, H., Schoffelen, J. M., Meyer, A. S., & Martin, A. E. (2023). Delta-band neural responses to individual words are modulated by sentence processing. Journal of Neuroscience, 43(26), 4867-4883.https://doi.org/10.1523/JNEUROSCI.0964-22.2023
399Snijders Blok, C. (2021). Let the genes speak! De novo variants in developmental disorders with speech and language impairment (Doctoral dissertation, Radboud University Nijmegen).https://hdl.handle.net/2066/236675
400Snijders Blok, L., Goosen, Y. M., van Haaften, L., van Hulst, K., Fisher, S. E., Brunner, H. G., … & Kleefstra, T. (2021). Speech‐language profiles in the context of cognitive and adaptive functioning in SATB2‐associated syndrome. Genes, Brain and Behavior, 20(7), e12761.https://doi.org/10.1111/gbb.12761
401Snijders Blok, L., Kleefstra, T., Venselaar, H., Maas, S., Kroes, H. Y., Lachmeijer, A. M. A., Van Gassen, K. L. I., Firth, H. V., Tomkins, S., Bodek, S., The DDD Study, Õunap, K., Wojcik, M. H., Cunniff, C., Bergstrom, K., Powis, Z., Tang, S., Shinde, D. N., Au, C., Iglesias, A. D., Izumi, K. and 18 more (2019). De novo variants disturbing the transactivation capacity of POU3F3 cause a characteristic neurodevelopmental disorder. The American Journal of Human Genetics, 105(2), 403-412. https://doi.org/10.1016/j.ajhg.2019.06.007https://doi.org/10.1016/j.ajhg.2019.06.007
402Snijders Blok, L., Rousseau, J., Twist, J., Ehresmann, S., Takaku, M., Venselaar, H., … & Steeves, M. A. (2018). CHD3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language. Nature communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-06014-6https://doi.org/10.1038/s41467-018-06014-6
403Snijders Blok, L., Rousseau, J., Twist, J., Ehresmann, S., Takaku, M., Venselaar, H., … & Steeves, M. A. (2019). Author Correction: CHD3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language. Nature Communications, 10(1).https://doi.org/10.1038/s41467-019-10161-9
404Snijders Blok, L., Vino, A., Den Hoed, J., Underhill, H. R., Monteil, D., Li, H., … & Fisher, S. E. (2021). Heterozygous variants that disturb the transcriptional repressor activity of FOXP4 cause a developmental disorder with speech/language delays and multiple congenital abnormalities. Genetics in Medicine, 23(3), 534-542.https://doi.org/10.1038/s41436-020-01016-6
405Soheili-Nezhad, S., Schijven, D., Mars, R. B., Fisher, S. E., & Francks, C. (2024). Distinct impact modes of polygenic disposition to dyslexia in the adult brain. medRxiv, 2024-02.https://doi.org/10.1101/2024.02.14.24302815
406Sommer, N., & Levshina, N. (2021). Cross-linguistic differential and optional marking database. Version 1.0.0. https://zenodo.org/record/4896007#.YdLhI2iZOUk
407Stolk, A., Bašnáková, J., & Toni, I. (2022). Joint epistemic engineering: The neglected process in human communication. In The Routledge Handbook of Semiosis and the Brain (pp. 259-278). Routledge.https://doi.org/10.4324/9781003051817-21
408Stolk, A., D’Imperio, D., Di Pellegrino, G., & Toni, I. (2015). Altered communicative decisions following ventromedial prefrontal lesions. Current Biology, 25(11), 1469-1474.
409Stolk, A., Noordzij, M. L., Verhagen, L., Volman, I., Schoffelen, J. M., Oostenveld, R., … & Toni, I. (2014). Cerebral coherence between communicators marks the emergence of meaning. Proceedings of the National Academy of Sciences, 111(51), 18183-18188.
410Stolk, A., Verhagen, L., & Toni, I. (2016). Conceptual alignment: How brains achieve mutual understanding. Trends in cognitive sciences, 20(3), 180-191.
