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.

ReferenceDOI
Abnar, S., & Zuidema, W. (2020). Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928.
Abnar, 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.
Abnar, S., Dehghani, M., & Zuidema, W. (2020). Transferring inductive biases through knowledge distillation. arXiv preprint arXiv:2006.00555.
Abrahamse, 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 Neurophysiology132(1), 258-268.https://doi.org/10.1016/j.clinph.2020.09.022
Abrahamse, 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
Alhama, 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.
Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules?. Journal of Artificial Intelligence Research61, 927-946.
Ambrogioni, 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].
Arana, 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
Araújo, 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
Armeni, K., Willems, R. M., & Frank, S. L. (2017). Probabilistic language models in cognitive neuroscience: Promises and pitfalls. Neuroscience & Biobehavioral Reviews83, 579-588.
Baas, 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
Beinborn, L., & Choenni, R. (2020). Semantic drift in multilingual representations. Computational Linguistics, 46, 571–603.https://doi.org/10.1162/coli_a_00382
Berezutskaya, J. (2020). Data-driven modeling of the neural dynamics underlying language processing (Doctoral dissertation, Utrecht University).https://doi.org/10.33540/103
Berezutskaya, 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
Berezutskaya, 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
Berezutskaya, 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
Berezutskaya, 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
Berezutskaya, 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).
Berezutskaya, 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.https://doi.org/10.1038/s41597-022-01173-0
Berezutskaya, 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
Blazquez 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
Blok, 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 genetics137(5), 375-388.https://doi.org/10.1007/s00439-018-1887-y
Blok, 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 communications9(1), 1-12.https://doi.org/10.1038/s41467-018-06014-6
Blok, 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
Blokpoel, M. & van Rooij, I. (2021). Theoretical modeling for cognitive science and psychology (Open, interactive book). https://computationalcognitivescience.github.io/lovelace/home
Blokpoel, M. (2018). Sculpting Computational‐Level Models. Topics in cognitive science10(3), 641-648.https://doi.org/10.1111/tops.12282
Blokpoel, 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
Bögels, S., & Levinson, S. C. (2017). The brain behind the response: Insights into turn-taking in conversation from neuroimaging. Research on Language and Social Interaction50(1), 71-89.https://doi.org/10.1080/08351813.2017.1262118
Bögels, S., Casillas, M., & Levinson, S. C. (2018). Planning versus comprehension in turn-taking: Fast responders show reduced anticipatory processing of the question. Neuropsychologia109, 295-310.https://doi.org/10.1016/j.neuropsychologia.2017.12.028
Bögels, 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
Bögels, 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.
Bosker, H. R. (2014). The processing and evaluation of fluency in native and non-native speech. PhD Thesis, Utrecht University, Utrecht.
Bosker, H. R. (2016). Our own speech rate influences speech perception. In Speech Prosody 2016 (pp. 227-231).
Bosker, 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.
Bosker, H. R. (2017). Accounting for rate-dependent category boundary shifts in speech perception. Attention, Perception, & Psychophysics79(1), 333-343.
Bosker, 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.
Bosker, H. R. (2017). How your own speech rate can change how you listen to others. In the Abstraction, Diversity and Speech Dynamics Workshop.
Bosker, H. R. (2017). Neural entrainment persists after stimulation, guiding temporal sampling of subsequent speech. In the Neural Oscillations in Speech and Language Processing symposium.
Bosker, 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.
Bosker, H. R. (2017). The role of temporal amplitude modulations in the political arena: Hillary Clinton vs. Donald Trump. In Interspeech 2017 (pp. 2228-2232).
Bosker, 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
Bosker, 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
Bosker, H. R., & Cooke, M. (2017). Comparing the rhythmic properties of plain and Lombard speech. In the Abstraction, Diversity and Speech Dynamics Workshop.
Bosker, H. R., & Cooke, M. (2017). Rhythm in plain and Lombard speech. In the 9th Speech in Noise Workshop.
Bosker, 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
Bosker, 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
Bosker, 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.
Bosker, H. R., & Kösem, A. (2017). An entrained rhythm’s frequency, not phase, influences temporal sampling of speech. In Interspeech 2017 (pp. 2416-2420).
Bosker, 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.
Bosker, 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).
Bosker, 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.
