Chris D. Frith - What brain plasticity reveals about the nature of consciousness: Commentary

Chris D. Frith used to be a hard-core materialist regarding the realm of neuroscience and consciousness. Over the years, however, he has become a leader in the research into how human minds create culture and how culture shapes human minds. Here is his own statement of his research (from his website):

Although I retired from my position at the Wellcome Centre for Neuroimaging at UCL in 2007, I am continuing to develop the new discipline of neural hermeneutics. This discipline concerns the neural basis of social interaction. I am fortunate in having a number of excellent collaborators for this enterprise, in particular, Uta Frith. In October 2011 I was elected a two-year fellow of All-Souls where I am organising a series of seminars on Meta-cognition in order to explore the critical role of this process in sharing experiences. My main experimental work is currently performed in the interacting minds centre at Aarhus University. We are trying to delineate the mechanisms underlying this human ability to share representations of the world for it is this ability that makes communication possible.

We think that there are two major processes involved. The first is an automatic form of priming (sometimes referred to as contagion or empathy), whereby our representations of the world become aligned with those of the person with whom we are interacting. The second is a form of forward modelling, analogous to that used in the control of our own actions. Such generative models enable us to predict the actions of others and use prediction errors to correct and refine our representations of the mental states of the person we are interacting with.

We are carrying out a series of behavioural and brain imaging experiments that will delineate the neural mechanisms that underlie these two processes in healthy volunteers.

The results will be relevant for our understanding of psychiatric disorders such as schizophrenia. One characteristic of the mistaken perceptions (hallucinations) and beliefs (delusions) associated with this disorder is their resistance to change in spite of their incompatibility with the beliefs and perceptions of others. This indicates a failure in the mechanism by which we align our representations of the world with those of others. Delineating the normal mechanisms of alignment will help us to identify the neural basis of hallucinations and delusions.

A couple of years ago, he published the article below in Frontiers in Psychology: Consciousness Research. This essay served as the introduction to a special issue on the relevance of brain plasticity to the understanding of consciousness. Frith suggests, based on the evidence, that "consciousness, and the qualia that make up that consciousness, are not static. The contents of consciousness are constantly changing and developing through our experiences and especially through our sharing of experiences with others."

I agree - this short essay is definitely worth the read.

Full Citation: 
Frith CD. (2011, May 11). What brain plasticity reveals about the nature of consciousness: Commentary. Frontiers in Psychology: Consciousness Research; 2:87. doi: 10.3389/fpsyg.2011.00087

What brain plasticity reveals about the nature of consciousness: Commentary

Chris D. Frith [1,2]

1. Center for Functional Integrative Neuroscience, Aarhus University, Aarhus, Denmark
2. Wellcome Trust Centre for Neuroimaging, University College London, London, UK

What is Consciousness?

Consciousness continues to be an ill-defined concept, so I shall start by discussing how the term is used in this special issue. As discussed in Overgaard and Overgaard (2010), there is an important distinction between level of consciousness and content of consciousness. Level of consciousness refers to a dimension that varies from coma at one extreme, through sleep and, at the other extreme, alert wakefulness. Philosophers call this creature consciousness since it applies to the whole creature (Rosenthal, 2009). Level of consciousness is of particular relevance to the studies of patients in persistent vegetative state discussed by Laureys and colleagues (Demertzi et al., 2011). However, most of the contributions to this special issue are concerned with the content of consciousness.

Alert wakefulness is characterized by consciousness of specific mental states. The states that we are aware of are the contents of consciousness. Philosophers call this state consciousness. This is somewhat confusing, given that, when people talk about altered states of consciousness, they are usually referring to different levels of consciousness rather than different contents of consciousness. So I will continue to use the terms level and contents of consciousness.

In studies of the neural correlates of consciousness there is great interest in contrasting the neural activity associated with stimuli that influence the contents of consciousness with the neural activity associated with the same stimuli when they affect behavior in the absence of any change in the content of consciousness (Frith et al., 1999). When a stimulus elicits neural activity and affects behavior it does not necessarily follow that we are aware of that stimulus.

