Publikationen 2019


  1. Pallavicini, C.; Vilas, M. G.; Villarreal, M.; Zamberlan, F.; Muthukumaraswamy, S.; Nutt, D.; Carhart-Harris, R.; Tagliazucchi, E.: Spectral signatures of serotonergic psychedelics and glutamatergic dissociatives. NeuroImage 200, S. 281 - 291 (2019)
  2. Vessel, E. A.; Isik, A. I.; Belfi, A. M.; Stahl, J. L.; Starr, a. G. G.: The default-mode network represents aesthetic appeal that generalizes across visual domains. Proceedings of the National Academy of Sciences of the United States of America (2019)
  3. Akkermans, J.; Schapiro, R.; Mullensiefen, D.; Jakubowski, K.; Shanahan, D.; Baker, D.; Busch, V.; Lothwesen, K.; Elvers, P.; Fischinger, T. et al.; Schlemmer, K.; Frieler, K.: Decoding emotions in expressive music performances: A multi-lab replication and extension study. Cognition & Emotion 33 (6), S. 1099 - 1118 (2019)
  4. Lei, J.; Conradi, N.; Abel, C.; Frisch, S.; Brodski-Guerniero, A.; Hildner, M.; Kell, C. A.; Kaiser, J.; Schmidt-Kassow, M.: Cognitive effects of rhythmic auditory stimulation in Parkinson’s disease: A P300 study. Brain Research 1716, S. 70 - 79 (2019)
  5. Assaneo, M. F.; Rimmele, J. M.; Orpella, J.; Ripollés, P.; de Diego-Balaguer, R.; Poeppel, D.: The lateralization of speech-brain coupling is differentially modulated by intrinsic auditory and top-down mechanisms. Frontiers in Integrative Neuroscience (2019)
  6. Hoshi, H.; Kwon, N.; Akita, K.; Auracher, J.: Semantic associations dominate over perceptual associations in vowel–size iconicity. i-Perception 10 (4) (2019)
  7. Rimmele, J. M.; Gudi-Mindermann, H.; Nolte, G.; Roeder, B.; Engel, K. A.: Working memory training integrates visual cortex into beta-band networks in congenitally bind individuals. NeuroImage 194, S. 259 - 271 (2019)
  8. Schlotz, W.: Investigating associations between momentary stress and cortisol in daily life: What have we learned so far? Psychoneuroendocrinology 105, S. 105 - 116 (2019)
  9. Wald-Fuhrmann, M.: Komplement und Korrektiv: Empirie als Teil einer transdisziplinären Musikästhetik. Musik & Ästhetik 23 (91), S. 84 - 88 (2019)
  10. Menninghaus, W.; Wagner, V.; Kegel, V.; Knoop, C. A.; Schlotz, W.: Beauty, elegance, grace, and sexiness compared. PLOS ONE 14 (6), S. e0218728 (2019)
  11. Lewis, G. A.; Poeppel, D.; Murphy, G. L.: Contrasting semantic versus inhibitory processing in the angular gyrus: An fMRI study. Cerebral Cortex 29 (6), S. 2470 - 2481 (2019)
  12. 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), S. 855 - 873 (2019)
  13. Tavano, A.; Poeppel, D.: A division of labor between power and phase coherence in encoding attention to stimulus streams. NeuroImage 193, S. 146 - 156 (2019)
  14. 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), S. 10113 - 10121 (2019)
  15. 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), S. e0216930 (2019)
  16. Toelle, J.; Sloboda, J. A.: The audience as artist? The audience’s experience of participatory music. Musicae Scientiae (2019)
  17. 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), S. 393 - 405 (2019)
  18. Larrouy-Maestri, P.; Harrison, P. M. C.; Müllensiefen, D.: The mistuning perception test: A new measurement instrument. Behavior Research Methods 51 (2), S. 663 - 675 (2019)
  19. 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), S. 583 - 590 (2019)
  20. Wallot, S.: Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – A Method for Quantifying Correlation between Multivariate Time-Series. Multivariate Behavioral Research 54 (2), S. 1 - 19 (2019)
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