Works

Preprints

Aeschbach, S., Mata, R., & Wulff, D. U. (2024). Measuring individual semantic networks: A simulation study. arXiv. https://doi.org/10.48550/arXiv.2410.18326

Aeschbach, S., Mata, R., & Wulff, D. U. (2024). Mapping the Mind With Free Associations: A Tutorial Using the R Package associatoR. PsyArXiv. https://doi.org/10.31234/osf.io/ra87s

Peer reviewed work

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., … Hertwig, R. (2024). How large language models can reshape collective intelligence. Nature Human Behaviour, 1–13. https://doi.org/10.1038/s41562-024-01959-9

Wulff, D. U., Aeschbach, S., De Deyne, S., & Mata, R. (2022). Data from the MySWOW proof-of-concept study: Linking individual semantic networks and cognitive performance. Journal of Open Psychology Data, 10(1), 5. https://doi.org/10.5334/jopd.55

Wulff, D. U., De Deyne, S., Aeschbach, S., & Mata, R. (2022). Using Network Science to Understand the Aging Lexicon: Linking Individuals’ Experience, Semantic Networks, and Cognitive Performance. Topics in Cognitive Science, 14(2022), 93–110. https://doi.org/10.1111/tops.12586