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Year: 2009
A common role of insula in feelings, empathy and uncertainty
Singer, T; Critchley, H D; Preuschoff, K
Singer, T; Critchley, H D; Preuschoff, K (2009). A common role of insula in feelings, empathy and uncertainty.Trends in Cognitive Sciences, 13(8):334-440.Postprint available at:http://www.zora.uzh.ch
Posted at the Zurich Open Repository and Archive, University of Zurich.http://www.zora.uzh.ch
Originally published at:Trends in Cognitive Sciences 2009, 13(8):334-440.
Singer, T; Critchley, H D; Preuschoff, K (2009). A common role of insula in feelings, empathy and uncertainty.Trends in Cognitive Sciences, 13(8):334-440.Postprint available at:http://www.zora.uzh.ch
Posted at the Zurich Open Repository and Archive, University of Zurich.http://www.zora.uzh.ch
Originally published at:Trends in Cognitive Sciences 2009, 13(8):334-440.
1
24/01/09
A common role of insula in feelings, empathy and uncertainty
Tania Singer1,2, Hugo D. Critchley3, Kerstin Preuschoff2
1Laboratory for Social and Neural Systems Research, University of Zurich, Switzerland
2Institute for Empirical Research in Economics, University of Zurich, Switzerland
3Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Universities of
Brighton and Sussex, UK
Type of Article: Opinion Paper
Corresponding Author:
Prof. Dr. Tania Singer Laboratory for Social and Neural Systems Research Institute for Empirical Research in Economics University of Zurich Bluemlisalpstrasse 10 CH-8006 Zurich E-Mail: [email protected]
2
Abstract
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.
Words: 119
3
Introduction
Human insular cortex is a large and highly interconnected structure embedded deep in the
brain (see 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 pain1-6. Furthermore, AI activity reflects the
subjective intensity of both one’s own and others’ emotional experiences, for example, when
one experiences distress, pain or disgust7 or when one empathizes with others who are in
these emotional states8-10. Finally, neuroeconomics which studies motivational decision
making, has recently strongly linked AI activity to processing, representing and learning
information about risk and uncertainty11-14.
Until now, however, no model of insula/AI 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/AI 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 summarise relevant studies illustrating insula/AI involvement in: a) bodily
awareness, b) self-related and empathic feelings, c) risk and uncertainty processing (for earlier
extensive reviews of the literature in these respective domains, see for example Refs. 1,2,4,15-
19).
[ INSERT BOX 1 ]
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The interoceptive cortex: Insula and feelings
Insular cortex is broadly acknowledged as viscerosensory cortex, and implicated in mapping
internal bodily state (including pain and taste) and in representing emotional arousal and
feelings1,2,4. Early ‘peripheral’ theories of emotion argue that visceral and somatic changes
within the body underlie emotional feeling states and provide emotional colour to
perception20: Thus, when exposed to a spider, a perceived change in one’s heart beat can
trigger the experience of fear. Damasio21 and colleagues revived neurobiological interest in
these theories suggesting that insular cortex plays a role in mapping visceral states that are
associated with emotional experience, giving rise to conscious feelings (see Box 2). This
notion was recently extended by Craig who argued that sensory information about the body’s
physiological state is mapped in insula and re-represented in AI (particularly in right AI)
where it becomes consciously accessible, enabling a subjective affective experience or feeling
state1,2. Accordingly, enhanced AI activity is observed during high physiological arousal and
during declarative awareness of changes in bodily states4. Differences in AI size and
reactivity is linked to awareness of bodily responses (such as one’s heartbeat) and to the
general experience of anxiety symptoms22, suggesting that the link between emotional
feelings and conscious perception of bodily response20,21,23 is strongest for anxiety (see Figure
1, Panels A and B).
