Selected Publications
- Fitsch, H., Kaiser Trujillo, A., Plümecke, T. (2021). Sex/Gender in the Brain: Politics ofNeuroscience. Journal Science for the People. Bio-Politics, volume: 23, issue: 3
- Kaiser, A., Eppenberger, L., Kuenzli, E., Borgwardt, S., Radue, E.W., … Bendfeldt, K. (2015). Ageof second language acquisition in multilinguals has an impact on grey matter volume inlanguage-associated brain areas. Frontiers in Psychology, 6, 638.
- Schellenberg, D. & Kaiser, A. (2017). The sex/gender distinction: Beyond F and M. In C. Travis & J. W. White (Eds.), APA Handbook of the Psychology of Women (pp 165-187). Washington,DC: American Psychological Association.
- Joel, D., Kaiser, A., Richardson, S., Ritz, S., Roy, D., and Subramaniam, B. (2015). Lab Meeting: A Discussion on experiments and experimentation. Catalyst: Feminism, Theory, Technoscience,1(1), 1-12. www.catalystjournal.org
- Bryant, K., Grossi, G., Kaiser, A. (2019). Feminist Interventions on the Sex/Gender Question in Broca’s Area. The Scholar & Feminist Online; published by the Barnard Center for research onwomen.
FRIAS Project
Sex/Gender in Computer-Based Neuroscience: The Role of Big Data
This research project is the continuation of a project that examines whether and how novel approaches in data processing and computer-based neuroscience contribute to answering the question of neurobiological sex/gender differences and similarities. This project includes the disciplines of gender studies, cognitive science, MRI-neuroscience and computer science, disciplines hampered by their different – and sometimes even antagonistic – understandings of knowledge production. By applying several approaches, I aim to bridge this interdisciplinary gap. In the 10-months research project presented here, two detected research shortcomings, sex/gender as a co-variate and the overproduction of sex/gender differences, will be furtherdeveloped. The shortcoming of sex/gender as a co-variate will be addressed by a quantitative meta-analysis, more specifically, an activation likelihood estimation meta-analysis (A). The second shortcoming, the overproduction of sex/gender differences, will be explored by the calculation of a “correction factor” (B). Furthermore, at a neuro-empirical level, a new tool, the ultra-fast technique MR-encephalography, will be applied to examine whether this dynamic method differs from rather “static” techniques showing bilateral functional activation for women and a lateral activation patterns for men (C). Lastly, the role of “big data” against the backdrop of sex/gender in neuroscience will be examined because the paradigmatic change we are facing in neuroscience – from small to large data – holds interesting possibilities for the examination of sex/gender in neuroscience.