Browsing by Author "Urnberg, Heidi"

The goal of this thesis was to examine how the representation of faces and identities has been researched with multivariate brain-imaging analysis methods. The coding of the visual identity of faces is a complex process, which arises from abstract high-level representations that are viewpoint independent. This process has been localized to certain visual brain areas, namely FFA and ATL-FA in humans and AM in macaque monkeys. The coding processes of identities have been researched with functional magnetic resonance imaging (fMRI) and multivariate pattern analysis (MVPA) methods, where the measured brain activity distribution is compared with the distributions predicted by the processing models of identity. Most models are based on face spaces, which are defined by the identities, features or abstract properties of a face. The neural basis for coding of the identity of faces has not yet been resolved. However, the low-level and feature models were best at predicting the data, although low-level models explain mainly the properties of the stimuli and the function of V1. The prototype model was not as successful in predicting the activity distributions as the other models. In general, the models explained the orientation of the faces better than their identity. This might be due to the differences between the brain activities associated with different identities being so subtle that the multivariate pattern analysis cannot differentiate between them. It is also possible that the coding of identity does not happen within the face processing areas, but perhaps in the connections between frontal and face areas. To conclude, the usefulness of fMRI-MVPA in studying identity has been questioned and the methods still need improvement. However, MVPA is a more versatile and sensitive method of investigating the coding of identity than traditional univariate analysis methods.