[ INSERT BOX 2]
In a neurobiological model of anxiety, Paulus and Stein24 suggested that insula computes an
‘interoceptive prediction error’, signalling mismatch between actual and anticipated bodily
arousal, evoking subjective anxiety and avoidance behaviour. This interoceptive signal is
integrated with other information processing streams, such that AI activation is engaged by
5
the joint processing of internal arousal and conscious representation of threat (relevant to ‘two
stage’ models of emotion23, Box 2) and enhanced in anxiety-prone individuals during
emotion- or risk-processing tasks13,25. Moreover, when ‘interoceptive mismatch’ is induced
experimentally using false physiological feedback, enhanced AI activity correlates with
increased emotional salience attributed to previously unthreatening neutral stimuli26.
These findings extend the role of insula/AI cortex beyond representation of current
physiological or emotional feeling states and suggest a role in behaviourally-relevant
computation of predictive states and prediction errors. Also, they suggest through anxiety a
connection between feelings and risk, a crucial point for the model described below.
[ INSERT FIGURE 1]
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. 16-19). 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 one experiences pain and when one observes pain in other
people (see Figure 1, Panel B). Such empathic engagement of AI also reflects dispositional
empathy scores and individual trial-by-trial empathy ratings8-10,27,28 (reviewed in Ref. 16).
Bilateral AI is similarly engaged when one tastes or sees other people taste, unpleasant and,
notably, pleasant drinks8.
6
Singer and others10 proposed a ‘dual function’ for insular representations of bodily states:
First, the primary mapping of internal states forms the basis for predictions of physiological
reactions to emotional stimuli with respect to the self (subjective feeling states). Second, these
predictive representations permit the simulation of how the same emotional stimuli feel to
others (empathic feeling states).
This account predicts that impaired access to one’s own emotional state will cause empathic
deficits. Alexithymia describes the difficulty in identifying and describing emotional feelings,
despite a basic awareness of bodily sensations and arousal. When alexithymic individuals
focus on their emotions, the failure to engage AI reflects both the degree of alexithymia and
deficiencies in trait empathy29. Further studies of empathic brain responses in alexithymic
patients may confirm that deficits in empathy emerge from a failure to simulate forward
representations of bodily states within AI.
This research implicates insula/AI in the representation of predictive feeling states used both
to anticipate emotional events impacting one’s own body and to understand the emotional
experience of others. It also points to a central role of insular cortex in vicarious learning.
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 processes and learns 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 may forego possible large rewards in favour of smaller,
but less uncertain, rewards (risk aversion). Imaging studies report increased AI signals when
making risky decisions (gambles) compared to safe decisions13 and in response to increasing
7
task ‘instability’30, complexity31 and ambiguity11, which engender uncertainty through lack of
knowledge. Mathematical tools derived from economics permit a mechanistic description of
how AI represents risk and uncertainty: In a decision-making task, if the probability and
magnitude of each potential outcome are known, the precise risk of each decision can be
calculated. This methodology does not require a subjective evaluation of risk, yet explains
risk-taking behaviour across species32.
INSERT BOX 3: What is risk and how is it assessed?
Preuschoff and co-workers33 applied this model to examine neural substrates of risk
processing: Using a gambling task, activity within bilateral AI was observed to encode the
computed risk prediction when waiting for the outcome of a risky decision and to reflect the
risk prediction error once the outcome was known. This error signal compares predicted risk
to realized risk34 (see Figures 1, Panel D). Through these two levels of representations of
uncertainty information, AI can guide choice selection in risk-sensitive individuals and
modulate learning rates in uncertain environments. Behaviourally, changes in learning rates
correlate with changing risk prediction35. Moreover the level of activity within AI before a
choice is made can predict both risk-taking behaviour and errors resulting from risk-taking12.
These studies suggest that AI does not only process and learn about feeling states but also
process and learn about risk associated with current decisions and is likely to mediate
behavioural and physiological effects of risk prediction.
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
8
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.
[INSERT FIGURE 2]
The model differentiates several mechanisms and levels of representations within insular
cortex (Figure 2A). First, there are modality-specific feeling states: A current feeling state
(how something feels now), a predictive feeling state (how something will feel) and a feeling
prediction error (how wrong the prediction was). In the case of pain, predictive feeling states
simulate the feeling in response to nociceptive stimulation before it is applied to me or others.
