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.

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
5Alhama, 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.
6Alhama, 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.
7Ambrogioni, 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].
8Arana, 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
9Araújo, 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
10Armeni, K., Willems, R. M., & Frank, S. L. (2017). Probabilistic language models in cognitive neuroscience: Promises and pitfalls. Neuroscience & Biobehavioral Reviews, 83, 579-588.
11Baas, 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
12Beinborn, L., & Choenni, R. (2020). Semantic drift in multilingual representations. Computational Linguistics, 46, 571–603.https://doi.org/10.1162/coli_a_00382
13Berezutskaya, J. (2020). Data-driven modeling of the neural dynamics underlying language processing (Doctoral dissertation, Utrecht University).https://doi.org/10.33540/103
14Berezutskaya, 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
15Berezutskaya, 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
16Berezutskaya, 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
17Berezutskaya, 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
18Berezutskaya, 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).
19Berezutskaya, 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
20Berezutskaya, 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
21Blazquez 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
22Blok, 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
23Blok, 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
24Blok, 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
25Blokpoel, M. & van Rooij, I. (2021). Theoretical modeling for cognitive science and psychology (Open, interactive book). https://computationalcognitivescience.github.io/lovelace/home
26Blokpoel, M. (2018). Sculpting Computational‐Level Models. Topics in cognitive science, 10(3), 641-648.https://doi.org/10.1111/tops.12282
27Blokpoel, 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
28Bögels, 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
29Bögels, 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
30Bögels, 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
31Bögels, 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
32Bosker, H. R. (2014). The processing and evaluation of fluency in native and non-native speech. PhD Thesis, Utrecht University, Utrecht.
33Bosker, H. R. (2016). Our own speech rate influences speech perception. In Speech Prosody 2016 (pp. 227-231).
34Bosker, 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.
35Bosker, H. R. (2017). Accounting for rate-dependent category boundary shifts in speech perception. Attention, Perception, & Psychophysics, 79(1), 333-343.
36Bosker, 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.
37Bosker, H. R. (2017). How your own speech rate can change how you listen to others. In the Abstraction, Diversity and Speech Dynamics Workshop.
38Bosker, H. R. (2017). Neural entrainment persists after stimulation, guiding temporal sampling of subsequent speech. In the Neural Oscillations in Speech and Language Processing symposium.
39Bosker, 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.
40Bosker, H. R. (2017). The role of temporal amplitude modulations in the political arena: Hillary Clinton vs. Donald Trump. In Interspeech 2017 (pp. 2228-2232).
41Bosker, 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
42Bosker, 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
43Bosker, H. R., & Cooke, M. (2017). Comparing the rhythmic properties of plain and Lombard speech. In the Abstraction, Diversity and Speech Dynamics Workshop.
44Bosker, H. R., & Cooke, M. (2017). Rhythm in plain and Lombard speech. In the 9th Speech in Noise Workshop.
45Bosker, 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
46Bosker, 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
47Bosker, 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.
48Bosker, H. R., & Kösem, A. (2017). An entrained rhythm’s frequency, not phase, influences temporal sampling of speech. In Interspeech 2017 (pp. 2416-2420).
49Bosker, 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.
50Bosker, 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).
51Bosker, 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.
52Bosker, 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
53Bosker, 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
54Bosker, 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
55Bosker, 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).
56Bosker, 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.
57Bosker, 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
58Bosker, 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.
59Braunsdorf, 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
60Brysbaert, M., Sui, L., Dirix, N., & Hintz, F. (2020). Dutch author recognition test. Journal of cognition, 3(1).https://doi.org/10.5334/joc.95
61Burgering, 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
62Burgering, 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
63Burgering, 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
64Burgering, M. (2021). The multidimensionality of speech categorization: Exploring shared mechanisms in songbirds together with audiovisual and neural mechanisms in humans. [Dissertation]
65Camerino, 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
66Cools, R. (2016). The costs and benefits of brain dopamine for cognitive control. Wiley Interdisciplinary Reviews: Cognitive Science, 7(5), 317-329.
