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A common role of insula in feelings, empathy and uncertainty

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Although accumulating evidence highlights a crucial role of the insular cortex in feelings, empathy and processing uncertainty in the context of decision making, neuroscientific models of affective learning and decision making have mostly focused on structures such as the amygdala and the striatum. Here, we propose a unifying model in which insula cortex supports different levels of representation of current and predictive states allowing for error-based learning of both feeling states and uncertainty. This information is then integrated in a general subjective feeling state which is modulated by individual preferences such as risk aversion and contextual appraisal. Such mechanisms could facilitate affective learning and regulation of body homeostasis, and could also guide decision making in complex and uncertain environments.

Introduction

Human insular cortex is a large and highly interconnected structure embedded deep in the brain (Box 1). Despite neuroimaging observations of insula engagement across multiple tasks, the contribution of this region has been largely neglected within neuroscientific theories and models. The emergence of affective and social neuroscience has accumulated evidence implicating insular cortex, particularly its most anterior portion (the anterior insula, AI), in visceral representation and emotional experience. Commentaries emphasize the activation of AI by motivationally important changes in bodily states as diverse as autonomic arousal, sensual touch, air hunger, taste, craving and pain 1, 2, 3, 4, 5, 6. Furthermore, AI activity reflects the subjective intensity of both one's own and others’ emotional experiences, for example, when one experiences distress, pain or disgust [7] or when one empathizes with others who are in these emotional states 8, 9, 10. Finally, neuroeconomics, which studies motivational decision making, has recently strongly linked AI activity to processing, representing and learning information about risk and uncertainty 11, 12, 13, 14.

Until now, however, no model of insula function has integrated its contribution to physiological and emotional states with empathic understanding or behavioural risk processing. Here, we propose a novel account of the role of insula in which predictions and realizations of bodily and affective states are integrated with predictions and realizations of uncertainty, to engender an integrated feeling state that is shaped by individual risk preference and appraisal of the context. Before outlining our model and its implications, we will first summarize relevant studies illustrating insula involvement in: (i) bodily awareness, (ii) self-related and empathic feelings, (iii) risk and uncertainty processing (for earlier extensive reviews of the literature in these respective domains, see for example Refs. 1, 2, 4, 15, 16, 17, 18, 19).

Section snippets

The interoceptive cortex: insula and feelings

Insular cortex is broadly acknowledged as viscerosensory cortex, and implicated in mapping internal bodily states (including pain and taste) and in representing emotional arousal and feelings 1, 2, 4. Early ‘peripheral’ theories of emotion argue that visceral and somatic changes within the body underlie emotional feeling states and provide emotional colour to perception [20]. Thus, when exposed to a spider, a perceived change in one's heartbeat can trigger the experience of fear. Damasio [21]

The interoceptive cortex and its role in empathy

Independent evidence linking insular cortex to predictive feeling states also stems from research in social neuroscience, focusing on the neural basis of empathy – our ability to share and understand the emotional states of others (reviewed in Refs 16, 17, 18, 19). Functional neuroimaging studies demonstrate overlapping patterns of brain activity during the subjective and the vicarious experience of emotions or sensations. For example, bilateral AI activation is consistently observed both when

The role of anterior insula in uncertainty and uncertainty prediction

Research associated to Neuroeconomics, which explores the neural basis of motivational decision making, suggest that AI is involved in processing and learning information about risk and uncertainty (see Box 3 for definition of risk and uncertainty; for simplicity, we use the terms interchangeably). ‘Uncertainty’ describes the inability to fully predict an outcome. Most organisms are sensitive to uncertainty. For instance, someone might forego possible large rewards in favour of smaller, but

Towards an integrative model

A comprehensive explanatory account of AI function must be able to link these three lines of evidence. We propose a model in which insular cortex integrates external sensory and internal physiological signals with computations about their uncertainty. Through AI, this integration is expressed as a dominant feeling state that modulates social and motivational behaviour in conjunction with bodily homeostasis. This model is schematically depicted in Figure 2.

The model differentiates several

Current evidence and model prediction

Our model proposes the existence of predictive feeling states, current feeling states and feeling state prediction errors that support the error-based learning of feeling states in insula (Figure 2a). Along with the analogous uncertainty representations, these are likely in spatially distinct but close neural circuits in insula.

Studies of pain and pleasant touch suggest that, across insular cortex, primary representations are topographically distinct from predictive and empathic

Conclusion

Affective neuroscience, social neuroscience and neuroeconomics demonstrate that insular cortex, particularly AI, is involved in processing subjective feelings, empathy and uncertainty. We propose an account of insular cortex function in which sensory, affective and bodily information is integrated with information about uncertainty to generate a dominant subjective feeling state. Learning about feeling states is supported by insular representations of current feeling states, predicted feeling

Acknowledgements

We thank Klaas Enno Stephan and Peter Bossaerts for useful comments on the present paper. H.D.C. was supported by a programme grant from the Wellcome Trust.

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