Open Access Publications of the Institute

Köbis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, Article 106553. https://doi.org/10.1016/j.chb.2020.106553

Åkerstedt, T., Lekander, M., Nilsonne, G., Tamm, S., d’Onofrio, P., Kecklund, G., Fischer, H., Schwarz, J., Petrovic, P., & Månsson, K. N. T. (2020). Gray matter volume correlates of sleepiness: A voxel–based morphometry study in younger and older adults. Nature and Science of Sleep, 12, 289–298. https://doi.org/10.2147/NSS.S240493

Al, E., Iliopoulos, F., Forschack, N., Nierhaus, T., Grund, M., Motyka, P., Gaebler, M., Nikulin, V. V., & Villringer, A. (2020). Heart-brain interactions shape somatosensory perception and evoked potentials. Proceedings of the National Academy of Sciences of the United States of America, 117(19), 10575–10584. https://doi.org/10.1073/pnas.1915629117

Albrecht, R., Hoffmann, J. A., Pleskac, T. J., Rieskamp, J., & Helversen, B. von. (2020). Competitive retrieval strategy causes multimodal response distributions in multiple-cue judgments. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(6), 1064–1090. https://doi.org/10.1037/xlm0000772

Allegra, M., Seyed-Allaei, S., Schuck, N. W., Amati, D., Laio, A., & Reverberi, C. (2020). Brain network dynamics during spontaneous strategy shifts and incremental task optimization. NeuroImage, 217, Article 116854. https://doi.org/10.1016/j.neuroimage.2020.116854

Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaïd, M., & Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences of the United States of America, 117(21), 11379–11386. https://doi.org/10.1073/pnas.1917687117

Arnold, M., Oberski, D. L., Brandmaier, A. M., & Voelkle, M. C. (2020). Identifying heterogeneity in dynamic panel models with individual parameter contribution regression. Structural Equation Modeling, 27(4), 613–628. https://doi.org/10.1080/10705511.2019.1667240

Arrebola-Moreno, M., Petrova, D., García-Retamero, R., Rivera-López, R., Jordan-Martínez, L., Arrebola, J. P., Ramírez-Hernández, J. A., & Catena, A. (2020). Psychological and cognitive factors related to prehospital delay in acute coronary syndrome: A systematic review. International Journal of Nursing Studies, 108, Article 103613. https://doi.org/10.1016/j.ijnurstu.2020.103613

Arslan, R. C., Brümmer, M., Dohmen, T., Drewelies, J., Hertwig, R., & Wagner, G. G. (2020). How people know their risk preference. Scientific Reports, 10(1), Article 15365. https://doi.org/10.1038/s41598-020-72077-5

Arslan, R. C., Reitz, A. K., Driebe, J. C., Gerlach, T. M., & Penke, L. (2020). Routinely randomize potential sources of measurement reactivity to estimate and adjust for biases in subjective reports. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000294

Arslan, R. C., Walther, M. P., & Tata, C. S. (2020). formr: A study framework allowing for automated feedback generation and complex longitudinal experience-sampling studies using R. Behavior Research Methods, 52(1), 376–387. https://doi.org/10.3758/s13428-019-01236-y

Awad, E., Dsouza, S., Shariff, A., Rahwan, I., & Bonnefon, J.-F. (2020). Universals and variations in moral decisions made in 42 countries by 70,000 participants. Proceedings of the National Academy of Sciences of the United States of America, 117(5), 2332–2337. https://doi.org/10.1073/pnas.1911517117

Awad, E., Levine, S., Kleiman-Weiner, M., Dsouza, S., Tenenbaum, J. B., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2020). Drivers are blamed more than their automated cars when both make mistakes. Nature Human Behaviour, 4, 134–143. https://doi.org/10.1038/s41562-019-0762-8

Bach, D. R., & Melinscak, F. (2020). Psychophysiological modelling and the measurement of fear conditioning. Behaviour Research and Therapy, 127, Article 103576. https://doi.org/10.1016/j.brat.2020.103576

Baumann, F., Lorenz-Spreen, P., Sokolov, I. M., & Starnini, M. (2020). Modeling echo chambers and polarization dynamics in social networks. Physical Review Letters, 124(4), Article 048301. https://doi.org/10.1103/PhysRevLett.124.048301

Baumert, J., Fleckenstein, J., Leucht, M., Köller, O., & Möller, J. (2020). The long-term proficiency of early, middle, and late starters learning English as a foreign language at school: A narrative review and empirical study. Language Learning, 70(4), 1091–1135. https://doi.org/10.1111/lang.12414

Becker, M., Sommer, T., & Kühn, S. (2020). Verbal insight revisited: FMRI evidence for early processing in bilateral insulae for solutions with AHA! experience shortly after trial onset. Human Brain Mapping, 41(1), 30–45. https://doi.org/10.1002/hbm.24785

Becker, M., Sommer, T., & Kühn, S. (2020). Inferior frontal gyrus involvement during search and solution in verbal creative problem solving: A parametric fMRI study. NeuroImage, 206, Article 116294. https://doi.org/10.1016/j.neuroimage.2019.116294

Becker, M., Tetzner, J., & Baumert, J. (2020). Schulformen und sozioökonomischer Erfolg im jungen Erwachsenenalter: Werden unterschiedliche Ausbildungswege auf dem Arbeitsmarkt gleich honoriert? Zeitschrift für Erziehungswissenschaft, 23, 979–1017. https://doi.org/10.1007/s11618-020-00973-7

Bender, A. R., Brandmaier, A. M., Düzel, S., Keresztes, A., Pasternak, O., Lindenberger, U., & Kühn, S. (2020). Hippocampal subfields and limbic white matter jointly predict learning rate in older adults. Cerebral Cortex, 30(4), 2465–2477. https://doi.org/10.1093/cercor/bhz252