Fig. 4.

The brain is a concept generator. (A) Brodmann areas are shaded to depict their degree of laminar organization, including the insula (bottom right). The brain’s computational architecture is depicted (adapted from Barbas, 2015), where prediction signals flow from the deep layers of less granular regions (cell bodies depicted with triangles) to the upper layers of more granular regions; this, can also be thought of concept construction [as described in Barrett (2017)]. I hypothesize that agranular (i.e. limbic) cortices generatively combine past experiences to initiate the construction of embodied concepts; multimodal summaries cascade to sensory and motor systems to create the simulations that will become motor plans and perceptions. Prediction error processing, in turn, is akin to concept learning. The upper layers of cortex compress prediction errors and reduce error dimensionality, eventually creating multimodal summaries, by virtue of a cytoarchitectural gradient: prediction error flows from the upper layers of primary sensory and motor regions (highly granular cortex) populated with many small pyramidal cells with few connections towards less granular heteromodal regions (including limbic cortices) with fewer but larger pyramidal cells having many connections (Finlay and Uchiyama, 2015). (B) Evidence of conceptual processing in the default mode network: Multimodal summaries for emotion concepts [adapted from Skerry and Saxe (2015), Figure 1B]; summary representations of sensory-motor properties (color, shape, visual motion, sound and physical manipulation [Fernandino et al. (2016), Figure 5]; and, semantic processing [adapted from Binder and Desai (2011), Figure 2]. (C) Regions that consistently increase activity during emotional experience (green), emotion regulation (blue), and their overlap (red) [as appears in Clark-Polner et al. (2016); adapted from Buhle et al. (2014) and Satpute et al. (2015)]. Overlaps are observed in the aIns, vlPFC, the MCC, SMA and posterior superior temporal sulcus. Studies of emotional experience show consistent increase in activity that is consistent with manipulating predictions (i.e. the default mode and salience networks), whereas reappraisal instructions appear to manipulate the modification of those predictions (i.e. the frontoparietal and salience networks). (D) Intensity maps for five emotion categories examined by Wager et al. (2015). Maps represent the expected activations or population centers, given a specific emotion category. Maps also reflect expected co-activation patterns. Notice that population centers for all emotion categories can be found within the default mode and salience networks. These are probabilistic summaries, not brain states for emotion Adapted from Wager et al. (2015).