A certain minimal level of consciousness is necessary for there to be any contents of consciousness, but the level of consciousness does not determine what the contents of consciousness will be. As is demonstrated in this special issue, brain plasticity has an important role in determining the contents of consciousness.

There is also a reflective aspect of consciousness which is modeled by Cleeremans (2011) and which Allen and Williams (2011) suggest may be uniquely human. Are conscious mental states thoughts about thoughts? Is consciousness by its very nature reflective? There is clearly a relationship between this aspect of consciousness and metacognition. I shall return to consideration of this relationship at the end of this introduction.

Certain Neural Structures are Necessary, but Not Sufficient for Consciousness

It is well established that brain lesions can alter the contents of consciousness. To take just one example, lesions to the extra-striate cortex can eliminate awareness of color (achromatopsia, Zeki, 1990). Brain stimulation can also generate the contents of consciousness. For example, electrical stimulation of extra-striate cortex can generate hallucinations of various visual features including color (Lee et al., 2000). This has lead to the idea of essential nodes for the specific conscious contents (e.g., V4 for color, Zeki and Bartels, 1999). In accord with this idea, Silvanto and Rees (2011) conclude that, in the absence of primary visual cortex, humans seem to have extremely limited capacity for visual awareness (but see Ffytche and Zeki, 2011).

However, these essential nodes are clearly not sufficient for consciousness. If the level of consciousness is too low, as in coma, then experience does not occur even though the essential nodes are intact. Laureys and colleagues (Demertzi et al., 2011) suggest that the lack of awareness in such cases is due to the loss of certain kinds of long-range connectivity in the brain. Similar effects may be produced by anesthesia in which long-ranged connectivity is lost temporarily (Alkire et al., 2008).

Brain Plasticity Provides Important Clues for Understanding the Nature of Consciousness and Its Relation to the Brain

The theme of this special issue is the observation that the loss of awareness associated with brain damage is frequently reversed. Unilateral spatial neglect, for example, is a disorder of consciousness associated with stroke from which recovery can be quite rapid (Cappa and Perani, 2010). In some cases it may be that brain tissue has been temporarily been deactivated, and subsequently recovers. However, in the many cases where brain tissue has been permanently damaged, we have to ask how such recovery is possible. The doctrine of essential nodes would suggest that, if the node has been destroyed, recovery should not be possible.

Mogensen (2011) presents an excellent discussion of this problem. Does recovery depend upon the growth of new connections? Does the patient develop new cognitive strategies? One important conclusion is that the brain activity supporting recovery need not be in the same location as that originally supporting the experience. This observation supports two conclusions: (1) Conscious experience (qualia) can be re-acquired through some sort of learning process. (2) The nature of the experience (qualia) is not solely determined by the nature/location of the brain activity supporting it. This second conclusion is dramatically illustrated in the studies from Kupers et al. (2011) in which congenitally blind subjects were trained to “see” using a tactile stimulator. This technique involves turning the 2D images picked up by a video camera into a corresponding pattern of 2D tactile stimulation applied to the tongue. After being trained to recognize simple patterns with this stimulator, brain imaging revealed that performance of the task elicited activity in visual cortex. In addition transcranial magnetic stimulation applied to visual cortex lead to the experience tactile qualia. This is evidence against the idea, know as cortical dominance (Hurley and Noë, 2003), that qualia are determined by the cortical location of the associated brain activity. But what then is the property of nervous activity that determines the difference in the experience of the different senses?

New Qualia Can be Learned

However, it is not only after brain damage that qualia can be relearned and even learned for the first time. There are many examples of learning in the normal case. For example, between 6 and 12 months infants lose awareness of speech sound distinctions not present in their native language. With sufficiently early intervention this loss of awareness can be reversed, but interestingly only through direct interaction with a speaker, rather than passive exposure to audio or video-tapes (Kuhl et al., 2003).

