This dataset contains the raw data associated with the paper Shared neural codes for visual and semantic information about familiar faces in a common representational space by Matteo Visconti di Oleggio Castello, James V. Haxby, and M. Ida Gobbini published in the Proceedings of the National Academy of Sciences.

Fourteen subjects performed a 1-back repetition detection task on faces of personally familiar identities and visually familiar identities over two scanning sessions. Hyperalignment was used to functionally align their data based on a movie dataset, "The Grand Budapest Hotel".

Sample code for the analyses is available at https://github.com/mvdoc/identity-decoding. Additional behavioral data is available at https://osf.io/ehygz/.

For more information, see the corresponding paper at https://doi.org/10.1073/pnas.2110474118.

The Grand Budapest Hotel data is available at https://openneuro.org/datasets/ds003017/. See also the associated paper: Visconti di Oleggio Castello, M., Chauhan, V., Jiahui, G., & Gobbini, M. I. (2020). An fMRI dataset in response to "The Grand Budapest Hotel", a socially-rich, naturalistic movie. Scientific Data, 7(1), 1-9 https://doi.org/10.1038/s41597-020-00735-4

If you use this dataset or the code, please cite the corresponding paper:

Visconti di Oleggio Castello, M., Haxby, J.V., & Gobbini, M.I. Shared neural codes for visual and semantic information about familiar faces in a common representational space. Proceedings of the National Academy of Sciences (2021). https://doi.org/10.1073/pnas.2110474118

Notes

T1 anatomicals were refaced using AFNI's @afni_refacer_run https://afni.nimh.nih.gov/pub/dist/doc/program_help/@afni_refacer_run.html

Contact information

For questions on this dataset, please contact Matteo Visconti di Oleggio Castello (matteo.visconti@berkeley.edu).