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Omigie, D.; Pearce, M.; Lehongre, K.; Hasboun, D.; Navarro, V.; Adam, C.; Samson, S.: Intracranial recordings and computational modeling of music reveal the time course of prediction error signaling in frontal and temporal cortices. Journal of Cognitive Neuroscience 31 (6), pp. 855 - 873 (2019)

Tavano, A.; Poeppel, D.: A division of labor between power and phase coherence in encoding attention to stimulus streams. NeuroImage 193, pp. 146 - 156 (2019)

Vroegh, T. P.: Zoning-in or tuning-in? Identifying distinct absorption states in response to music. Psychomusicology: Music, Mind, and Brain 29 (2-3), pp. 156 - 170 (2019)

Doelling, K. B.; Assaneo, M. F.; Bevilacqua, D.; Pesaran, B.; Poeppel, D.: An oscillator model better predicts cortical entrainment to music. Proceedings of the National Academy of Sciences of the United States of America 116 (20), pp. 10113 - 10121 (2019)

Auracher, J.; Scharinger, M.; Menninghaus, W.: Contiguity-based sound iconicity: The meaning of words resonates with phonetic properties of their immediate verbal contexts. PLoS One 14 (5), e0216930 (2019)

Assaneo, M. F.; Ripollés, P.; Orpella, J.; Lin, W. M.; de Diego-Balaguer, R.; Poeppel, D.: Spontaneous synchronization to speech reveals neural mechanisms facilitating language learning. Nature Neuroscience 22, pp. 627 - 632 (2019)

Flinker, A.; Doyle, W. K.; Mehta, A. D.; Devinsky, O.; Poeppel, D.: Spectrotemporal modulation provides a unifying framework for auditory cortical asymmetries. Nature Human Behaviour 3 (4), pp. 393 - 405 (2019)

Larrouy-Maestri, P.; Harrison, P. M. C.; Müllensiefen, D.: The mistuning perception test: A new measurement instrument. Behavior Research Methods 51 (2), pp. 663 - 675 (2019)

Rowland, J.; Kasdan, A.; Poeppel, D.: There is music in repetition: Looped segments of speech and nonspeech induce the perception of music in a time-dependent manner. Psychonomic Bulletin & Review 26 (2), pp. 583 - 590 (2019)

Greb, F.; Steffens, J.; Schlotz, W.: Modeling music-selection behavior in everyday life: A multilevel statistical learning approach and mediation analysis of experience sampling data. Frontiers in Psychology 10, 390 (2019)

Wallot, S.: Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – A Method for Quantifying Correlation between Multivariate Time-Series. Multivariate Behavioral Research 54 (2), pp. 173 - 191 (2019)

Belfi, A. M.; Vessel, E. A.; Brielmann, A.; Isik, A. I.; Chatterjee, A.; Leder, H.; Pelli, D. G.; Starr, G.: Dynamics of aesthetic experience are reflected in the default-mode network. NeuroImage 188, pp. 584 - 597 (2019)

Bevilacqua, D.; Davidesco, I.; Wan, L.; Chaloner, K.; Rowland, J.; Ding, M.; Poeppel, D.; Dikker, S.: Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience 31 (3), pp. 401 - 411 (2019)

Menninghaus, W.; Wagner, V.; Wassiliwizky, E.; Schindler, I.; Hanich, J.; Jacobsen, T.; Koelsch, S.: What are aesthetic emotions? Psychological Review 126 (2), pp. 171 - 195 (2019)

Kappert, M. B.; Wuttke-Linnemann, A.; Schlotz, W.; Nater, U. M.: The Aim Justifies the Means—Differences Among Musical and Nonmusical Means of Relaxation or Activation Induction in Daily Life. Frontiers in Human Neuroscience 13, 36 (2019)

Fink , L. K.; Lange, E. B.; Groner , R.: The application of eye-tracking in music research. Journal of Eye Movement Research 11 (2) (2019)

Wallot, S.; Lee, J. T.; Kelty-Stephen, D. G.: Switching between reading tasks leads to phase-transitions in reading times in L1 and L2 readers. PLoS One 14 (2), e0211502 (2019)

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Kuijpers, M.; Douglas, S.; Kuiken, D.: Personality traits and reading habits that predict absorbed narrative fiction reading. Psychology of Aesthetics, Creativity, and the Arts 13 (1), pp. 74 - 88 (2019)