Figure 3.

Conceptual Structure Effects In The Ventral Visual Pathway. (A) Conceptual structure statistics modulate activity in both the posterior and anterior-medial temporal lobe based on different feature-based statistics. Posterior fusiform activity increases in the lateral posterior fusiform for objects with relatively more shared features, and activity increases in the medial posterior fusiform for objects with relatively fewer shared features. Bilateral anteromedial temporal cortex (AMTC) activity increases for concepts that are semantically more-confusable (reproduced from [42] with permission from MIT press). (B) Increasing damage to the perirhinal cortex (PRC) results in poorer performance for naming semantically more-confusable objects. This is shown by first correlating the naming accuracy of each patient with a conceptual structure measure for the ease of conceptual individuation. This correlation is then related to the degree of damage to the PRC (crosses denote left hemisphere damage; circles denote right hemisphere damage) (reprinted from [43]). (C) Pattern similarity in bilateral PRC is related to conceptual similarity based on semantic features. Semantic similarity can be defined based on overlapping semantic features between concepts, where concepts both cluster by superordinate category and show within-category variability. Testing the relationship between semantic feature similarity and pattern similarity in the brain shows that bilateral PRC similarity patterns also show a clustering by superordinate category and, crucially, within-category differentiation aligned to conceptual similarity (reprinted from [49] with permission from the Society for Neuroscience). (D) The timecourse of superordinate category and basic-level concept information shown with magnetoencephalography (MEG). Using multiple linear regression we can learn how to map between the recorded MEG data and the visual and semantic measures for different objects. After showing how well this model can explain the observed neural data, we asked how accurately the model could predict MEG data for new objects. This showed than the superordinate category of an object can be successfully predicted before the prediction of the basic-level concept (after accounting for the influence of visual statistics) (reprinted from [68] with permission from Oxford University Press).