Bosker, 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
Bosker, 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
Bosker, 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
Bosker, 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).
Bosker, 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.
Bosker, 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 Language94, 166-176.https://doi.org/10.1016/j.jml.2016.12.002
Bosker, 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.
Braunsdorf, 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
Brysbaert, M., Sui, L., Dirix, N., & Hintz, F. (2020). Dutch author recognition test. Journal of cognition, 3(1).https://doi.org/10.5334/joc.95
Burgering, M. A., Ten Cate, C., & Vroomen, J. (2018). Mechanisms underlying speech sound discrimination and categorization in humans and zebra finches. Animal cognition21(2), 285-299.https://doi.org/10.1007/s10071-018-1165-3
Burgering, 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
Burgering, 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
Burgering, M. (2021). The multidimensionality of speech categorization: Exploring shared mechanisms in songbirds together with audiovisual and neural mechanisms in humans.
Burgering, M. (2021). The multidimensionality of speech categorization: Exploring shared mechanisms in songbirds together with audiovisual and neural mechanisms in humans. [Dissertation]
Camerino, 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 Mapping42(4), 993-1002.https://doi.org/10.1002/hbm.25273
Camerino, 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
Cools, R. (2016). The costs and benefits of brain dopamine for cognitive control. Wiley Interdisciplinary Reviews: Cognitive Science7(5), 317-329.
Cools, 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
Cools, 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
Coopmans, 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).
Coopmans, 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).
Coopmans, 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
Cornelissen, 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.
Cutler, 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.
Cutler, 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
Cutler, A., & Jesse, A. (2021). Word stress in speech perception. The Handbook of Speech Perception, 239-265.
Cutler, A., & Jesse, A. (2021). Word stress in speech perception. The Handbook of Speech Perception, 239-265.
Cutler, A., Junge, C., Spokes, T. & Kidd, E. (2018). Phonological acquisition: Stress-based segmentation in English.  Abstracts of Laboratory Phonology 16, Lisbon; pp. 22-23
Cutter, 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
Cutter, 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
Cutter, 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
de Lange, F. P., & Ekman, M. (2018). Vision: Framing orientation selectivity. Elife7, e39762.https://doi.org/10.7554/eLife.39762
de Zubicaray, G. I., & Piai, V. (2019). Investigating the spatial and temporal components of speech production. The Oxford handbook of neurolinguistics, 471-497.
Dekker, 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
Den 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
Doumas, L. A., & Martin, A. E. (2021). A model for learning structured representations of similarity and relative magnitude from experience. Current Opinion in Behavioral Sciences37, 158-166.https://doi.org/10.1016/j.cobeha.2021.01.001
Doumas, 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.
Drijvers, L. (2019). On the oscillatory dynamics underlying speech-gesture integration in clear and adverse listening conditions (Doctoral dissertation, [Sl: sn]).
Drijvers, 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).
Drijvers, L., & Ozyurek, A. (2016). What do iconic gestures and visible speech contribute to degraded speech comprehension?. In the Nijmegen Lectures 2016.
Drijvers, 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 Research60(1), 212-222.
Drijvers, 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 language177, 7-17.https://doi.org/10.1016/j.bandl.2018.01.003
Drijvers, 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.
Drijvers, L., & Trujillo, J. P. (2018). Commentary: Transcranial magnetic stimulation over left inferior frontal and posterior temporal cortex disrupts gesture-speech integration. Frontiers in human neuroscience12, 256.https://doi.org/10.3389/fnhum.2018.00256
Drijvers, 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
Drijvers, L., Mulder, K., & Ernestus, M. (2016). Alpha and gamma band oscillations index differential processing of acoustically reduced and full forms. Brain and language153, 27-37.
Drijvers, 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).
Drijvers, 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.
Drijvers, 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
Drijvers, 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
Drijvers, 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.
Drjivers, 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).
Dubois, Y., Dagan, G., Hupkes, D., & Bruni, E. (2019). Location attention for extrapolation to longer sequences. arXiv preprint arXiv:1911.03872.
Eijk, L., Ernestus, M., & Schriefers, H. (2019). Alignment of Pitch and Articulation Rate. In Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne.
Eijk, 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
Eising, 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
Eisner, F., & McQueen, J. M. (2018). Speech perception. Stevens’ handbook of experimental psychology and cognitive neuroscience3, 1-46.