411Strauß, A., Wu, T., McQueen, J. M., Scharenborg, O., & Hintz, F.* (2022). The differential roles of lexical and sublexical processing during spoken-word recognition in clear and in noise. Cortex, 151, 70-88.https://doi.org/10.1016/j.cortex.2022.02.011
412Szymanik, J. (2016). Computing simple quantifiers. In Quantifiers and Cognition: Logical and Computational Perspectives (pp. 41-49). Springer, Cham.
413Szymanik, J. (2016). Quantifiers and cognition: Logical and computational perspectives (Vol. 96). Cham: Springer.
414Szymanik, J., & Thorne, C. (2017). Exploring the relation between semantic complexity and quantifier distribution in large corpora. Language Sciences, 60, 80-93.https://doi.org/10.1016/j.langsci.2017.01.006
415ten Cate, C. (2014). On the phonetic and syntactic processing abilities of birds: From songs to speech and artificial grammars. Current Opinion in Neurobiology, 28, 157-164.
416Theves, S. (2017). Accumulation and updating of hierarchical knowledge in hippocampus and PFC. In Internal Symposium.http://hdl.handle.net/21.11116/0000-0006-F9AF-D
417Theves, S. (2020). Mapping conceptual knowledge in the hippocampal system. (Doctoral dissertation, [Sl: sn]).https://hdl.handle.net/2066/219703
418Theves, S., Fernandez, G., & Doeller, C. F. (2019). The hippocampus encodes distances in multidimensional feature space. Current Biology, 29(7), 1226-1231. https://doi.org/10.1016/j.cub.2019.02.035https://doi.org/10.1016/j.cub.2019.02.035
419Theves, S., Fernández, G., & Doeller, C. F. (2020). The hippocampus maps concept space, not feature space. Journal of Neuroscience, 40(38), 7318-7325.https://doi.org/10.1523/JNEUROSCI.0494-20.2020
420Theves, S., Neville, D. A., Fernández, G., & Doeller, C. F. (2021). Learning and representation of hierarchical concepts in hippocampus and prefrontal cortex. Journal of Neuroscience, 41(36), 7675-7686.https://doi.org/10.1523/JNEUROSCI.0657-21.2021
421Thorin, J. (2020). Can you hear what you cannot say? The interactions of speech perception and production during non-native phoneme learning (Doctoral dissertation, [Sl: sn]).https://hdl.handle.net/2066/216199
422Thorin, J., Sadakata, M., Desain, P., & McQueen, J. M. (2018). Perception and production in interaction during non-native speech category learning. The Journal of the Acoustical Society of America, 144(1), 92-103.https://doi.org/10.1121/1.5044415
423Thothathiri, M., Bašnáková, J., Lewis, A. G., & Briand, J. M. (2024). Fractionating difficulty during sentence comprehension using functional neuroimaging. Cerebral Cortex, 34(2), bhae032.https://doi.org/10.1093/cercor/bhae032
424Todorova, L. (2021). Language bias in visually driven decisions: Computational and neurophysiological mechanisms (Doctoral dissertation, [Sl: sn]).https://hdl.handle.net/2066/229753
425Todorova, L., & Neville, D. A. (2019). Perception or control? Mechanisms of gender bias introduced by labels and associative words.
426Todorova, L., & Neville, D. A. (2020). Associative and identity words promote the speed of visual categorization: a hierarchical drift diffusion account. Frontiers in psychology, 11, 955.https://doi.org/10.3389/fpsyg.2020.00955
427Todorova, L., Neville, D. A., & Piai, V. (2020). Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective. Neuropsychologia, 146, 107560.https://doi.org/10.1016/j.neuropsychologia.2020.107560
428Todorova, L., Neville, D., & Piai, V. (2019). Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective.
429Torreira, F., & Bögels, S. (2022). Vocal reaction times to speech offsets: Implications for processing models of conversational turn-taking. Journal of Phonetics, 94, 101175.https://doi.org/10.1016/j.wocn.2022.101175
430Trujillo, J. P. (2016). The Kinematic Correlates of Signaling Communicative Intent in Action and Pantomime. In the 10th Annual Rovereto Workshop on Concepts, Actions and Objects (CAOS)-Functional and Neuronal Perspectives.