67Cools, 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
68Cools, 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
69Coopmans, 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
70Coopmans, 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).
71Coopmans, 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
72Cornelissen, 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.
73Cutler, 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.
74Cutler, 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
75Cutler, A., & Jesse, A. (2021). Word stress in speech perception. The Handbook of Speech Perception, 239-265.
76Cutler, A., Junge, C., Spokes, T. & Kidd, E. (2018). Phonological acquisition: Stress-based segmentation in English.  Abstracts of Laboratory Phonology 16, Lisbon; pp. 22-23
77Cutter, 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
78Cutter, 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
79Cutter, 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
80de Lange, F. P., & Ekman, M. (2018). Vision: Framing orientation selectivity. Elife, 7, e39762.https://doi.org/10.7554/eLife.39762
81de Zubicaray, G. I., & Piai, V. (2019). Investigating the spatial and temporal components of speech production. The Oxford handbook of neurolinguistics, 471-497.
82Dekker, 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
83Den 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
84Doumas, 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
85Doumas, 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.
86Drijvers, 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
87Drijvers, 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).
88Drijvers, L., & Ozyurek, A. (2016). What do iconic gestures and visible speech contribute to degraded speech comprehension?. In the Nijmegen Lectures 2016.
89Drijvers, L., & Özyürek, A. (2017). Visual context enhanced: The joint contribution of iconic gestures and visible speech to degraded speech comprehension. Journal of Speech, Language, and Hearing Research, 60(1), 212-222.
90Drijvers, L., & Özyürek, A. (2018). Native language status of the listener modulates the neural integration of speech and iconic gestures in clear and adverse listening conditions. Brain and language, 177, 7-17.https://doi.org/10.1016/j.bandl.2018.01.003
91Drijvers, 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.
92Drijvers, L., & Trujillo, J. P. (2018). Commentary: Transcranial magnetic stimulation over left inferior frontal and posterior temporal cortex disrupts gesture-speech integration. Frontiers in human neuroscience, 12, 256.https://doi.org/10.3389/fnhum.2018.00256
93Drijvers, 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
94Drijvers, L., Mulder, K., & Ernestus, M. (2016). Alpha and gamma band oscillations index differential processing of acoustically reduced and full forms. Brain and language, 153, 27-37.
95Drijvers, L., Ozyurek, A., & Jensen, O. (2017). Alpha and beta oscillations in the language network, motor and visual cortex index semantic congruency between speech and gestures in clear and degraded speech. In the 47th Annual Meeting of the Society for Neuroscience (SfN).http://hdl.handle.net/21.11116/0000-0002-6979-1
96Drijvers, L., Ozyurek, A., & Jensen, O. (2017). Low-and high-frequency oscillations predict the semantic integration of speech and gestures in clear and degraded speech. In the Neural Oscillations in Speech and Language Processing symposium.http://hdl.handle.net/21.11116/0000-0002-6971-9
97Drijvers, 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
98Drijvers, 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
99Drijvers, 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.
100Drjivers, 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).
101Dubois, Y., Dagan, G., Hupkes, D., & Bruni, E. (2019). Location attention for extrapolation to longer sequences. arXiv preprint arXiv:1911.03872.
102Eijk, L., Ernestus, M., & Schriefers, H. (2019). Alignment of Pitch and Articulation Rate. In Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne.https://hdl.handle.net/2066/208198
103Eijk, 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
104Eising, 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
105Eisner, F., & McQueen, J. M. (2018). Speech perception. Stevens’ handbook of experimental psychology and cognitive neuroscience, 3, 1-46.
106Eisner, 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).
107Ernestus, M., Dikmans, M. E., & Giezenaar, G. (2017). Advanced second language learners experience difficulties processing reduced word pronunciation variants. Dutch Journal of Applied Linguistics, 6(1), 1-20.https://doi.org/10.1075/dujal.6.1.01ern
108Favier, 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
109Ferreira, 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
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