Eisner, 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).
Ernestus, M., Dikmans, M. E., & Giezenaar, G. (2017). Advanced second language learners experience difficulties processing reduced word pronunciation variants. Dutch Journal of Applied Linguistics6(1), 1-20.https://doi.org/10.1075/dujal.6.1.01ern
Favier, 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
Ferreira, 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
Fisher, S. E. (2017). Evolution of language: Lessons from the genome. Psychonomic bulletin & review24(1), 34-40.
Fisher, 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
Fisher, 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.
Fisher, 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
Fitz, 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).
Fitz, H., & Chang, F. (2017). Meaningful questions: The acquisition of auxiliary inversion in a connectionist model of sentence production. Cognition166, 225-250.https://doi.org/10.1016/j.cognition.2017.05.008
Fitz, 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
Fitz, 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
Francks, 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
Frank, 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).
Frank, S. L. (2017). Word embedding distance does not predict word reading time.
Frank, S. L. (2021). Toward computational models of multilingual sentence processing. Language Learning71(S1), 193-218.https://doi.org/10.1111/lang.12406
Frank, S. L. (2021). Toward computational models of multilingual sentence processing. Language Learning, 71(S1), 193-218.https://doi.org/10.1111/lang.12406
Frank, 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
Frank, 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
Frank, S. L., & Fitz, H. (2016). Reservoir computing and the Sooner-is-Better bottleneck [Commentary on Christiansen & Slater]. Behavioral and Brain Sciences39.
Frank, S. L., & Willems, R. M. (2017). Word predictability and semantic similarity show distinct patterns of brain activity during language comprehension. Language, Cognition and Neuroscience32(9), 1192-1203.https://doi.org/10.1080/23273798.2017.1323109
Frank, S. L., & Yang, J. (2018). Lexical representation explains cortical entrainment during speech comprehension. PloS one13(5), e0197304.https://doi.org/10.1371/journal.pone.0197304
Frank, 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
Frank, 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.
Frank, S. L., Trompenaars, T., & Vasishth, S. (2016). Cross‐linguistic differences in processing double‐embedded relative clauses: Working‐memory constraints or language statistics?. Cognitive Science40(3), 554-578.
Frank, 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.
Frank, 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).
Frank, 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.
Franken, 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
Freches, 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
Gibson, M., & Bosker, H. R. (2016). Over vloeiendheid in spraak. Tijdschrift Taal, 7(10), 40-45.
Giglio, 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).
Giglio, 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
Grey, 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
Gunz, 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
Haak, K. V., Marquand, A. F., & Beckmann, C. F. (2018). Connectopic mapping with resting-state fMRI. Neuroimage170, 83-94.https://doi.org/10.1016/j.neuroimage.2017.06.075
Hagoort, P. (2014). Nodes and networks in the neural architecture for language: Broca’s region and beyond. Current opinion in Neurobiology28, 136-141.
Hagoort, 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 Sciences40.https://doi.org/10.1017/S0140525X16002028
Hagoort, P. (2017). The core and beyond in the language-ready brain. Neuroscience & Biobehavioral Reviews81, 194-204.https://doi.org/10.1016/j.neubiorev.2017.01.048
Hagoort, 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
Hagoort, P. (2018). Prerequisites for an evolutionary stance on the neurobiology of language. Current opinion in behavioral sciences21, 191-194.https://doi.org/10.1016/j.cobeha.2018.05.012
Hagoort, P. (2019). Introduction. In P. Hagoort (Ed.), Human language: From genes and brains to behavior (pp. 1-6). Cambridge, MA: MIT Press.
Hagoort, 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
Hagoort, 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.
Hagoort, 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
Hagoort, P. (Ed.). (2019). Human language: From genes and brains to behavior. Cambridge, MA: MIT Press.
Hagoort, 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.
Hagoort, P., & Indefrey, P. (2014). The neurobiology of language beyond single words. Annual review of neuroscience37, 347-362.
Heidlmayr, 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).
Heidlmayr, 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
Heilbron, 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).
Heilbron, 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
Heilbron, 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
Heyselaar, 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
Hintz, 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
Hintz, 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
Hintz, 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 Cognition43(9), 1352.http://dx.doi.org/10.1037/xlm0000388
Hintz, 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
Hintz, 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
Hintz, 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).
