Interoceptive inference, emotion, and the embodied self
Authors: Anil K. Seth
Journal: Trends in Cognitive Sciences (2013)

•A new view of emotion as active inference on the causes of interoceptive signals.•Extension of appraisal emotion theories to a contemporary inferential framework.•A unified predictive model of emotion and experience of body ownership.•Interpretation of neuropsychiatric conditions as disordered interoceptive inference.•How predictive integration of interoceptive and exteroceptive signals affects self. The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Bayesian brain and predictive coding approaches within cognitive science. To date, this perspective has been applied primarily to exteroceptive perception (e.g., vision, audition), and action. Here, I describe a predictive, inferential perspective on interoception: 'interoceptive inference' conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents. The model generalizes 'appraisal' theories that view emotions as emerging from cognitive evaluations of physiological changes, and it sheds new light on the neurocognitive mechanisms that underlie the experience of body ownership and conscious selfhood in health and in neuropsychiatric illness. The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Bayesian brain and predictive coding approaches within cognitive science. To date, this perspective has been applied primarily to exteroceptive perception (e.g., vision, audition), and action. Here, I describe a predictive, inferential perspective on interoception: 'interoceptive inference' conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents. The model generalizes 'appraisal' theories that view emotions as emerging from cognitive evaluations of physiological changes, and it sheds new light on the neurocognitive mechanisms that underlie the experience of body ownership and conscious selfhood in health and in neuropsychiatric illness. The view that prediction and error correction provide fundamental principles for understanding brain operation is gaining increasing traction within the cognitive and brain sciences. In the renascent guise of 'predictive coding' (PC – see Glossary) or 'predictive processing', perceptual content is seen as resulting from probabilistic, knowledge-driven inference on the external causes of sensory signals [1Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2639) Google Scholar, 2Friston K.J. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1008) Google Scholar, 3Lee T.S. Mumford D. Hierarchical Bayesian inference in the visual cortex.J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2003; 20: 1434-1448Crossref PubMed Scopus (937) Google Scholar, 4Hohwy J. The Predictive Mind. Oxford University Press, 2013Crossref Google Scholar]. Here, this framework is applied to interoception, the sense of the internal physiological condition of the body [5Craig A.D. How do you feel? Interoception: the sense of the physiological condition of the body.Nat. Rev. Neurosci. 2002; 3: 655-666PubMed Google Scholar], in order to elaborate a model of emotion as 'interoceptive inference' [6Seth A.K. et al.An interoceptive predictive coding model of conscious presence.Front. Psychol. 2011; 2: 395PubMed Google Scholar, 7Seth A.K. Critchley H.D. Extending predictive processing to the body: Emotion as interoceptive inference.Behav. Brain Sci. 2013; 36: 227-228Crossref PubMed Scopus (102) Google Scholar, 8Critchley H. Seth A. Will studies of macaque insula reveal the neural mechanisms of self-awareness?.Neuron. 2012; 74: 423-426Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar, 9Gu X. et al.Anterior insular cortex and emotional awareness.J. Comp. Neurol. 2013; 521: 3371-3388Crossref PubMed Scopus (410) Google Scholar]. Interoceptive predictive coding – equivalently here, interoceptive inference – is hypothesised to engage an extended autonomic neural substrate with emphasis on the anterior insular cortex (AIC) as a comparator. This view extends alternative frameworks for understanding emotion [10James W. The physical basis of emotion.Psychol. Rev. 1894; 1: 516-529Crossref Scopus (285) Google Scholar, 11Damasio A. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harvest Books, 2000Google Scholar, 12Critchley H.D. et al.Neural systems supporting interoceptive awareness.Nat. Neurosci. 2004; 7: 189-195Crossref PubMed Scopus (2442) Google Scholar, 13Craig A.D. How do you feel – now? The anterior insula and human awareness.Nat. Rev. Neurosci. 2009; 10: 59-70Crossref PubMed Scopus (4386) Google Scholar, 14Damasio A. Carvalho G.B. The nature of feelings: evolutionary and neurobiological origins.Nat. Rev. Neurosci. 