Normal subjects can also learn to become conscious of stimuli previously outside awareness. Schwiedrzik et al. (2009) used meta-contrast masking to achieve chance performance in the detection of stimuli. After 5 days of training sensitivity was significantly increased and subjects reported awareness of the stimuli. Gottfried and his colleagues (Li et al., 2008) exposed volunteers to odor molecules (rose oxide and 2-butanol) that exist in two mirror image forms (enantiomers). At the beginning of the experiment the participants were entirely unable to smell any differences between the two mirror image forms, as is the case for most people. After only seven trials (for each odor) of standard Pavlovian conditioning, participants exhibited fear responses to the odor associated with shock and not to the other form, indicating that they now could distinguish between the mirror image odors. A further perceptual experiment showed that participants could now consciously detect the difference in smell.

In these examples, it seems likely that the potential to make perceptual distinctions was already present in the brain and that training revealed and enhanced this ability. For example, given the nature of the human eye we would not expect training to lead to awareness of infrared or ultraviolet light.

But the potential for awareness can also be artificially modified. Genetic manipulation in both mice and monkeys can alter the perception of color. Male squirrel monkeys are normally dichromats, but, even as adults, can be turned into trichromats through insertion of the missing opsin gene (Mancuso et al., 2009).

But How Do We Compare Qualia?

The observation that new qualia can be acquired, whether through learning or gene therapy, reminds us of a fundamental problem in consciousness research. How can we compare qualia from one person to another? Or within the same person at different times? As Overgaard and Mogensen (2011) ask, when a brain damaged patient recovers an awareness that had been lost, how can we know whether it is the same as the awareness that was present before the brain damage? If this recovered awareness is instantiated by activity in a different brain region and depends upon a different cognitive strategy it might well be different. Are there methods for determining whether two seemingly identical conscious states are actually different?

We have long known that people do have different sensory experiences. An obvious example is color blindness. The presence of the receptors necessary for color vision is under genetic control and some people have only two receptors instead of three, leading to different forms of color blindness (dichromacy), depending on which particular pigment is missing. The visual qualia of the color blind is clearly different, but trichromats still have some idea of what color blindness is like. It has now been found that some women have more than three retinal photopigment genes. These women also perceive significantly more color appearances than men or women with the usual three photopigment genes (Jameson et al., 2001). In this case the discovery of the biological difference led to the identification of the difference in the experience of color that can be explored empirically by asking subjects to make fine color discriminations.

Another example concerns individual differences in the spatial extent of primary visual cortex (V1). People with larger V1 are more susceptible to size illusions (Schwarzkopf et al., 2011). Having identified this biological difference we now explore the idea that these people have a subtly different experience of space.

Probably the most striking success in comparing qualia across people is Bartoshuk’s et al. (2004) demonstration of the existence of supertasters. These are people who experience the sense of taste with a far greater intensity than average. This discovery depended upon the development of scales for subjective experience that do not eliminate individual differences. There is still much work to be done in developing scales for quantifying subjective experience (see, for example, Sandberg et al., 2010), but it is clearly possible for such comparisons to be made.

How Can We Learn New Qualia?

Outside the laboratory human beings spend a lot of time in discussing their experiences. We enjoy telling each other what something was like. When we share experiences with others in this way, we can learn about two kinds of things. We can learn that other people have different experiences from ourselves. However, by pooling our experiences we can also get a better estimate of that the world is like, since, most of the time, two heads are better than one (Bahrami et al., 2010).

In order to pool our experiences we need to down play our differences and take the best features from each experience. Since successful joint action (as well as joint perception) depends upon such pooling, this may be why we are so often unaware of subtle, but consistent differences in experience. The implication is that, as a result of sharing experiences, our qualia may shift toward that of the person we are sharing with. I predict that the greatest shift will occur in the least expert member of the group. So I find most plausible the suggestion from Allen and Williams (2011), that we learn new qualia by interacting with others. This seems to be the case, for example, with activities like wine tasting (Smith, 2007). But for sharing our experiences we have to introspect upon and communicate our experience. This requirement emphasizes the reflective aspect of consciousness that is probably uniquely human. Reflecting upon our own experience is an example of metacognition, that is thinking about our thoughts.