Hintz, 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).
Hoedemaker, 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
Hollenstein, 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).
Huizeling, 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).
Huizeling, 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
Hulten, 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
Hupkes, D. (2020). Hierarchy and interpretability in neural models of language processing. AmsterdamInstitute for Logic, Language and Computation.
Hupkes, 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).
Hupkes, 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.
Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (2020). Compositionality decomposed: how do neural networks generalise?. Journal of Artificial Intelligence Research, 67, 757-795.
Hupkes, D., Singh, A., Korrel, K., Kruszewski, G., & Bruni, E. (2018). Learning compositionally through attentive guidance. arXiv preprint arXiv:1805.09657.
Hupkes, D., Singh, A.K., Korrel K., Kruszewski, G., & Bruni, E. (2019). Learning compositionally through attentive guidance. CICLing 2019. arXiv preprint arXiv:1805.09657.
Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907-926.
Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research61, 907-926.
Hustá, 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
Hustá, 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
Janssen, N. (2020). Staying connected as we speak: Behavioral and tractography evidence from health and neurodegenerative disease (Doctoral dissertation, Radboud University Nijmegen).
Janssen, 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
Janssen, 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.
Jongman, 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
Jongman, 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
Jongman, 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
Jongman, 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
Jumelet, J., & Hupkes, D. (2018). Do language models understand anything? on the ability of lstms to understand negative polarity items. arXiv preprint arXiv:1808.10627.
Jumelet, 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
Kapteijns, 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
Khoe, Y. H., Kootstra, G. J., Schoonen, R., & Frank, S. L. (2021). Simulating proficiency and exposure effects on cross-language structural priming in simultaneous bilinguals.
Khoe, 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)
Kochari, 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
Kochari, A. (2020). Processing symbolic magnitude information conveyed by number words and by scalar adjectives.
Kochari, A. (2020). Perceiving and communicating magnitudes: Behavioral and electrophysiological studies. AmsterdamInstitute for Logic, Language and Computation.
Kochari, 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
Kochari, 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
Kochari, 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. Neuropsychologia155, 107754.https://doi.org/10.1016/j.neuropsychologia.2021.107754
Kochari, 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
Kochari, 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
Kochari, 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
Kochari, A., Van Rooij, R., & Schulz, K. (2020). Generics and Alternatives. Frontiers in Psychology11, 1274.https://doi.org/10.3389/fpsyg.2020.01274
Korrel, 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.
Kösem, 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
Kösem, 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
Krutwig, 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).
Levinson, S. C. (2016). Turn-taking in human communication–origins and implications for language processing. Trends in cognitive sciences20(1), 6-14.
Levinson, S. C., & Holler, J. (2014). The origin of human multi-modal communication. Philosophical Transactions of the Royal Society B: Biological Sciences369(1651), 20130302.
Levshina, N. (2020). Communicative efficiency and differential case marking: A reverse-engineering approach. Linguistics Vanguard.
Levshina, N. (2020). Conditional inference trees and random forests. In Practical Handbook of Corpus Linguistics. Springer.
Levshina, N. (2020). Database of Annotated Core Arguments: English, Lao and Russian (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4065523
Levshina, N. (2020). Efficient trade-offs as explanations in functional linguistics: some problems and an alternative proposal. Revista da ABRALIN 19(3): 50-78.
Levshina, 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
Levshina, N. (2021). Communicative efficiency and differential case marking: a reverse-engineering approach. Linguistics Vanguard, 7(s3).https://doi.org/10.1515/lingvan-2019-0087
Levshina, N. (2021). Corpus-based typology: Applications, challenges and some solutions. Linguistic Typology.
Levshina, N. (2021). Corpus-based typology: Applications, challenges and some solutions. Linguistic Typology.
Levshina, 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
Levshina, N. (2022). Frequency, informativity and word length: Insights from typologically diverse corpora. Entropy, 24(2), 280.https://doi.org/10.3390/e24020280
Levshina, 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
Levshina, N., & Moran, S. (2021). Efficiency in human languages: Corpus evidence for universal principles. Linguistics Vanguard7(s3).
Lewis, 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
Liu, R., Bögels, S., Bird, G., Medendorp, P., & Toni, I. (2019). Integrating visuospatial perspective-taking and communicative demands in sensorimotor control of referential pointing.. PsyArXiv.
