Using task-optimized neural networks to understand how experience might shape human face perception - Forschungsportal der Justus-Liebig-Universität Gießen:

Meeting Abstract

Using task-optimized neural networks to understand how experience might shape human face perception

Autorenliste: Dobs, K.; Yuan, J.; Martinez, J.; Kanwisher, N

Erschienen in: TeaP 2021 : abstracts of the 63th Conference of Experimental Psychologists

Herausgeberliste: Huckauf, A.; Baumann, M.; Ernst, M.; Herbert, C.; Kiefer, M.; Sauter, M.

Jahr der Veröffentlichung: 2021

Seiten: 61-62

DOI Link: https://doi.org/10.23668/psycharchives.5178

Konferenz: 63th Conference of Experimental Psychologists (TeaP 2021)

Autoren/Herausgeber

- Dr. Katharina Barbara Dobs

Zitierstile

Harvard-Zitierstil: Dobs, K., Yuan, J., Martinez, J. and Kanwisher, N. (2021) Using task-optimized neural networks to understand how experience might shape human face perception, in Huckauf, A., Baumann, M., Ernst, M., Herbert, C., Kiefer, M. and Sauter, M. (eds.) TeaP 2021 : abstracts of the 63th Conference of Experimental Psychologists. Ulm. pp. 61-62. https://doi.org/10.23668/psycharchives.5178

APA-Zitierstil: Dobs, K., Yuan, J., Martinez, J., & Kanwisher, N. (2021). Using task-optimized neural networks to understand how experience might shape human face perception. In Huckauf, A., Baumann, M., Ernst, M., Herbert, C., Kiefer, M., & Sauter, M. (Eds.), TeaP 2021 : abstracts of the 63th Conference of Experimental Psychologists. (pp. 61-62). https://doi.org/10.23668/psycharchives.5178