This predictive feeling is compared to the actual (current) feeling during stimulation.
Learning from this prediction error (e.g., through reinforcement learning) will result in more
accurate predictive feeling states and fewer prediction errors36-38.
Second, we learn about uncertainty via a parallel mechanism that involves a corresponding set
of representations: actual uncertainty, predictive uncertainty and uncertainty prediction errors
(greater or less uncertainty than predicted) (see Box 3 and Refs. 33,38). In the case of pain, this
mechanism provides a measure of how uncertain I am about the upcoming pain stimulus
within the current environment. It reflects the variance of the pain stimulus (rather than its
mean), in other words, how certain I am about the occurrence and magnitude of the painful
stimulus and the resulting feeling state and – after pain delivery – how (in)accurate my
certainty about the feeling state was.
Third insular cortex integrates information from modality-specific feeling states and
uncertainty with individual preferences (e.g., risk aversion or sensation seeking) and
9
contextual information (e.g., personal belief about the source of the pain) to produce a global
feeling state (see Figure 2B). As decisions between equally expected rewards are biased by
the degree of uncertainty, uncertainty itself possesses motivational value32 and can be
reflected in a feeling state. Bodily and affective responses to uncertain stimuli may facilitate
(and therefore accelerate) behavioural responses aimed at avoiding uncertainty. Accordingly,
it is adaptive for a body to prepare a flight response in highly uncertain situations.
Finally, we suggest that the integration of uncertainty with bodily, affective and sensory
information within AI improves learning and guides decision making alongside with
physiological reactivity. The anatomical interconnections of insular cortex with subcortical
and cortical areas support such higher-level integrated feeling states (see Box 1 and Ref. 2).
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 representations: Actual subjective
experience of pain or touch engages mid and posterior insula, while anticipation or
empathizing with others’ experience of pain or touch engages AI1,2,6,10,39. While this is
consistent with a shared representation within AI underlying predictive and empathic feeling
states in the domain of pain (see above and Ref. 10), the caudo-rostral organization of
representational levels within the insula may not generalize to other sensory modalities,
including taste, where primary cortex lies within AI8. Evidence for prediction errors within AI
10
have been reported both for learning about nociceptive stimuli40 and, recently, for learning
about uncertainty33,34.
Our model further proposes that a dominant subjective feeling state arises in insula from
integrating modality-specific feeling states about self and others (e.g., pain, touch) with
information about uncertainty as well as individual preferences (Figure 2B).
Although research has not focused on the simultaneous manipulation of uncertainty, bodily
signals and feeling states, several studies suggest direct interaction between the three
domains. For example, behaviourally, physically identical pain stimuli are rated as being more
painful when they are unpredictable rather than predictable. Interestingly, the unpredictability
of pain is reflected in anterior insula41,42 and the predictability of pain is reflected in posterior
insula activity41. Misleading anticipatory cues also modulate perceived pain through
engagement of mid- and posterior insula, in other words, influence the representation of the
subjective experience of the stimulus43. Furthermore, correlations have been reported between
risk prediction and the physiological arousal of financial traders, between risk perception and
dispositional fear as well as between risk-taking behaviour, anxiety32 and harm avoidance13.
In addition, patients with insular damage show a reduced sensitivity to risk44 and impaired
emotional intelligence45. Interestingly, Simmons et al.46 demonstrated an interaction between
emotion and uncertainty in mid insula: The neural signal increased with increasing intolerance
of uncertainty during an affective ambiguity task, but not during a non-affective ambiguity
task.
