Recent evidence suggests that the Fusiform Face Area (FFA) is not exclusively dedicated to the interactive processing of face features, but also contains neurons sensitive to local features. This suggests the existence of both interactive and local processing modes, consistent with recent behavioral findings that the strength of interactive feature processing (IFP) engages most strongly when similar features need to be disambiguated. Here we address whether the engagement of the FFA into interactive versus featural representational modes is governed by local feature discriminability. We scanned human participants while they matched target features within face pairs, independently of the context of distracter features. IFP was operationalized as the failure to match the target without being distracted by distracter features. Picture-plane inversion was used to disrupt IFP while preserving input properties. We found that FFA activation was comparably strong, irrespective of whether similar target features were embedded in dissimilar contexts(i.e., inducing robust IFP) or dissimilar target features were embedded in the same context (i.e., engaging local processing). Second, inversion decreased FFA activation to faces most robustly when similar target features were embedded in dissimilar contexts, indicating that FFA engages into IFP mainly when features cannot be disambiguated at a local level. Third, by means of Spearman rank correlation tests, we show that the local processing of feature differences in the FFA is supported to a large extent by the Occipital Face Area, the Lateral Occipital Complex, and early visual cortex, suggesting that these regions encode the local aspects of face information. The present findings confirm the co-existence of holistic and featural representations in the FFA. Furthermore, they establish FFA as the main contributor to the featural/holistic representational mode switches determined by local discriminability.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 January 2013.
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