Liu, 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.
Liu, 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.
Lopopolo, 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).
Lopopolo, 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).
Lopopolo, 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).
Lopopolo, 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).
Lopopolo, 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 one12(5), e0177794.https://doi.org/10.1371/journal.pone.0177794
Lopopolo, 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).
Lopopolo, 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 Language2(1), 152-175.https://doi.org/10.1162/nol_a_00029
Lopopolo, 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
Marquand, A. F., Haak, K. V., & Beckmann, C. F. (2017). Functional corticostriatal connection topographies predict goal-directed behaviour in humans. Nature human behaviour1(8), 1-9.https://doi.org/10.1038/s41562-017-0146
Marquand, 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
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016). Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biological psychiatry80(7), 552-561.
Martin, 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
Martin, 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
Martin, 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
Martin, 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
Maslowski, 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.
Maslowski, 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.
Maslowski, 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
Maslowski, 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
Maslowski, 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
Maslowski, 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
McQueen, 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 Neuroscience31(7), 860-863.
McQueen, J. M., Eisner, F., Burgering, M. A., & Vroomen, J. (2020). Specialized memory systems for learning spoken words. Journal of Experimental Psychology: Learning, Memory, and Cognition46(1), 189.https://doi.org/10.1037/xlm0000704
McQueen, 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 America144(3), 1716-1716.https://doi.org/10.1121/1.5067608
McQueen, 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.
Merkx, 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
Merkx, D., & Frank, S. L. (2020). Human Sentence Processing: Recurrence or Attention?. arXiv preprint arXiv:2005.09471.https://doi.org/10.48550/arXiv.2005.09471
Merkx, 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
Merkx, D., Frank, S.L., & Ernestus, M. (2019). Language learning using speech to image retrieval. In Proceedings of Interspeech 2019, 1841-1845.
Meyer, 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
Milivojevic, 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 Neuroscience36(49), 12412-12424.
Mooijman, 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
Muysken, P. (2016). Bilingual complexes: the perspective of the Gradient Symbolic Computation framework. Bilingualism19(5), 891.https://doi.org/10.1017/S1366728916000031
Muysken, P. (2016). Creole languages. In Oxford Research Encyclopedia of Linguistics.https://doi.org/10.1093/acrefore/9780199384655.013.68
Muysken, P. (2016). Fine tuning cross-linguistic interaction: The nuts and bolts. Cognitive Perspectives on Bilingualism17, 207.
Muysken, P. (2016). From Colombo to Athens: Areal and universalist perspectives on bilingual compound verbs. Languages1(1), 2.https://doi.org/10.3390/languages1010002
Muysken, P. (2016). Language contact: Trojan horse or new potential for cross-fertilization?. Linguistic Typology20(3), 537-545.https://doi.org/10.1515/lingty-2016-0025
Neville, 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).
Neville, 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
Noorman, 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
Noorman, 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
Norris, D., McQueen, J. M., & Cutler, A. (2016). Prediction, Bayesian inference and feedback in speech recognition. Language, cognition and neuroscience31(1), 4-18.
Özyürek, A. (2014). Hearing and seeing meaning in speech and gesture: insights from brain and behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences369(1651), 20130296.
Özyürek, A. (2017). Function and processing of gesture in the context of language. Why gesture, 39-58.
Piai, 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
Piai, 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 Sciences113(40), 11366-11371.
Piai, 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
Piai, 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
Piai, 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
Piai, 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
Pouw, 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.
Pouw, 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
Pouw, 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
Pouw, W., Dingemanse, M., & Ozyurek, A. (2020). Gesture Network Analysis Reveals Structural Properties of Change in Evolving Languages in the Lab.
Pouw, W., Dingemanse, M., Motamedi, Y., & Ozyurek, A. (2020). Multiscale kinematic analysis reveals structural properties of change in evolving manual languages in the lab.
Pouw, 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
Pouw, 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
Przezdzik, 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).
Przezdzik, 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).
Przeździk, 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
Przeździk, 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
Quaresima, 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).
Rasenberg, 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
Rasenberg, 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.
Rasenberg, 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
Rasenberg, 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
Repplinger, M., Beinborn, L. M., & Zuidema, W. H. (2018). Vector-space models of words and sentences. Nieuw Archief voor Wiskunde19(3), 167-174.