Finally, we suggest that the integration of uncertainty with bodily, affective and sensory
information within AI influences decision making in conjunction with homeostatic body
regulation. Several recent studies support such a link. During decision making, anticipatory
11
right AI activation predicts low risk choices, while overall insula activation predicts risk
preferences12,47. Sanfey and colleagues48 revealed that anterior insula activation precedes the
choice to defect against unfair but not fair players and Naqvi et al.5 found that smokers with
brain damage involving the insula, compared to smokers with lesions in other areas, were
more likely to quit smoking easily and immediately, without relapse, and without persistence
of the urge to smoke, a finding that also points to the important role of insula in craving and
addiction. In addition, patients with insular damage demonstrate poor decision making during
gambling experiments resulting in higher losses44. Finally, it is interesting to note that during
instrumental conditioning, rats with lesions to the insular cortex fail to adjust their choice
behaviour to variations in motivational state during learning49.
The above studies strongly support a role for insular cortex in learning predictive feeling
states and mediating the interaction between feeling states and uncertainty. However, no
studies have explored the interaction between different feeling states (predictive, current and
error) and uncertainty during learning. A clever combination of previous paradigms can
provide further insight. Consider a conditioning paradigm using pain40 in which the intensity
(low vs. high) and the probability of stimulus delivery (50% vs. 100%) are varied
simultaneously. Participants in two pre-selected groups (risk-averse vs. risk-seeking) rate how
they feel after each trial. Our model predicts spatially distinct signals in AI reflecting
predictions and prediction errors related to the intensity of pain on the one hand and
uncertainty on the other. The model predicts different activation patterns for different
modalities (e.g., pain vs. taste). However, uncertainty signals in AI could be shared across
modalities. Another peak of activation in AI should reveal the integrated feeling state
reflecting the interaction between risk preferences, pain and uncertainty: Risk-averse
participants will feel more negative about the painful stimulus if it is accompanied by the
negative feeling of situational uncertainty (50% predictability). In contrast, for a risk-seeking
12
participant, the negative feeling about the painful stimulus will be alleviated by the positive
feeling towards uncertainty.
There is less evidence about the effects of uncertain environments on empathy. Recent studies
highlighted the sensitivity of empathic responses within AI to contextual factors including
affective bond or the perceived source for others’ pain (for an overview see19). The
trustworthiness of others47,50 could be used experimentally to influence the degree of empathic
uncertainty. Similarly, vicarious aspects of emotional learning and feeling prediction errors
could be explored by providing feedback to empathizing participants about the subjective
feeling or physiological states of others receiving pain. If this information does not match the
empathic prediction (what the participant thought the other was feeling), a vicarious
prediction error signal should be observed within AI.
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 states and feeling prediction errors. Learning about
uncertainty is supported by analogous representations in anterior insula. A dominant
integrated feeling state includes current and predictive feeling states and uncertainty as well as
individual preferences. It is putatively experienced as emotional confidence that serves to
motivate and guide social interaction and decision-making behaviour.
13
Extending the computational model of insula during risk-taking to include emotion and
empathy could considerably advance our understanding of affective processes and emotion
learning, with particular relevance to understanding decision making in complex social
environments. Conversely, theories of economic decision making may benefit from
understanding the role of bodily signals, feeling states and subjectivity in shaping complex
decisions.
INSERT QUESTION BOX: Open Questions
Acknowledgements
We thank Klaas Enno Stephan and Peter Bossaerts for useful comments on the present paper. HDC was supported by a programme grant from the Wellcome Trust.
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Box 1: Anatomy and connectivity of insular cortex
Insular cortex is located bilaterally within the brain, tucked away under the posterior part of
the frontal lobe and the anterior part of the temporal lobe. It connects anteriorly to the lateral
orbitofrontal cortex (OFC) and posteriorly to the superior temporal cortex. Its main division is
the central insular sulcus that separates anterior from posterior insula although more recent
work also focuses on the dissociation of ventral and dorsal parts.
