The theory of constructed emotion: an active inference account of interoception and categorization

Abstract

The science of emotion has been using folk psychology categories derived from philosophy to search for the brain basis of emotion. The last two decades of neuroscience research have brought us to the brink of a paradigm shift in understanding the workings of the brain, however, setting the stage to revolutionize our understanding of what emotions are and how they work. In this article, we begin with the structure and function of the brain, and from there deduce what the biological basis of emotions might be. The answer is a brain-based, computational account called the theory of constructed emotion.

Keywords: affect; categorization; concepts; construction; emotion; interoception; predictive coding.

Fig. 1.

The classical view of emotion. The classical view of emotion includes basic emotion theories (e.g. for a review, see Tracy and Randles, 2011), causal appraisal theories (e.g. Scherer, 2009; Roseman, 2011), and theories of emotion that rely on black-box functionalism (Davis, 1992; Anderson and Adolphs, 2014). Each emotion faculty is assumed to have its own innate ‘essence’ that distinguishes it from all other emotions. This might be a Lockean essence (an underlying causal mechanism that all instances of an emotion category share, making them that kind of emotion and not some other kind of emotion, depicted by the circles in the figure). Lockean essences might be a biological, such as a set of dedicated neurons, or psychological, such as a set of evaluative mechanisms called ‘appraisals’. An emotion category is usually assumed to have a Platonic essence [a physical fingerprint that instances of that emotion share, but that other emotions do not, such a set of facial movements (an ‘expression’), a pattern of autonomic nervous system activity, and/or a pattern of appraisals]. Of course, no one is expecting complete invariance, but it is assumed that instances of a category are similar enough to be easily diagnosed as the same emotion using objective (perceiver-independent) measures alone. (A) is adapted from Davis (1992). (B) is adapted Anderson and Adolphs (2014). (C) is adapted from Barrett (2006a), which reviews the growing evidence that contracts the classical view of emotion.

Fig. 2.

Hubs in the human brain. (A) Hubs of the rich club, adapted from van den Heuvel and Sporns (2013). These regions are strongly interconnected with one another and it is proposed that they integrate information across the brain to create large-scale patterns of information flow (i.e. synchronized activity; van den Heuvel and Sporns, 2013). They are sometimes referred to as convergence or confluence zones (e.g. Damasio, 1989; Meyer and Damasio, 2009). (B) Results of a forward inference analysis, revealing ‘hot spots’ in the brain that show a better than chance increase in BOLD signal across 5633 studies from the Neurosynth database. Activations are thresholded at FWE P < 0.05. Limbic regions (i.e. agranular/dysgranular with descending projections to visceromotor control nuclei) include the cingulate cortex [midcingulate cortex (MCC), pregenual anterior cingulate cortex (pgACC)], ventromedial prefrontal cortex (vmPFC), supplementary motor and premotor areas (SMA and PMC), medial temporal lobe, the anterior insula (aINS) and ventrolateral prefrontal cortex (vlPFC) (e.g. Carrive and Morgan, 2012; Bar et al., 2016); for a discussion and additional references, see (Kleckner et al., in press). AG, angular gyrus; MC, motor cortex.

Fig. 3.

Neural activity during simulation. N = 16 (data from Wilson-Mendenhall et al., 2013). Participants listened with eyes closed to multimodal descriptions rich in sensory details and imagined each real-world scenario as if it was actually happening to them (i.e. the experiences were high in subjective realism). Contrast presented is scenario immersion > resting baseline; maps are FDR corrected P < 0.05. Left image, x = 1; right image, x = −42. Heightened neural activity in primary visual cortex (not labeled), somatosensory cortex (SSC), and MC during scenario immersion replicated prior simulation research (McNorgan, 2012) and established the validity of the paradigm. Notice that simulation was associated with an increase in BOLD response within primary interoceptive cortex (i.e. the pINS), in the sensory integration network of lateral orbitofrontal cortex (lOFC) (Ongur et al., 2003) and in the thalamus; increased BOLD responses were also seen, as expected, in limbic and paralimbic regions such as the vmPFC, the aINS, the temporal pole (TP), SMA and vlPFC, as well as in the hypothalamus and the subcortical nuclei that control the internal milieu. PAG, periacquiductal gray; PBN, parabrachial nucleus.

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).

Fig. 5.

A depiction of predictive coding in the human brain. (A) Key limbic and paralimbic cortices (in blue) provide cortical control the body’s internal milieu. Primary MC is depicted in red, and primary sensory regions are in yellow. For simplicity, only primary visual, interoceptive and somatosensory cortices are shown; subcortical regions are not shown. (B) Limbic cortices initiate visceromotor predictions to the hypothalamus and brainstem nuclei (e.g. PAG, PBN, nucleus of the solitary tract) to regulate the autonomic, neuroendocrine, and immune systems (solid lines). The incoming sensory inputs from the internal milieu of the body are carried along the vagus nerve and small diameter C and Aδ fibers to limbic regions (dotted lines). Comparisons between prediction signals and ascending sensory input results in prediction error that is available to update the brain’s internal model. In this way, prediction errors are learning signals and therefore adjust subsequent predictions. (C) Efferent copies of visceromotor predictions are sent to MC as motor predictions (solid lines) and prediction errors are sent from MC to limbic cortices (dotted lines). (D) Sensory cortices receive sensory predictions from several sources. They receive efferent copies of visceromotor predictions (black lines) and efferent copies of motor predictions (red lines). Sensory cortices with less well developed lamination (e.g. primary interoceptive cortex) also send sensory predictions to cortices with more well-developed granular architecture (e.g. in this figure, somatosensory and primary visual cortices, gold lines). For simplicity’s sake, prediction errors are not depicted in panel D. sgACC, subgenual anterior cingulate cortex; vmPFC, ventromedial prefrontal cortex; pgACC, pregenual anterior cingulate cortex; dmPFC, dorsomedial prefrontal cortex; MCC, midcingulate cortex; vaIns, ventral anterior insula; daIns, dorsal anterior insula and includes ventrolateral prefrontal cortex; SMA, supplementary motor area; PMC, premotor cortex m/pIns, mid/posterior insula (primary interoceptive cortex); SSC, somatosensory cortex; V1, primary visual cortex; and MC, motor cortex (for relevant neuroanatomy references, see Kleckner et al., in press).

Fig. 6.

A large-scale system for allostasis and interoception in the human brain. (A) The system implementing allostasis and interoception is composed of two large-scale intrinsic networks (shown in red and blue) that are interconnected by several hubs (shown in purple; for coordinates, see Kleckner et al., in press). Hubs belonging to the ‘rich club’ are labeled. These maps were constructed with resting state BOLD data from 280 participants, binarized at p < 10⁻⁵, and then replicated on a second sample of 270 participants. vaIns, ventral anterior insula; MCC, midcingulate cortex; PHG, parahippocampal gyrus; PostCG, postcentral gyrus; PAG, periaqueductal gray; PBN, parabrachial nucleus; NTS, the nucleus of the solitary tract; vStriat., ventral striatum; Hypothal, hypothalamus. (B) Reliable subcortical connections, thresholded P < 0.05 uncorrected, replicated in 270 participants.