What's behind a face: person context coding in fusiform face area as revealed by multivoxel pattern analysis.

The identification of a face comprises processing of both visual features and conceptual knowledge. Studies showing that the fusiform face area (FFA) is sensitive to face identity generally neglect this dissociation. The present study is the first that isolates conceptual face processing by using words presented in a person context instead of faces. The design consisted of 2 different conditions. In one condition, participants were presented with blocks of words related to each other at the categorical level (e.g., brands of cars, European cities). The second condition consisted of blocks of words linked to the personality features of a specific face. Both conditions were created from the same 8 × 8 word matrix, thereby controlling for visual input across conditions. Univariate statistical contrasts did not yield any significant differences between the 2 conditions in FFA. However, a machine learning classification algorithm was able to successfully learn the functional relationship between the 2 contexts and their underlying response patterns in FFA, suggesting that these activation patterns can code for different semantic contexts. These results suggest that the level of processing in FFA goes beyond facial features. This has strong implications for the debate about the role of FFA in face identification.