Rich, 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
Rich, 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
Rietbergen, 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
Rodd, J. (2020). How speaking fast is like running: Modelling control of speaking rate (Doctoral dissertation, Radboud University Nijmegen Nijmegen).
Rodd, 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).
Rodd, 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
Rodd, 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.
Rodd, 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.
Rodd, 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
Rodd, 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
Rodd, 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
Rodriguez-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
Roelofs, A. (2018). A unified computational account of cumulative semantic, semantic blocking, and semantic distractor effects in picture naming. Cognition172, 59-72.https://doi.org/10.1016/j.cognition.2017.12.007
Roelofs, A. (2021). How attention controls naming: Lessons from Wundt 2.0. Journal of Experimental Psychology: General.https://doi.org/10.1037/xge0001030
Roelofs, 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
Roelofs, 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
Roelofs, A., & Piai, V. (2017). Distributional analysis of semantic interference in picture naming. Quarterly Journal of Experimental Psychology70(4), 782-792.https://doi.org/10.1080/17470218.2016.1165264
Rojas Berscia, L. M. (2019). From Kawapanan to Shawi: Topics in language variation and change (Doctoral dissertation, Radboud University Nijmegen).
Rojas-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.
Rojas-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.
Rojas-Berscia, L. M. (2016). Lóxoro, traces of a contemporary Peruvian genderlect. Borealis–An International Journal of Hispanic Linguistics5(1), 157-170.https://doi.org/10.7557/1.5.1.3725
Rojas-Berscia, L. M. (2019). Nominalization in Shawi (Chayahuita). Nominalization in the languages of the Americas, 491-514.
Rojas-Berscia, L. M., & Bourdeau, C. (2017). Optional or syntactic ergativity in Shawi? Distribution and possible origins. Linguistic discovery, 15(1), 50-65.
Rojas-Berscia, L. M., & Ghavami Dicker, S. (2015). Teonimia en el Alto Amazonas, el caso de Kanpunama. Escritura y Pensamiento, 18(36), 117-146.
Rojas-Berscia, L. M., & Shi, J. A. (2017). Hakka as spoken in Suriname. In Boundaries and Bridges (pp. 179-196). De Gruyter Mouton.
Rojas-Berscia, L. M., Napurí, A., & Wang, L. (2019). Shawi (Chayahuita). Journal of the International Phonetic Association, 1-14.
Roos, 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
Rowland, 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.
Ruiter, 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.
Satizabal, 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
Schiller, 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.
Schoffelen, 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
Schubotz, 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).
Schubotz, 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
Seeliger, 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 Biology17(2), e1008558.https://doi.org/10.1371/journal.pcbi.1008558
Seeliger, 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
Sharoh, D. L. (2020). Advances in layer specific fMRI for the study of language, cognition and directed brain networks (Doctoral dissertation, [Sl: sn]).
Sharoh, 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
Sharoh, 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).
Sierpowska, 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
Sierpowska, 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
Simanova, I.,Francken, J. C., de Lange, F. P., & Bekkering, H. (2016). Linguistic priors shape categorical perception. Language, Cognition and Neuroscience, 31(1), 159-165.
Snijders 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
Snijders Blok, C. (2021). Let the genes speak! De novo variants in developmental disorders with speech and language impairment (Doctoral dissertation, Radboud University Nijmegen).
Snijders 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
Snijders 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
Snijders 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
Snijders 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
Snijders 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
Sommer, N., & Levshina, N. (2021). Cross-linguistic differential and optional marking database. Version 1.0.0. https://zenodo.org/record/4896007#.YdLhI2iZOUk
Stolk, A., Bašnáková, J., & Toni, I. (2020). Joint epistemic engineering: The neglected process of context construction in human communication.
Stolk, A., D’Imperio, D., Di Pellegrino, G., & Toni, I. (2015). Altered communicative decisions following ventromedial prefrontal lesions. Current Biology25(11), 1469-1474.
Stolk, 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 Sciences111(51), 18183-18188.
Stolk, A., Verhagen, L., & Toni, I. (2016). Conceptual alignment: How brains achieve mutual understanding. Trends in cognitive sciences20(3), 180-191.