2013; 14: 143-152Crossref PubMed Scopus (625) Google Scholar] by proposing that emotional content is generated by active 'top-down' inference of the causes of interoceptive signals in a predictive coding context. It also extends previous models of insular cortex as supporting error-based learning of feeling states and uncertainty [15Singer T. et al.A common role of insula in feelings, empathy and uncertainty.Trends Cogn. Sci. 2009; 13: 334-340Abstract Full Text Full Text PDF PubMed Scopus (875) Google Scholar] and as responding to interoceptive mismatches that underlie anxiety [16Paulus M.P. Stein M.B. An insular view of anxiety.Biol. Psychiatry. 2006; 60: 383-387Abstract Full Text Full Text PDF PubMed Scopus (983) Google Scholar]. Representations of physiological conditions have frequently been associated with basic pre-reflective forms of selfhood [11Damasio A. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harvest Books, 2000Google Scholar], with the AIC occupying a central role on some views [13Craig A.D. How do you feel – now? The anterior insula and human awareness.Nat. Rev. Neurosci. 2009; 10: 59-70Crossref PubMed Scopus (4386) Google Scholar]. Selfhood is a constellation concept that involves not only representation and control of physiological homeostasis, but also the experience of owning and identifying with a particular body, the emergence of a first-person perspective, intention and agency, and metacognitive aspects that relate to the subjective 'I' and the narrative linking of episodic memories over time [17Metzinger T. Being No-One. MIT Press, 2003Google Scholar, 18Northoff G. Bermpohl F. Cortical midline structures and the self.Trends Cogn. Sci. 2004; 8: 102-107Abstract Full Text Full Text PDF PubMed Scopus (1069) Google Scholar]. Here, I apply the framework of interoceptive inference to the experience of body ownership (EBO) as a central aspect of selfhood, proposing on the basis of recent data [19Suzuki K. et al.Multisensory integration across interoceptive and exteroceptive domains modulates self-experience in the rubber-hand illusion.Neuropsychologia. 2013; https://doi.org/10.1016/j.neuropsychologia.2013.08.014Crossref Scopus (283) Google Scholar, 20Aspell J.E. et al.Turning body and self inside out: visualized heartbeats alter bodily self-consciousness and tactile perception.Psychol. Sci. 2013; https://doi.org/10.1177/0956797613498395Crossref PubMed Scopus (159) Google Scholar] that EBO is shaped by predictive multisensory integration of self-related signals across interoceptive and exteroceptive domains. Overall, the model described here provides a unified view of self-related processing relevant to emotional awareness and EBO, and carries implications for understanding specific neuropsychiatric disorders. The concept of PC overturns classical notions of perception as a largely 'bottom-up' process of evidence accumulation or feature detection, proposing instead that perceptual content is specified by top-down predictive signals that emerge from hierarchically organized generative models of the causes of sensory signals. According to PC, the brain is continuously attempting to minimize the discrepancy or 'prediction error' between its inputs and its emerging models of the causes of these inputs via neural computations approximating Bayesian inference (Figure 1 and Box 1). Importantly, prediction errors can be minimized either by updating generative models (perceptual inference and learning; changing the model to fit the world) or by performing actions to bring about sensory states in line with predictions (active inference; changing the world to fit the model). In most incarnations these processes are assumed to unfold continuously and simultaneously, underlining a deep continuity between perception and action. Prediction errors are associated with 'precisions', which determine their influence on subsequent hierarchical processing. For example, precision weighting (possibly implemented by post-synaptic gain modulation of prediction error units) can modulate the extent to which prediction errors are resolved by updating generative models or by performing actions [21Friston K.J. et al.Action and behavior: a free-energy formulation.Biol. Cybern. 2010; 102: 227-260Crossref PubMed Scopus (467) Google Scholar]. This leads to an interpretation of attention as the optimization of precision weighting, balancing the relative influence of prediction errors and prior expectations on perceptual inference [2Friston K.J. