Scientific publications

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

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

NrReferenceDOI
1Abnar, S., & Zuidema, W. (2020). Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928.
2Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. (2017). Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity. arXiv preprint arXiv:1711.09285.
3Abnar, S., Dehghani, M., & Zuidema, W. (2020). Transferring inductive biases through knowledge distillation. arXiv preprint arXiv:2006.00555.
4Abrahamse, R., Beynon, A., & Piai, V. (2021). Long-term auditory processing outcomes in early implanted young adults with cochlear implants: The mismatch negativity vs. P300 response. Clinical Neurophysiology, 132(1), 258-268.https://doi.org/10.1016/j.clinph.2020.09.022
5Adolfi, F., Wareham, T., & van Rooij, I.
(2022). A computational complexity perspective on segmentation as a cognitive subcomputation. Topics in Cognitive Science.		10.1111/tops.12629 
6Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition: the R&R model. In the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 1531-1536). Cognitive Science Society.
7Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules?. Journal of Artificial Intelligence Research, 61, 927-946.
8Ambrogioni, L., Berezutskaya, J., Güçlü, U., van den Borne, E. W., Güçlütürk, Y., van Gerven, M. A., & Maris, E. (2017). Bayesian model ensembling using meta-trained recurrent neural networks. In Workshop on Meta-Learning (MetaLearn 2017)-Neural Information Processing Systems (NIPS 2017) (pp. 1-5). [Sl: sn].
9Arana, 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
1-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
11Armeni, K., Willems, R. M., & Frank, S. L. (2017). Probabilistic language models in cognitive neuroscience: Promises and pitfalls. Neuroscience & Biobehavioral Reviews, 83, 579-588.
12Baas, 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
13Beinborn, L., & Choenni, R. (2020). Semantic drift in multilingual representations. Computational Linguistics, 46, 571–603.https://doi.org/10.1162/coli_a_00382
14Berezutskaya, J. (2020). Data-driven modeling of the neural dynamics underlying language processing (Doctoral dissertation, Utrecht University).https://doi.org/10.33540/103
15Berezutskaya, J., Ambrogioni, L., Ramsey, N. F., & van Gerven, M. A. (2022). Towards naturalistic speech decoding from intracranial brain data. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3100-3104). IEEE.10.1109/EMBC48229.2022.9871301
16Berezutskaya, 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
17Berezutskaya, 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
18Berezutskaya, 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
19Berezutskaya, J., Freudenburg, Z. V., Vansteensel, M. J., Aarnoutse, E. J., Ramsey, N. F., & van Gerven, M. A.
(2022). Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models. bioRxiv.		10.1101/2022.08.02.502503 
20Berezutskaya, 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
21Berezutskaya, 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).
22Berezutskaya, J., Saive, A. L., Jerbi, K., & van Gerven, M. (2022). How does artificial intelligence contribute to iEEG research? In: Axmacher (Ed.), Intracranial EEG for cognitive neuroscientists, Springerhttps://doi.org/10.48550/arXiv.2207.13190
23Berezutskaya, 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.
24Berezutskaya, 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
25Blazquez 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
26Blok, L. S., Hiatt, S. M., Bowling, K. M., Prokop, J. W., Engel, K. L., Cochran, J. N., … & Cooper, G. M. (2018). De novo mutations in MED13, a component of the Mediator complex, are associated with a novel neurodevelopmental disorder. Human genetics, 137(5), 375-388.https://doi.org/10.1007/s00439-018-1887-y
27Blok, L. S., Rousseau, J., Twist, J., Ehresmann, S., Takaku, M., Venselaar, H., … & Campeau, P. M. (2018). CHD3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language. Nature communications, 9(1), 1-12.https://doi.org/10.1038/s41467-018-06014-6
28Blok, 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
29Blokpoel, M. & van Rooij, I. (2021). Theoretical modeling for cognitive science and psychology (Open, interactive book). https://computationalcognitivescience.github.io/lovelace/home
30Blokpoel, M. (2018). Sculpting Computational‐Level Models. Topics in cognitive science, 10(3), 641-648.https://doi.org/10.1111/tops.12282
31Blokpoel, 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
32Bögels, S., & Levinson, S. C. (2017). The brain behind the response: Insights into turn-taking in conversation from neuroimaging. Research on Language and Social Interaction, 50(1), 71-89.https://doi.org/10.1080/08351813.2017.1262118
33Bögels, S., Casillas, M., & Levinson, S. C. (2018). Planning versus comprehension in turn-taking: Fast responders show reduced anticipatory processing of the question. Neuropsychologia, 109, 295-310.https://doi.org/10.1016/j.neuropsychologia.2017.12.028
34Bögels, 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
35Bögels, S., Milvojevic, B., De Haas, N., Döller, C., Rasenberg, M., Ozyurek, A., … & Toni, I. (2018). Creating shared conceptual representations. In the 10th Dubrovnik Conference on Cognitive Science.http://hdl.handle.net/21.11116/0000-0003-F389-0
36Bosker, H. R. (2014). The processing and evaluation of fluency in native and non-native speech. PhD Thesis, Utrecht University, Utrecht.