Szymanik, J. (2016). Computing simple quantifiers. In Quantifiers and Cognition: Logical and Computational Perspectives (pp. 41-49). Springer, Cham.
Szymanik, J. (2016). Quantifiers and cognition: Logical and computational perspectives (Vol. 96). Cham: Springer.
Szymanik, J., & Thorne, C. (2017). Exploring the relation between semantic complexity and quantifier distribution in large corpora. Language Sciences60, 80-93.https://doi.org/10.1016/j.langsci.2017.01.006
ten Cate, C. (2014). On the phonetic and syntactic processing abilities of birds: From songs to speech and artificial grammars. Current Opinion in Neurobiology28, 157-164.
Theves, S. (2017). Accumulation and updating of hierarchical knowledge in hippocampus and PFC. In Internal Symposium.
Theves, S. (2020). Mapping conceptual knowledge in the hippocampal system. (Doctoral dissertation, [Sl: sn]).
Theves, 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
Theves, 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
Theves, 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
Thorin, 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]).
Thorin, 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
Todorova, L. (2021). Language bias in visually driven decisions: Computational and neurophysiological mechanisms (Doctoral dissertation, [Sl: sn]).
Todorova, L. (2021). Language bias in visually driven decisions: Computational and neurophysiological mechanisms (Doctoral dissertation, [Sl: sn]).
Todorova, L., & Neville, D. A. (2019). Perception or control? Mechanisms of gender bias introduced by labels and associative words.
Todorova, 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
Todorova, 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
Todorova, L., Neville, D., & Piai, V. (2019). Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective.
Trujillo, 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.
Trujillo, 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.
Trujillo, 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.
Trujillo, J. P. (2020). Movement speaks for itself: The kinematic and neural dynamics of communicative action and gesture (Doctoral dissertation, Radboud University Nijmegen Nijmegen).
Trujillo, 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
Trujillo, 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
Trujillo, 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
Trujillo, 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
Trujillo, 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
Trujillo, 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
Trujillo, 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.
Trujillo, 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
Tsoukala, C. (2021). Bilingual sentence production and code-switching: Neural network simulations (Doctoral dissertation, Nijmegen:[Sl: sn]).
Tsoukala, C. (2021). Bilingual sentence production and code-switching: Neural network simulations (Doctoral dissertation, Nijmegen:[Sl: sn]).
Tsoukala, 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 & Behavior4(1), 87-100.https://doi.org/10.1007/s42113-020-00088-6
Tsoukala, 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
Tsoukala, 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.
Tsoukala, 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
Tsoukala, 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
Tsoukala, 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.
Uhlmann, M. (2020). Neurobiological models of sentence processing (Doctoral dissertation, Radboud University Nijmegen Nijmegen).
Ullas, S. (2020). Lexical and audiovisual bases of perceptual adaptation in speech. Ipskamp Printing BV.https://doi.org/10.26481/dis.20200617su
Ullas, 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).
Ullas, 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
Ullas, 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
Ullas, 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
Ulmer, D., Hupkes, D., & Bruni, E. (2019). Assessing incrementality in sequence-to-sequence models. Repl4NLP, ACL 2019. arXiv preprint arXiv:1906.03293
van 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).
van 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).
van 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.
Van 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
Van den Bosch, A. (2016). Open-domain extraction of future events from Twitter.https://doi.org/10.1017/ S1351324916000036
Van 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
Van 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.
van der Wal, O., de Boer, S., Bruni, E., & Hupkes, D. (2020). The Grammar of Emergent Languages. arXiv preprint arXiv:2010.02069.
Van 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
van Rooij, I., & Blokpoel, M. (2020). Formalizing verbal theories: A tutorial by dialogue. Social Psychology, 51, 285-298https://doi.org/10.1027/1864-9335/a000428
van 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
van 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
van 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
van 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).
Vos, 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
Vos, 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
Wheatley, 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
Willems, 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
Winner, 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
Woensdregt, 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).
Wolf, 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
Zhao, 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
Zheng, X., Roelofs, A., & Lemhöfer, K. (2016). Speaking Two Languages with One Mind: Language Selection Errors during Switching. In the Donders Discussions 2016.
Zheng, 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
Zheng, 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
Zuidema, 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.
Zuidema, 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.
Zuidema, 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
Zuidema, 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.