This repository contains analyses of the Hyperface data, specifically the dynamic localizer task. The data were obtained from the open-access database OpenNeuro and include 21 participants. https://openneuro.org/datasets/ds007384/versions/1.0.0
During the task, participants viewed naturalistic 4-second videos from five conditions: faces, bodies, scenes, objects, and scrambled objects.
The main analysis focused on representational similarity analysis (RSA) within the fusiform face area (FFA), a brain region associated with face processing. ROI-RSA analyses were conducted in Python using Nilearn and beta maps estimated with a first-level general linear model (GLM). We mainly explored three representational models: (1) animacy, contrasting faces and bodies with objects, scenes, and scrambled objects; (2) objects versus scenes; and (3) intact versus scrambled objects. We also briefly explored the available metadata. The project includes exploratory whole-brain searchlight classification analyses as well.
The repository includes the code used to organize the data, run the fMRI analyses, generate representational dissimilarity matrices, and visualize the results.
Contributors: Lucia Z-Rivera, Jillian O'Malley, Bailey Harris,Natalia Pallis-Hassani, Heather Laurel Jensen& Emily Fitzgerald