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1008) Google Scholar].Box 1PC, free energy, and active inferencePC has a long history, originating with the insights of von Helmholtz and reaching recent prominence in the 'Bayesian brain' hypothesis [1Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2639) Google Scholar, 4Hohwy J. The Predictive Mind. Oxford University Press, 2013Crossref Google Scholar]. The idea is that, in order to support adaptive responses, the brain must discover information about the likely causes of sensory signals (i.e., perception) without direct access to these causes, using only information in the flux of sensory signals themselves [2Friston K.J. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1008) Google Scholar]. According to PC, this is accomplished via probabilistic inference on the causes of sensory signals, computed according to Bayesian principles. This means estimating the probable causes of data (the posterior) given observed conditional probabilities (likelihoods) and prior 'beliefs' about probable causes. This, in turn, means inducing a predictive or 'generative' model of the sensory data. Although exact Bayesian inference is computationally challenging and often intractable, a variety of approximate methods exist. Within neuroscience, these approximations have been elaborated in Friston's 'free energy principle' [2Friston K.J. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1008) Google Scholar, 65Friston K.J. A theory of cortical responses.Philos. Trans. R. Soc. Lond. B: Biol. Sci. 2005; 360: 815-836Crossref PubMed Scopus (2557) Google Scholar], which, following seminal work by Hinton and colleagues [66Dayan P. et al.The Helmholtz machine.Neural Comput. 1995; 7: 889-904Crossref PubMed Scopus (763) Google Scholar, 67Hinton G.E. Dayan P. Varieties of Helmholtz Machine.Neural Netw. 1996; 9: 1385-1403Crossref PubMed Scopus (82) Google Scholar], shows how generative models can be induced from data by assuming that the brain minimizes a bound on the evidence for this data (the 'free energy', which under simplifying (Gaussian) assumptions is equivalent to prediction error). The generalization of Bayes theorem to a hierarchical context implies that posteriors at one level form the priors at one level lower, thus enabling priors to be induced from the data stream itself ('empirical' Bayes). Applied to cortical networks, PC interprets bottom-up signals as conveying prediction errors and top-down signals as conveying predictions (Figure 1). Although unequivocal neural evidence for PC is still lacking, a growing body of supportive data details how perceptual content – and underlying neural responses – can be shaped by pre-stimulus expectations [1Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2639) Google Scholar, 4Hohwy J. The Predictive Mind. Oxford University Press, 2013Crossref Google Scholar, 42Egner T. et al.Neural repetition suppression reflects fulfilled perceptual expectations.Nat. Neurosci. 2008; 11: 1004-1006Crossref PubMed Scopus (472) Google Scholar].Key to PC is the minimization of prediction error across hierarchical levels. This can be accomplished either by updating generative models to accommodate unexpected sensory signals or by performing actions to confirm sensory predictions (active inference, [21Friston K.J. et al.Action and behavior: a free-energy formulation.Biol. Cybern. 2010; 102: 227-260Crossref PubMed Scopus (467) Google Scholar]). This duality underlines a strong continuity between perception, action, and imagination [68Clark A. Dreaming the whole cat: generative models, predictive processing, and the enactivist conception of perceptual experience.Mind. 2012; 121: 753-771Crossref Scopus (56) Google Scholar]. Also important is that prediction errors are associated with precisions (Figure 1), so that dynamic precision-weighting (for example by attention) can modulate the balance between top-down and bottom-up signal flow (e.g., low precision on error signals corresponds to high confidence in top-down prior beliefs). The present framework generalizes PC to interoception, proposing that affective states depend on active inference of interoceptive responses. PC has a long history, originating with the insights of von Helmholtz and reaching recent prominence in the 'Bayesian brain' hypothesis [1Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2639) Google Scholar, 4Hohwy J. The Predictive Mind. Oxford University Press, 2013Crossref Google Scholar]. The idea is that, in order to support adaptive responses, the brain must discover information about the likely causes of sensory signals (i.e., perception) without direct access to these causes, using only information in the flux of sensory signals themselves [2Friston K.J. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1008) Google Scholar]. According to PC, this is accomplished via probabilistic inference on the causes of sensory signals, computed according to Bayesian principles. This means estimating the probable causes of data (the posterior) given observed conditional probabilities (likelihoods) and prior 'beliefs' about probable causes. This, in turn, means inducing a predictive or 'generative' model of the sensory data. Although exact Bayesian inference is computationally challenging and often intractable, a variety of approximate methods exist. Within neuroscience, these approximations have been elaborated in Friston's 'free energy principle' [2Friston K.J. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (1008) Google Scholar, 65Friston K.J. A theory of cortical responses.Philos. Trans. R. Soc. Lond. B: Biol. Sci. 2005; 360: 815-836Crossref PubMed Scopus (2557) Google Scholar], which, following seminal work by Hinton and colleagues [66Dayan P. et al.The Helmholtz machine.Neural Comput. 1995; 7: 889-904Crossref PubMed Scopus (763) Google Scholar, 67Hinton G.E. Dayan P. Varieties of Helmholtz Machine.Neural Netw. 1996; 9: 1385-1403Crossref PubMed Scopus (82) Google Scholar], shows how generative models can be induced from data by assuming that the brain minimizes a bound on the evidence for this data (the 'free energy', which under simplifying (Gaussian) assumptions is equivalent to prediction error). The generalization of Bayes theorem to a hierarchical context implies that posteriors at one level form the priors at one level lower, thus enabling priors to be induced from the data stream itself ('empirical' Bayes). Applied to cortical networks, PC interprets bottom-up signals as conveying prediction errors and top-down signals as conveying predictions (Figure 1). Although unequivocal neural evidence for PC is still lacking, a growing body of supportive data details how perceptual content – and underlying neural responses – can be shaped by pre-stimulus expectations [1Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2639) Google Scholar, 4Hohwy J. The Predictive Mind. Oxford University Press, 2013Crossref Google Scholar, 42Egner T. et al.Neural repetition suppression reflects fulfilled perceptual expectations.Nat. Neurosci. 2008; 11: 1004-1006Crossref PubMed Scopus (472) Google Scholar]. Key to PC is the minimization of prediction error across hierarchical levels. This can be accomplished either by updating generative models to accommodate unexpected sensory signals or by performing actions to confirm sensory predictions (active inference, [21Friston K.J. et al.Action and behavior: a free-energy formulation.Biol. Cybern. 2010; 102: 227-260Crossref PubMed Scopus (467) Google Scholar]). This duality underlines a strong continuity between perception, action, and imagination [68Clark A. Dreaming the whole cat: generative models, predictive processing, and the enactivist conception of perceptual experience.Mind. 2012; 121: 753-771Crossref Scopus (56) Google Scholar]. Also important is that prediction errors are associated with precisions (Figure 1), so that dynamic precision-weighting (for example by attention) can modulate the balance between top-down and bottom-up signal flow (e.g., low precision on error signals corresponds to high confidence in top-down prior beliefs). The present framework generalizes PC to interoception, proposing that affective states depend on active inference of interoceptive responses. PC has been elaborated principally in the context of exteroception; that is, to predictive modelling of external states of the world. However, one of the most relevant features of the world for a particular organism is the organism itself [11Damasio A. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harvest Books, 2000Google Scholar, 22Apps M.A. Tsakiris M. The free-energy self: a predictive coding account of self-recognition.Neurosci. Biobehav. Rev. 2013; https://doi.org/10.1016/j.neubiorev.2013.01.029Crossref PubMed Scopus (302) Google Scholar]. This reflects a long-standing notion that mental representations of selfhood are ultimately grounded in representations of the body, with the internal physiological milieu providing a primary reference – a 'material me' [13Craig A.D. How do you feel – now? The anterior insula and human awareness.Nat. Rev. Neurosci. 