Publikationen von Dirk U. Wulff

Aeschbach, S., Mata, R., & Wulff, D. U. (2025). Mapping mental representations with free associations: A tutorial using the R package associatoR. Journal of Cognition, 8(1), Article 3. https://doi.org/10.5334/joc.407

Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., Griffiths, T. L., Haridi, S., Jagadish, A. K., Ji-An, L., Kipnis, A., Kumar, S., Ludwig, T., Mathony, M., Mattar, M., Modirshanechi, A., Nath, S. S., Peterson, J. C., Rmus, M., Russek, E. M., Saanum, T., Schubert, J. A., Schulze Buschoff, L. M., Singhi, N., Sui, X., Thalmann, M., Theis, F. J., Truong, V., Udandarao, V., Voudouris, K., Wilson, R., Witte, K., Wu, S., Wulff, D. U., Xiong, H., & Schulz, E. (2025). A foundation model to predict and capture human cognition. Nature. Advance online publication. https://doi.org/10.1038/s41586-025-09215-4

Binz, M., Alaniz, S., Roskies, A., Aczel, B., Bergstrom, C. T., Allen, C., Schad, D., Wulff, D. U., West, J. D., Zhang, Q., Shiffrin, R. M., Gershman, S. J., Popov, V., Bender, E. M., Marelli, M., Botvinick, M. M., Akata, Z., & Schulz, E. (2025). How should the advancement of large language models affect the practice of science? Proceedings of the National Academy of Sciences of the United States of America, 122(5), Article e2401227121. https://doi.org/10.1073/pnas.2401227121

Wulff, D. U., & Mata, R. (2025). Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement. Nature Human Behaviour, 9, 944–954. https://doi.org/10.1038/s41562-024-02089-y

Burton, J. W., Lopez-Lopez, E., Hechtlinger, S., Rahwan, Z., Aeschbach, S., Bakker, M. A., Becker, J. A., Berditchevskaia, A., Berger, J., Brinkmann, L., Flek, L., Herzog, S. M., Huang, S., Kapoor, S., Narayanan, A., Nussberger, A.-M., Yasseri, T., Nickl, P., Almaatouq, A., Hahn, U., Kurvers, R. H. J. M., Leavy, S., Rahwan, I., Siddarth, D., Siu, A., Woolley, A. W., Wulff, D. U., & Hertwig, R. (2024). How large language models can reshape collective intelligence. Nature Human Behaviour, 8, 1643–1655. https://doi.org/10.1038/s41562-024-01959-9

Fischer, O., Jeitziner, L. T., & Wulff, D. U. (2024). Affect in science communication: A data-driven analysis of TED Talks on YouTube. Humanities and Social Sciences Communications, 11, Article 80. https://doi.org/10.1057/s41599-023-02247-z

Hussain, Z., Binz, M., Mata, R., & Wulff, D. U. (2024). A tutorial on open-source large language models for behavioral science. Behavior Research Methods, 56, 8214–8237. https://doi.org/10.3758/s13428-024-02455-8

Hussain, Z., Mata, R., & Wulff, D. U. (2024). Novel embeddings improve the prediction of risk perception. EPJ Data Science, 13, Article 38. https://doi.org/10.1140/epjds/s13688-024-00478-x

Lejarraga, T., Ranganathan, K., & Wulff, D. U. (2024). Can simulated experience be harnessed to help people make investment decisions? Judgment and Decision Making, 19, Article e24. https://doi.org/10.1017/jdm.2024.17

Spektor, M. S., & Wulff, D. U. (2024). Predecisional information search adaptively reduces three types of uncertainty. Proceedings of the National Academy of Sciences of the United States of America, 121(47), Article e2311714121. https://doi.org/10.1073/pnas.2311714121