431Trujillo, J. P. (2017). Kinect and gesture research: a validation study of automatic feature coding. In the International Conference on Multimodal Communication (ICMC) 2017: Developing New Theories and Methods.http://hdl.handle.net/21.11116/0000-0002-85C9-5
432Trujillo, J. P. (2017). Space, Time and Gaze: How kinematic modulation and eye-gaze optimize communicative interaction. In the Language as a form of Action Conference.http://hdl.handle.net/21.11116/0000-0002-85CD-1
433Trujillo, J. P. (2020). Movement speaks for itself: The kinematic and neural dynamics of communicative action and gesture (Doctoral dissertation, Radboud University Nijmegen Nijmegen).https://hdl.handle.net/2066/215079
434Trujillo, J. P., Levinson, S. C., & Holler, J. (2021, July). Visual Information in Computer-Mediated Interaction Matters: Investigating the Association Between the Availability of Gesture and Turn Transition Timing in Conversation. In International Conference on Human-Computer Interaction (pp. 643-657). Springer, Cham.https://doi.org/10.1007/978-3-030-78468-3_44
435Trujillo, J. P., Özyürek, A., Kan, C. C., Sheftel‐Simanova, I., & Bekkering, H. (2021). Differences in the production and perception of communicative kinematics in autism. Autism Research, 14(12), 2640-2653.https://doi.org/10.1002/aur.2611
436Trujillo, J. P., Simanova, I., Bekkering, H., & Özyürek, A. (2018). Communicative intent modulates production and comprehension of actions and gestures: A Kinect study. Cognition, 180, 38-51.https://doi.org/10.1016/j.cognition.2018.04.003
437Trujillo, J. P., Simanova, I., Bekkering, H., & Özyürek, A. (2019). The communicative advantage: how kinematic signaling supports semantic comprehension. Psychological research, 1-15. https://doi.org/10.1007/s00426-019-01198-yhttps://doi.org/10.1007/s00426-019-01198-y
438Trujillo, J. P., Simanova, I., Özyürek, A., & Bekkering, H. (2020). Seeing the unexpected: How brains read communicative intent through kinematics. Cerebral Cortex, 30(3), 1056-1067.https://doi.org/10.1093/cercor/bhz148
439Trujillo, J. P., Vaitonyte, J., Simanova, I., & Ozyurek, A. (2019). Toward the markerless and automatic analysis of kinematic features: A toolkit for gesture and movement research. Behavior Research Methods, 51(2), 769-777. doi:10.3758/s13428-018-1086-8. https://doi.org/10.3758/s13428-018-1086-8
 		https://doi.org/10.3758/s13428-018-1086-8
440Trujillo, J., Özyürek, A., Holler, J., & Drijvers, L. (2020). Evidence for a Multimodal Lombard Effect: Speakers modulate not only speech but also gesture to overcome noise.