2009; 10: 59-70Crossref PubMed Scopus (4386) Google Scholar] – that supports adaptive interaction with the environment. From a PC perspective, this implies that an organism should maintain well-adapted predictive models of its own physical body (its position, morphology, etc.) and of its internal physiological condition. This entails inducing generative models of the causes of those signals 'most likely to be me' [22Apps M.A. Tsakiris M. The free-energy self: a predictive coding account of self-recognition.Neurosci. Biobehav. Rev. 2013; https://doi.org/10.1016/j.neubiorev.2013.01.029Crossref PubMed Scopus (302) Google Scholar] across interoceptive and exteroceptive domains, a framework that views emotion as 'interoceptive inference' and provides a unifying mechanism for self-representation at multiple levels, including perhaps especially those related to EBO. Interoceptive concepts of emotion were crystallized by James and Lange [10James W. The physical basis of emotion.Psychol. Rev. 1894; 1: 516-529Crossref Scopus (285) Google Scholar], who argued that emotions arise from perceptions of changes in the body. This approach evolved into 'appraisal' theories, which recognise that explicit cognitions and beliefs about the causes of physiological changes influence subjective feeling states and emotional behaviour [23Gendron M. Barrett L.F. reconstructing the past: a century of ideas about emotion in psychology.Emot. Rev. 2009; 1: 316-339Crossref PubMed Scopus (171) Google Scholar]. Schachter and Singer [24Schachter S. Singer J.E. Cognitive, social, and physiological determinants of emotional state.Psychol. Rev. 1962; 69: 379-399Crossref PubMed Scopus (3394) Google Scholar] famously demonstrated that injections of adrenaline, proximally causing a state of physiological arousal, would give rise to either anger or elation depending on the context (an irritated or elated confederate). This observation was formalized in their 'two factor' theory, in which emotional experience is determined by the combination of physiological change and cognitive appraisal, that is, emotion as interpreted bodily arousal (see [25Cantril H. Hunt W.A. Emotional effects produced by the injection of adrenalin.Am. J. Psychol. 1932; 44: 300-307Crossref Google Scholar] for a precursor). More than a century after James and Lange, there is now a consensus that emotions are psychological states that encompass behavioural, experiential, and visceral changes [23Gendron M. Barrett L.F. reconstructing the past: a century of ideas about emotion in psychology.Emot. Rev. 2009; 1: 316-339Crossref PubMed Scopus (171) Google Scholar, 26Critchley H.D. Harrison N.A. Visceral influences on brain and behavior.Neuron. 2013; 77: 624-638Abstract Full Text Full Text PDF PubMed Scopus (601) Google Scholar, 27Lane R.D. Schwartz G.E. Levels of emotional awareness: a cognitive-developmental theory and its application to psychopathology.Am. J. Psychiatry. 1987; 144: 133-143PubMed Google Scholar, 28Dolan R.J. Emotion, cognition, and behavior.Science. 2002; 298: 1191-1194Crossref PubMed Scopus (1270) Google Scholar]. This attitude underpins several contemporary frameworks for understanding emotion and its relation to cognition and self [11Damasio A. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harvest Books, 2000Google Scholar, 12Critchley H.D. et al.Neural systems supporting interoceptive awareness.Nat. Neurosci. 2004; 7: 189-195Crossref PubMed Scopus (2442) Google Scholar, 13Craig A.D. How do you feel – now? The anterior insula and human awareness.Nat. Rev. Neurosci. 2009; 10: 59-70Crossref PubMed Scopus (4386) Google Scholar], discussed further below. Despite the above insights, interoception has remained generally understood along feed-forward lines, similar to classical evidence accumulation theories of exteroception [23Gendron M. Barrett L.F. reconstructing the past: a century of ideas about emotion in psychology.Emot. Rev. 2009; 1: 316-339Crossref PubMed Scopus (171) Google Scholar, 28Dolan R.J. Emotion, cognition, and behavior.Science. 2002; 298: 1191-1194Crossref PubMed Scopus (1270) Google Scholar]. This assumption is however challenged by evidence of substantial cross-talk between levels of viscerosensory representation, including top-down cortical and behavioural influences to brainstem and spinal centres [26Critchley H.D. Harrison N.A. Visceral influences on brain and behavior.Neuron. 2013; 77: 624-638Abstract Full Text Full Text PDF PubMed Scopus (601) Google Scholar]. Informed by this emerging picture, I suggest that the role of interoception in shaping emotion and selfhood can be productively understood through the lens of PC. In this view, interoceptive inference involves hierarchically cascading top-down interoceptive predictions that counterflow with bottom-up interoceptive prediction errors. Subjective feeling states – experienced emotions – are hypothesized to depend on the integrated content of these predictive representations across multiple levels [6Seth A.K. et al.An interoceptive predictive coding model of conscious presence.Front. Psychol. 2011; 2: 395PubMed Google Scholar]. Following PC principles, interoceptive prediction errors can be suppressed both by modifying predictions and by transcribing these predictions into reference points for autonomic reflexes that regulate physiological homeostasis, as recently suggested by Gu and colleagues [9Gu X. et al.Anterior insular cortex and emotional awareness.J. Comp. Neurol. 