The dense anatomical connectivity of anterior and posterior insula to many subcortical and
cortical areas is coherent with integrative computations and high-level representations of
affective state:
AI is interconnected with subcortical structures such as the nuclei of the brain stem, limbic
structures and the basal ganglia, supporting its involvement in the representation and
integration of autonomic and visceral signals. Correspondingly, insula is also the cortical
terminus of visceral and ‘motivationally salient’ afferent fibres through spinal cord (Laminar
1) to brainstem and thalamus where spinal, humoral and vagus nerve interoceptive
information converge before projecting to posterior dorsal insular cortex. Somatosensory and
motor integration in posterior insula are supported by its connections to structures such as the
thalamus and basal ganglia.
Particularly important for our account are the AI’s dense connections with the nuclei of the
amygdala which is implicated in emotion (e.g., fear) and novelty detection.
In addition, AI connects bidirectionally to decision-making networks including areas
implicated in valuation and response selection such as OFC, nucleus accumbens, anterior
cingulate, dorsolateral prefrontal cortex (DLPFC, Brodmann area 46) and prefrontal areas.
Finally, intrainsular connections – required to represent and to compare predictive and actual
states – exist bi-directionally with stronger connectivity from AI to PI51-54.
19
Box 2: Interoceptive models of emotions
In the late 19th century, James and Lange suggested that the experience of emotions
necessarily depends on changes in bodily responses that automatically accompany emotive
stimuli20. Thus, we feel love when our hearts beat faster upon seeing our beloved and we feel
anxious when our stomachs constrict in response to the stress of making a difficult decision.
Although patterns of bodily responses can be linked to discrete emotions (e.g., our face
reddening with rage, blushing with embarrassment, paling with fear), bodily arousal states
remain rather undifferentiated. To better account for the variety of human emotional
experience while acknowledging a bodily contribution to feelings, Schachter and Singer23
refined this peripheral theory of emotion: They proposed a two-step model of emotions
according to which the onset and intensity of an emotion is determined by bodily signals and
the quality and category of the emotion is determined by one’s cognitive appraisal of the
context in which the change in bodily arousal was experienced (see also Klaus Scherer’s55
cognitive appraisal model). In recent years, these models have been rediscovered and
extended by neuroscientists, notably Damasio and colleagues (e.g., Ref. 21) in their
formulation of the somatic marker hypothesis. Here, body signals are seen as the necessary
basis in a hierarchy of conscious and emotional experiences, representing the core self in
terms of “I feel my body therefore I am” and also as a crucial influence on motivational
behaviour especially when one is making decisions involving uncertainty (see below). Bodily
arousal responses (‘somatic markers’ generated in response to the risk of a negative outcome)
are fed back to guide adaptive behaviour pre-consciously or in the form of ‘gut feelings’ and
‘hunches’. Patients with damage to medial prefrontal and somatosensory cortices manifest
emotional and behavioural difficulties consistent with acquired deficits in the generation and
feedback of somatic markers. An anatomically-inspired account proposed by Bud Craig
20
suggests that insular cortices support the cortical representation of interoceptive information
and awareness1.
Box 3: Uncertainty and risk
Uncertainty and risk
‘Uncertainty’ describes the degree to which past, present or future states and events are
known. It is induced by a lack of information (e.g., about the availability of rewards or about
how a sensory stimulus will feel to me). Imperfect measurements (e.g., due to the unreliability
of our sensory and visceral organs), poor predictive models (e.g., those employed when one
tries to predict a stranger’s behaviour in an ultimatum game) and random events (e.g., the
outcome of a coin toss) can all contribute to uncertainty.
Economics may further dissociate risk from uncertainty. ‘Risk’ describes the degree to which
all situational outcomes as well as their mathematical probabilities are known. For example, a
(fair) coin toss has 2 outcomes, each of which occurs with probability ½. Here, risk can be
modelled precisely, for example, as the variance of the outcome. In principle, (true or
estimated) probabilities can be assigned to any outcome, so the ideas presented above can be
applied to both risk and uncertainty, whether they are different concepts or two sides of the
same coin.
Learning about risk, uncertainty and feelings
One can learn about risk, uncertainty and feelings the same way one learns about rewards –
through reinforcement learning37. Comparing the risk prediction of an upcoming outcome
with the risk (or size of the error) measured at the time of the outcome results in risk
prediction error, which can serve to improve future risk predictions if taken into account.