37Bosker, H. R. (2016). Our own speech rate influences speech perception. In Speech Prosody 2016 (pp. 227-231).
38Bosker, 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.
39Bosker, H. R. (2017). Accounting for rate-dependent category boundary shifts in speech perception. Attention, Perception, & Psychophysics, 79(1), 333-343.
40Bosker, 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.
41Bosker, H. R. (2017). How your own speech rate can change how you listen to others. In the Abstraction, Diversity and Speech Dynamics Workshop.
42Bosker, H. R. (2017). Neural entrainment persists after stimulation, guiding temporal sampling of subsequent speech. In the Neural Oscillations in Speech and Language Processing symposium.
43Bosker, 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.
44Bosker, H. R. (2017). The role of temporal amplitude modulations in the political arena: Hillary Clinton vs. Donald Trump. In Interspeech 2017 (pp. 2228-2232).
45Bosker, 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
46Bosker, 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
47Bosker, H. R., & Cooke, M. (2017). Comparing the rhythmic properties of plain and Lombard speech. In the Abstraction, Diversity and Speech Dynamics Workshop.
48Bosker, H. R., & Cooke, M. (2017). Rhythm in plain and Lombard speech. In the 9th Speech in Noise Workshop.
49Bosker, 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
50Bosker, 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
51Bosker, 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.
52Bosker, H. R., & Kösem, A. (2017). An entrained rhythm’s frequency, not phase, influences temporal sampling of speech. In Interspeech 2017 (pp. 2416-2420).
53Bosker, 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.
54Bosker, 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).
55Bosker, 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.
56Bosker, 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
57Bosker, 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
58Bosker, 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
59Bosker, 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).
60Bosker, 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.
61Bosker, H. R., Reinisch, E., & Sjerps, M. J. (2017). Cognitive load makes speech sound fast, but does not modulate acoustic context effects. Journal of Memory and Language, 94, 166-176.https://doi.org/10.1016/j.jml.2016.12.002
62Bosker, 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.
63Braunsdorf, 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
64Brysbaert, M., Sui, L., Dirix, N., & Hintz, F. (2020). Dutch author recognition test. Journal of cognition, 3(1).https://doi.org/10.5334/joc.95
65Burgering, M. A., Ten Cate, C., & Vroomen, J. (2018). Mechanisms underlying speech sound discrimination and categorization in humans and zebra finches. Animal cognition, 21(2), 285-299.https://doi.org/10.1007/s10071-018-1165-3
66Burgering, 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
67Burgering, 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
68Burgering, M. (2021). The multidimensionality of speech categorization: Exploring shared mechanisms in songbirds together with audiovisual and neural mechanisms in humans. [Dissertation]
69Camerino, I., Ferreira, J., Vonk, J. M., Kessels, R. P., de Leeuw, F. E., Roelofs, A., David Copland, & Piai, V. (2022). Systematic Review and Meta-Analyses of Word Production Abilities in Dysfunction of the Basal Ganglia: Stroke, Small Vessel Disease, Parkinson’s Disease, and Huntington’s Disease. Neuropsychology Review, 1-26.https://doi.org/10.1007/s11065-022-09570-3
70Camerino, 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
71Cao, Y., Oostenveld, R., Alday, P. M., & Piai, V.
(2022). Are alpha and beta oscillations spatially dissociated over the cortex in context‐driven spoken‐word production?. Psychophysiology, 59(6), e13999.		https://doi.org/10.1111/psyp.13999
72Carota, F., Schoffelen, J. M., Oostenveld, R., & Indefrey, P. (2022). The time course of language production as revealed by pattern classification of MEG sensor data. Journal of Neuroscience, 42(29), 5745-5754.https://doi.org/10.1523/JNEUROSCI.1923-21.2022 
73Carota, F., Schoffelen, J.-M., R. Oostenveld, Indefrey, P. (2022). The neural dynamics of language production as revealed by pattern classification of MEG data. Journal of Neuroscience.
74Chu, M., Tobin, P., Ioannidou, F., & Bašnáková, J. (2022). Encoding and decoding hidden meanings in face-to-face communication: Understanding the role of verbal and nonverbal behaviors in indirect replies. Journal of Experimental Psychology: General.