441Trujillo, J., Özyürek, A., Holler, J., & Drijvers, L. (2021). Speakers exhibit a multimodal Lombard effect in noise. Scientific reports, 11(1), 1-12.https://doi.org/10.1038/s41598-021-95791-0
442Tsoukala, C. (2021). Bilingual sentence production and code-switching: Neural network simulations (Doctoral dissertation, Nijmegen:[Sl: sn]).https://hdl.handle.net/2066/232236
443Tsoukala, C., Broersma, M., van den Bosch, A., & Frank, S. L. (2021). Simulating Code-switching Using a Neural Network Model of Bilingual Sentence Production. Computational Brain & Behavior, 4(1), 87-100.https://doi.org/10.1007/s42113-020-00088-6
444Tsoukala, C., Frank, S. L., & Broersma, M. (2017). “He’s pregnant”: Simulating the confusing case of gender pronoun errors in L2 English. In the 39th annual meeting of the cognitive science society (cogsci 2017) (pp. 3392-3397). Cognitive Science Society.https://hdl.handle.net/2066/178590
445Tsoukala, C., Frank, S. L., Van Den Bosch, A., Kroff, J. V., & Broersma, M. (2020). Modeling the auxiliary phrase asymmetry in code-switched Spanish–English. Bilingualism: Language and Cognition, 1-10.https://doi.org/10.1017/S1366728920000449
446Tsoukala, C., Frank, S. L., Van Den Bosch, A., Kroff, J. V., & Broersma, M. (2021). Modeling the auxiliary phrase asymmetry in code-switched Spanish–English. Bilingualism: Language and Cognition, 24(2), 271-280.https://doi.org/10.1017/S1366728920000449
447Tsoukala, C., Frank, S.L., Van den Bosch, A., Valdés Kroff, J., & Broersma, M. (2019). Simulating Spanish-English code-switching: El modelo está generating code switches. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 20-29). Minneapolis, Minnesota: Association for Computational Linguistics.https://hdl.handle.net/2066/213711
448Udden, J., Hulten, A., Schoffelen, J., Lam, N. H. L., Harbusch, K., Van den Bosch, A., Kempen, G., Petersson, K. M., & Hagoort, P. (2022). Supramodal sentence processing in the human brain: fMRI evidence for the influence of syntactic complexity in more than 200 participants. Neurobiology of Language, 3(4), 575-598. https://doi.org/10.1162/nol_a_00076
449Uhlmann, M. (2020). Neurobiological models of sentence processing (Doctoral dissertation, Radboud University Nijmegen Nijmegen).https://hdl.handle.net/2066/226625
450Ullas, S. (2020). Lexical and audiovisual bases of perceptual adaptation in speech. Ipskamp Printing BV.https://doi.org/10.26481/dis.20200617su
451Ullas, S., Eisner, F., Cutler, A., & Formisano, E. (2016). Lexical and lip-reading information as sources of phonemic boundary recalibration. In the Eighth Annual Meeting of the Society for the Neurobiology of Language (SNL 2016).
452Ullas, S., Formisano, E., Eisner, F., & Cutler, A. (2020). Audiovisual and lexical cues do not additively enhance perceptual adaptation. Psychonomic bulletin & review, 27, 707-715.https://doi.org/10.3758/s13423-020-01728-5
453Ullas, S., Formisano, E., Eisner, F., & Cutler, A. (2020). Interleaved lexical and audiovisual information can retune phoneme boundaries. Attention, Perception, & Psychophysics, 1-9.https://doi.org/10.3758/s13414-019-01961-8
454Ullas, S., Hausfeld, L., Cutler, A., Eisner, F., & Formisano, E. (2020). Neural correlates of phonetic adaptation as induced by lexical and audiovisual context. Journal of Cognitive Neuroscience, 32(11), 2145-2158.https://doi.org/10.1162/jocn_a_01608
455Ulmer, D., Hupkes, D., & Bruni, E. (2019). Assessing incrementality in sequence-to-sequence models. Repl4NLP, ACL 2019. arXiv preprint arXiv:1906.03293
456van de Braak, L. D., Dingemanse, M., Toni, I., van Rooij, I., & Blokpoel, M. (2021). Computational challenges in explaining communication: How deep the rabbit hole goes. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
457van de Pol, I., Lodder, P., van Maanen, L., Steinert-Threlkeld, S., & Szymanik, J. (2021). Quantifiers satisfying semantic universals are simpler. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
458van de Pol, I., Steinert-Threlkeld, S., & Szymanik, J. (2019). Complexity and learnability in the explanation of semantic universals of quantifiers. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society.
