# Familiarity Processing through Faces and Names: Insights from Multivoxel Pattern Analysis
## 1 Introduction
[PERSON] (2012) extensively reviewed both case and group studies examining the effect of lateralized left or right temporal lobe lesions on the recognition of famous people. The author concluded that the data were most consistent with a modality-specific proposal [16]. Also, early neuroimaging studies using PET [17] and fMRI [18] identified distinct but sometimes overlapped activations for faces and names in familiarity processing, suggesting the access to a common familiarity knowledge system after a pre-semantic processing stage. Activation patterns, regardless of stimulus modality, involved extensive left hemisphere networks and bilateral activity in the hippocampus, posterior cingulate, and middle temporal gyrus [17; 18]. Famous faces activated the right hemisphere and famous names were more left- lateralized. These studies indicate that a set of shared regions typically associated with the retrieval of biographical knowledge and social-affective processing appears to participate in familiarity processing regardless of the modality of presentation. However, studies using activation-based methods may not be the optimal way to address this problem. Importantly, univariate statistical models do not encode relationships between voxels [19; 20]. Instead, each voxel activation value is modeled separately, to detect brain regions that respond more strongly during one experimental condition than during another. Also, in classical brain mapping analyses, the data are typically spatially smoothed to focus sensitivity on the overall activations of functional regions. Therefore, population-code information reflected in subtle differences between nearby voxels, such as the difference between an unfamiliar and a familiar face (or between an unfamiliar and a familiar name), may be lost [19]. Alternatively, in multivariate analysis, multiple responses are jointly tested for differences between experimental conditions. This approach has opened new questions and ways of thinking about the neural representation of familiarity, such as those from the study from [PERSON] and [PERSON] (2015), which were difficult or even impossible to formulate from a traditional univariate point of view. While classical activation analysis is aimed at revealing a region's involvement in a certain cognitive function, MVPA, on the contrary, pursues looking into each region and revealing its \"representational content\" by testing for combinatorial effects [13]. Our experimental design focused on assessing neural responses to familiar persons with a high degree of familiarity; specifically, individuals with whom the participants shared close relationships. While this approach provides valuable insights into the neural mechanisms underlying familiarity processing, we acknowledge the importance of extending our investigations to encompass varying degrees of familiarity. Future studies could explore neural responses to persons of varying familiarity levels, including less familiar or famous persons, to elucidate the nuanced nature of familiarity processing. The consideration of a broader familiarity spectrum will contribute to a more comprehensive understanding of the neural underpinnings of person recognition. In our study, we address the question of how our brain processes familiarity from faces and names. We employed searchlight multivoxel pattern analysis (MVPA) to investigate two contrasting hypotheses. Firstly, we explored whether multivoxel patterns associated with familiarity could be detected in distinct brain regions, as indicated by the precise decoding of familiarity for both faces and names. Some of these patterns may reveal shared characteristics between the two modalities, supported by successful cross-decoding from faces to names. Secondly, we examined the possibility that familiarity could be accurately decoded for a particular modality in specific regions where common patterns do not exist, leading to cross-decoding failures.