75Chupina, I., Sierpowska, J., Zheng, X. Y., Dewenter, A., Piastra, M. C., & Piai, V. (2022). Time course of right‐hemisphere recruitment during word production following left‐hemisphere damage: A single case of young stroke. European Journal of Neuroscience, 56(8), 5235-5259.10.1111/ejn.15813 
76Cools, R. (2016). The costs and benefits of brain dopamine for cognitive control. Wiley Interdisciplinary Reviews: Cognitive Science, 7(5), 317-329.
77Cools, 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
78Cools, 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
79Coopmans, C. W., De Hoop, H., Hagoort, P., & Martin, A. E. (2021). Cortical tracking and the relationship between structure and meaning. In the 13th Annual Meeting of the Society for the Neurobiology of Language (SNL 2021 Virtual Edition).http://hdl.handle.net/21.11116/0000-0009-5887-C
80Coopmans, C. W., De Hoop, H., Hagoort, P., & Martin, A. E. (2022). Effects of structure and meaning on cortical tracking of linguistic units in naturalistic speech. Neurobiology of Language, 3(3), 386-412.https://doi.org/10.1162/nol_a_00070
81Coopmans, 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).
82Coopmans, 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
83Cornelissen, 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.
84Corps, R. E., Knudsen, B., & Meyer, A. S. (2022). Overrated gaps: Inter-speaker gaps provide limited information about the timing of turns in conversation. Cognition, 223: 105037. doi:10.1016/j.cognition.2022.105037.https://doi.org/10.1016/j.cognition.2022.105037
85Cosma, R.A., Knobel, L., De Heer Kloots, M., & Van der Wal, O. (2022).Seeing the bigger picture: Can deep neural agents learn higher-level concepts in crossmodal referential games? Proceeding ML4Evolang 2022 Workshop.
86Cutler, 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.
87Cutler, 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
88Cutler, A., & Jesse, A. (2021). Word stress in speech perception. The Handbook of Speech Perception, 239-265.
89Cutler, A., Junge, C., Spokes, T. & Kidd, E. (2018). Phonological acquisition: Stress-based segmentation in English.  Abstracts of Laboratory Phonology 16, Lisbon; pp. 22-23
90Cutter, 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
91Cutter, 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
92Cutter, 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
93Dai, B., McQueen, J. M., Terporten, R., Hagoort, P., & Kösem, A. (2022). Distracting Linguistic Information Impairs Neural Tracking of Attended Speech. Current Research in Neurobiology, 3: 100043.https://doi.org/10.1016/j.crneur.2022.100043
94de Lange, F. P., & Ekman, M. (2018). Vision: Framing orientation selectivity. Elife, 7, e39762.https://doi.org/10.7554/eLife.39762
95De Wit, L., Kessels, R. P. C., Kurasz, A. M., Amofa, P., Sr., O’Shea, D., Marsiske, M., Chandler, M. J., et al. (2022). Declarative Learning, Priming, and Procedural Learning Performances comparing Individuals with Amnestic Mild Cognitive Impairment, and Cognitively Unimpaired Older Adults. Journal of the International Neuropsychological Society. doi:10.1017/s135561772200002910.1017/S1355617722000029
96de Zubicaray, G. I., & Piai, V. (2019). Investigating the spatial and temporal components of speech production. The Oxford handbook of neurolinguistics, 471-497.
97Dekker, 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
98Den 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
99Dideriksen, C., Christiansen, M. H., Tylén, K., Dingemanse, M.
, & Fusaroli, R. (2022). Quantifying the interplay of conversational devices in building mutual understanding. Journal of Experimental Psychology: General.		https://doi.org/10.1037/xge0001301
100Dingemanse, M., Liesenfeld, A., & Woensdregt, M. (2022). Convergent cultural evolution of continuers (mhmm). In A. Ravignani, R. Asano, D. Valente, F. Ferretti, S. Hartmann, M. Hayashi, et al. (Eds.), The Evolution of Language: Proceedings of the Joint Conference on Language Evolution (JCoLE) (pp. 160-167). Nijmegen: Joint Conference on Language Evolution (JCoLE). doi:10.31234/osf.io/65c79.https://doi.org/10.31234/osf.io/65c79
101Dingemanse, M., Liesenfeld, A., Rasenberg, M., Albert, S., Ameka, F. K., Birhane, A., … & Wiltschko, M. (2023). Beyond Single‐Mindedness: A Figure‐Ground Reversal for the Cognitive Sciences. Cognitive science, 47(1), e13230.https://doi.org/10.1111/cogs.13230
102Doumas, L. A., & Martin, A. E. (2021). A model for learning structured representations of similarity and relative magnitude from experience. Current Opinion in Behavioral Sciences, 37, 158-166.https://doi.org/10.1016/j.cobeha.2021.01.001
103Doumas, 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.
104Drijvers, L. (2019). On the oscillatory dynamics underlying speech-gesture integration in clear and adverse listening conditions (Doctoral dissertation, [Sl: sn]).https://hdl.handle.net/2066/202981
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