459Van De Pol, I., Van Rooij, I., & Szymanik, J. (2018). Parameterized complexity of theory of mind reasoning in dynamic epistemic logic. Journal of Logic, Language and Information, 27(3), 255-294.https://doi.org/10.1007/s10849-018-9268-4
460Van den Bosch, A. (2016). Open-domain extraction of future events from Twitter.https://doi.org/10.1017/ S1351324916000036
461Van der Meer, D., Sønderby, I. E., Kaufmann, T., Walters, G. B., Abdellaoui, A., Ames, D., Amunts, K., Andersson, M., Armstrong, N. J., Bernard, M., Blackburn, N. B., Blangero, J., Boomsma, D. I., Brodaty, H., Brouwer, R. M., Bülow, R., Cahn, W., Calhoun, V. D., Caspers, S., Cavalleri, G. L. and 112 more (2019). Association of copy number variation of the 15q11.2 BP1-BP2 region with cortical and subcortical morphology and cognition. JAMA Psychiatry. Advance online publication. doi: 10.1001/jamapsychiatry.2019.3779https://doi.org/10.1001/jamapsychiatry.2019.3779
462Van der Meer, H. A., Sheftel-Simanova, I., Kan, C. C., & Trujillo, J. P. (2022). Translation, cross-cultural adaptation, and validation of a dutch version of the actions and feelings questionnaire in autistic and neurotypical adults. Journal of autism and developmental disorders, 52(4), 1771-1777.
463van der Wal, O., de Boer, S., Bruni, E., & Hupkes, D. (2020). The Grammar of Emergent Languages. arXiv preprint arXiv:2010.02069.
464Van Gijn, R., Hammarström, H., Van de Kerke, S., Krasnoukhova, O., & Muysken, P. (2017). Linguistic areas, linguistic convergence and river systems in South America. In The Cambridge handbook of areal linguistics (pp. 964-996). Cambridge University Press.https://doi.org/10.1017/9781107279872.034
465van Rooij, I., & Blokpoel, M. (2020). Formalizing verbal theories: A tutorial by dialogue. Social Psychology, 51, 285-298https://doi.org/10.1027/1864-9335/a000428
466van Rooij, I., Blokpoel, M., de Haan, R., & Wareham, T. (2019). Tractable embodied computation needs embeddedness. Reti, saperi, linguaggi, 6(1), 25-38.https://doi.org/10.12832/94728
467van Rooij, I., Blokpoel, M., Kwisthout, J., & Wareham, T. (2019). Cognition and intractability: A guide to classical and parameterized complexity analysis. Cambridge University Press. DOI: 10.12832/94728
468van Rooij, R., & Kochari, A. (2019). Grounding a pragmatic theory of vagueness on experimental data: Semi-orders and Weber’s Law. In R. Dietz (Ed.), Vagueness & Rationality in Language Use and Cognition. Springer International Publishing. Doi: 10.1007/978-3-030-15931-3https://doi.org/10.1007/978-3-030-15931-3_9
469van Tiel, B., Blokpoel, M., van Rooij, I., & Martin, A. E. (2021). Compositionality, modularity, and the architecture of the language faculty. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
470Vos, S. H., Kessels, R. P., Vinke, R. S., Esselink, R. A., & Piai, V. (2021). Language function after DBS in Parkinson’s disease (Vos et al., 2021). ASHA journals. Journal contribution.https://doi.org/10.23641/asha.14794458
471Vos, S. H., Kessels, R. P., Vinke, R. S., Esselink, R. A., & Piai, V. (2021). The effect of deep brain stimulation of the subthalamic nucleus on language function in Parkinson’s disease: A systematic review. Journal of Speech, Language, and Hearing Research, 64(7), 2794-2810.https://doi.org/10.1044/2021_JSLHR-20-00515
472Wheatley, T., Boncz, A., Toni, I., & Stolk, A. (2019). Beyond the Isolated Brain: The Promise and Challenge of Interacting Minds. Neuron, 103(2), 186-188. https://doi.org/10.1016/j.neuron.2019.05.009https://doi.org/10.1016/j.neuron.2019.05.009
473Willems, R. M., Nastase, S. A., & Milivojevic, B. (2020). Narratives for neuroscience. Trends in neurosciences, 43(5), 271-273.https://doi.org/10.1016/j.tins.2020.03.003