2013; 521: 3371-3388Crossref PubMed Scopus (410) Google Scholar]. This role for active inference, which extends previous presentations of this model [6Seth A.K. et al.An interoceptive predictive coding model of conscious presence.Front. Psychol. 2011; 2: 395PubMed Google Scholar, 7Seth A.K. Critchley H.D. Extending predictive processing to the body: Emotion as interoceptive inference.Behav. Brain Sci. 2013; 36: 227-228Crossref PubMed Scopus (102) Google Scholar, 8Critchley H. Seth A. Will studies of macaque insula reveal the neural mechanisms of self-awareness?.Neuron. 2012; 74: 423-426Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar], directly parallels PC formulations for motor control (e.g. [29Adams R.A. et al.Predictions not commands: active inference in the motor system.Brain Struct. Funct. 2013; 218: 611-643Crossref PubMed Scopus (414) Google Scholar]) which highlight descending corticospinal signals as instantiating proprioceptive predictions that engage classical motor reflexes. Precisions play a key role here: descending predictions can engage motor or autonomic reflexes only if the corresponding error signals have diminished impact on hierarchical processing via transiently low precision weighting, which corresponds to decreased attention to these error signals. Without this transient modulation, precise prediction errors would lead to revision of predictions rather than to action [29Adams R.A. et al.Predictions not commands: active inference in the motor system.Brain Struct. Funct. 2013; 218: 611-643Crossref PubMed Scopus (414) Google Scholar]. This implies that active interoceptive inference depends on the selective attenuation of attention to interoceptive prediction errors. Interoceptive predictions arise from multiple hierarchical levels, with higher levels integrating interoceptive, proprioceptive and exteroceptive cues in formulating descending predictions. These multimodal predictions underwrite emotional responses to exteroceptive cues (which may include socially salient signals, see later). In short, interoceptive predictive coding (inference) proposes that emotional content is determined by active inference on the likely internal and external causes of changes in the physiological condition of the body (Figure 2). Although there is not yet any direct confirmatory evidence for interoceptive inference (as for PC generally, see [1Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science.Behav. Brain Sci. 2013; 36: 181-204Crossref PubMed Scopus (2639) Google Scholar]), supportive data are steadily accumulating. Much of these data rest on assuming a central role for the anterior insular cortex (AIC), operating within a rich functional network [30Deen B. et al.Three systems of insular functional connectivity identified with cluster analysis.Cereb. Cortex. 2011; 21: 1498-1506Crossref PubMed Scopus (507) Google Scholar], both as a comparator that registers top-down predictions against bottom-up prediction errors and as a source of anticipatory visceromotor control [6Seth A.K. et al.An interoceptive predictive coding model of conscious presence.Front. Psychol. 2011; 2: 395PubMed Google Scholar, 7Seth A.K. Critchley H.D. Extending predictive processing to the body: Emotion as interoceptive inference.Behav. Brain Sci. 2013; 36: 227-228Crossref PubMed Scopus (102) Google Scholar, 8Critchley H. Seth A. Will studies of macaque insula reveal the neural mechanisms of self-awareness?.Neuron. 2012; 74: 423-426Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar, 9Gu X. et al.Anterior insular cortex and emotional awareness.J. Comp. Neurol. 2013; 521: 3371-3388Crossref PubMed Scopus (410) Google Scholar, 15Singer T. et al.A common role of insula in feelings, empathy and uncertainty.Trends Cogn. Sci. 2009; 13: 334-340Abstract Full Text Full Text PDF PubMed Scopus (875) Google Scholar, 16Paulus M.P. Stein M.B. An insular view of anxiety.Biol. Psychiatry. 2006; 60: 383-387Abstract Full Text Full Text PDF PubMed Scopus (983) Google Scholar]. Structurally, the AIC is ideally placed both to detect and to cause changes in physiological condition, and to integrate interoceptive and exteroceptive signals; functionally, it instantiates interoceptive representations accessible to conscious awareness and is associated with processes that involve visceral representation, interoception, and emotional awareness relevant to selfhood (Box 2).Box 2The anterior insula cortex and interoceptionThe human insular cortex is found bilaterally beneath the temporal and frontal lobes, enjoying widespread bidirectional connectivity to parietal, frontal, and limbic regions [30Deen B. et al.Three systems of insular functional connectivity identif