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Incorporating uncertainty into reinforcement learning (RL) algorithms
We can use an uncertainty measure to further improve predictions as well as prediction
learning about events (e.g., rewards) and states (e.g., feeling states). Imagine two predictive
cues, C1 and C2, where C1 reliably predicts stimulus strength S1 (low uncertainty) and C2
unreliably predicts stimulus strength S2 (high uncertainty). If you see both C1 and C2, what
stimulus strength should you expect? The best prediction (with minimal prediction error) is
one that puts more weight on C1 than on C2. On average, this will yield smaller prediction
errors. This reasoning extends to prediction learning. Predictions made in an uncertain
environment are likely to result in more frequent and larger errors. However, such errors are
less surprising (given that I was uncertain) than errors made in a certain environment. For
instance, an error after seeing C2 (high uncertainty) will be less surprising than an error after
seeing C1 (low uncertainty). As a result, I may increase my learning rate in response to the C1
error, but not the C2 error, because I knew that C2 was unreliable.
How uncertainty and uncertainty errors are tracked in the human brain and how uncertainty an
be incorporated into RL can be read elsewhere (e.g., Refs. 33,35,38).
Question Box: Questions for future research
- Can we spatially dissociate the representations of different states (modality-
specific feeling state, uncertainty, dominant integrated feeling state) which insula
is proposed to be involved in?
- Can we further identify different sites in AI that represent current and predictive
feeling states as well as feeling prediction errors?
- Are representations of current feeling states temporally correlated with
representations of predictive feeling states and feeling state prediction errors and
does this correlation depend on the uncertainty about the predictive feeling state?
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- How are different modalities (e.g., feeling pain, disgust, pleasant tastes)
distinguished within insular cortex?
- Does insula represent feeling intensity or valence? How are positive and negative
valuation (feeling good or bad) distinguished within insular cortex?
- How is information about uncertainty integrated when one empathizes with and
vicariously learns about the feelings of others?
- Do functions of the insula influence decision making directly?
- How does insula function relate to memories about how things felt in the past? Is
this the same as a prediction of how things will feel in the future?
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Figure caption
Figure 1: Anterior insula (AI) activation from fMRI studies on feelings, empathy and risk.
Panel A illustrates brain activity that is significantly correlated with changes in peripheral
electrodermal activity observed in subjects performing a gambling task56. Panel B depicts
enhanced bilateral anterior insula (AI) and right frontal opercular (FO) activity observed when
subjects performed an interoceptive task (heartbeat monitoring) versus an exteroceptive (note
monitoring) task22. Panel C illustrates bilateral AI activity when subjects empathized with
another person feeling pain. Activation shown reflects the average activation (N=78) observed
in three independent studies on empathy for pain 10,27,28. Panel D illustrates brain activity in
bilateral AI that is correlated with risk prediction (blue) and risk prediction errors (orange)
during a gambling task33.
Figure 2: A Model for the Integration of Feelings, Empathy and Uncertainty in Insular Cortex A. Schematic of error-based learning: A predicted state is followed by the actual
(experienced) state. The difference between the two, the prediction error, is used to update the
predicted state such that future predicted states will be more accurate. In the case of pain, the
predicted state is a predictive feeling state that is followed by the actual feeling state in
response to a painful stimulus. The prediction error with respect to the feeling indicates how
(in)accurate the predictive feeling state was. In the case of uncertainty, the predicted state is
the prediction risk that indicates how accurate I expect my prediction to be. The prediction
risk error that is generated at the outcome is used to update future estimates of prediction risk.
B. The integrated subjective feeling state combines information about modality-specific
feelings, uncertainty, contextual appraisal and individual preferences and traits such as risk
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preferences and anxiety. No particular computational model is implied as to how the different
inputs are combined.
Figure Box 1. Illustration of the insula cortex in the human brain.
Figure 1
Figure 2
Figure Box 1