474Winner, T., Selen, L., Murillo Oosterwijk, A., Verhagen, L., Medendorp, P., van Rooij, I., & Toni, I. (2019). Recipient design in communicative pointing. Cognitive Science, 43(5), 1-19. https://doi.org/10.1111/cogs.12733https://doi.org/10.1111/cogs.12733
475Woensdregt, M. S., Spike, M., de Haan, R., Wareham, T., van Rooij, I., & Blokpoel, M. (2021). Why is scaling up models of language evolution hard?. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
476Wolf, M. C., Meyer, A. S., Rowland, C. F., & Hintz, F. (2021). The effects of input modality, word difficulty and reading experience on word recognition accuracy. Collabra: Psychology, 7(1), 24919.https://doi.org/10.1525/collabra.24919
477Wong, M. M., Sha, Z., Lutje, L., Kong, X. Z., Velthuijs, N., Van Heukelum, S., van de berg, W.D., Jonkman, L.E., Fisher, S.E. & Francks, C. (2024). The neocortical infrastructure for language involves region-specific patterns of laminar gene expression. bioRxiv, 2024-01.https://doi.org/10.1101/2024.01.17.575815
478Yang, J., Van den Bosch, A., & Frank, S.L. (2022). Unsupervised text segmentation predicts eye fixations during reading. Frontiers in Artificial Intelligence, 5, 731615https://doi.org/10.3389/frai.2022.731615
479Zeller, J., Bylund, E., & Lewis, A. G. (2022). The parser consults the lexicon in spite of transparent gender marking: EEG evidence from noun class agreement processing in Zulu. Cognition, 226, 105148.https://doi.org/10.1016/j.cognition.2022.105148
480Zhao, B., van de Pol, I., Raijmakers, M. E., & Szymanik, J. (2018). Predicting Cognitive Difficulty of the Deductive Mastermind Game with Dynamic Epistemic Logic Models. In Cogsci.https://doi.org/10.1121/1.5044415
481Zheng, X. Y. & Piai, V. (2022). Neural oscillations of interference control in the aging brain: evidence from picture-word interference. bioRxiv, 2022-08https://doi.org/10.1101/2022.08.26.505477 
482Zheng, X., Roelofs, A., & Lemhöfer, K. (2016). Speaking Two Languages with One Mind: Language Selection Errors during Switching. In the Donders Discussions 2016.
483Zheng, X., Roelofs, A., Erkan, H., & Lemhöfer, K. (2020). Dynamics of inhibitory control during bilingual speech production: An electrophysiological study. Neuropsychologia, 140, 107387.https://doi.org/10.1016/j.neuropsychologia.2020.107387
484Zheng, X., Roelofs, A., Erkan, H., & Lemhöfer, K. (2020). Dynamics of inhibitory control during bilingual speech production: An electrophysiological study. Neuropsychologia, 140, 107387.https://doi.org/10.34973/02r6-5585
485Zioga, I., Weissbart, H., Lewis, A. G., Haegens, S., & Martin, A. E. (2023). Naturalistic spoken language comprehension is supported by alpha and beta oscillations. Journal of Neuroscience, 43(20), 3718-3732.https://doi.org/10.1523/JNEUROSCI.1500-22.2023
486Zuidema, W., & Fitz, H. (2019). Key Issues and Future Directions: Models of Human Language and Speech Processing. In: P. Hagoort (Ed.), Human Language: from Genes and Brains to Behavior (pp. 353). Cambridge, MA: The MIT Press.
487Zuidema, W., & Le, P. (2019). 23 VectorBased and Neural Models. In: P. Hagoort (Ed.), Human Language: from Genes and Brains to Behavior (pp. 313). Cambridge, MA: The MIT Press.
488Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O’Donnell, T. J., Sainburg, T., & Gentner, T. Q. (2019). Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning. Topics in cognitive science. https://doi.org/10.1111/tops.12474https://doi.org/10.1111/tops.12474
489Zuidema, W., Hupkes, D., Wiggins, G., Scharff, C., & Rohrmeier, M. (2018). Formal models of structure building in music, language and animal song. The origins of musicality, 253.