There are considerable advantages for concentrating on this aspect of consciousness since metacognition is more precisely defined. Furthermore powerful techniques are now available for the quantification of metacognition (e.g., Galvin et al., 2003) and such measures have been applied to show that disruption of activity in dorsolateral prefrontal can change meta-cognitive sensitivity without altering discrimination performance (Rounis et al., 2010). Cleeremans (2011) uses the concept of metacognition to develop a computational model of how a brain can learn to be conscious by constructing a theory of its own behavior.

For me, a particularly interesting idea for further exploration is that this process of learning to be conscious of new things (i.e., to acquire new qualia) critically depends upon social interactions. In the various examples I mentioned above the learning of new qualia depended upon feedback from a teacher. To learn to experience the difference between the mirror image smell molecules required the experimenter to signal the distinction. More particularly, the American babies only learned to make the distinctions involved in Mandarin Chinese phonology through direct interaction with a speaker (Kuhl et al., 2003).

Conclusion

This special issue on the relevance of brain plasticity to the understanding of consciousness reminds us that consciousness, and the qualia that make up that consciousness, are not static. The contents of consciousness are constantly changing and developing through our experiences and especially through our sharing of experiences with others. Such change and development does not cease after brain damage. Indeed it is the dynamic relationship between brain and consciousness that enables the recovery of lost experience.

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Identifying Emotions on the Basis of Neural Activation - Kassam, et al

As of the publication of the new research detailed here, at least 200 articles have been published on the neural correlates of emotion using fMRI and PET alone (cited by the authors, [1]). The research suggests that some regions of the brain are more active than others when people experience specific emotions, but there is no region consistently and specifically activated by a single emotion type.

Rather than use the standard approaches, the authors of this new study, led by Karim Kassam, used a more complex approach:

Rather than search for contiguous neural structures associated with specific emotions, we applied multi-voxel pattern analysis techniques to identify distributed patterns of activity associated with specific emotions [21] [22]. Such techniques allow for the possibility that neural responses to emotional stimulation occur in many brain areas simultaneously. These algorithms frequently result in increased predictive power, and recent research suggests that they hold promise for classifying emotion using neurological and physiological data [23].

Their approach seems promising, but I am still apprehensive about trying to pinpoint the location of emotions in the brain, especially because they likely are distributed across a variety of modules. Still, it's interesting stuff.

[1] Kober H, Barrett LF, Joseph J, Bliss-Moreau E, Lindquist K, et al. (2008). Functional grouping and cortical–subcortical interactions in emotion: A meta-analysis of neuroimaging studies. Neuroimage 42: 998. doi: 10.1016/j.neuroimage.2008.03.059.

Full Citation:
Kassam KS, Markey AR, Cherkassky VL, Loewenstein G, Just MA. (2013). Identifying Emotions on the Basis of Neural Activation. PLoS ONE 8(6): e66032. doi:10.1371/journal.pone.0066032

Emotions Identified Based On Brain Activity

For the first time, scientists at Carnegie Mellon University have identified which emotion a person is experiencing based on brain activity.

The study, which will be published in the journal PLOS ONE, combines functional magnetic resonance imaging (fMRI) and machine learning to measure brain signals to accurately read emotions in individuals. Led by researchers in CMU's Dietrich College of Humanities and Social Sciences, the findings illustrate how the brain categorizes feelings, giving researchers the first reliable process to analyze emotions. Until now, research on emotions has been long stymied by the lack of reliable methods to evaluate them, mostly because people are often reluctant to honestly report their feelings. Further complicating matters is that many emotional responses may not be consciously experienced.

Identifying emotions based on neural activity builds on previous discoveries by CMU's Marcel Just and Tom M. Mitchell, which used similar techniques to create a computational model that identifies individuals' thoughts of concrete objects, often dubbed "mind reading."

"This research introduces a new method with potential to identify emotions without relying on people's ability to self-report," said Karim Kassam, assistant professor of social and decision sciences and lead author of the study. "It could be used to assess an individual's emotional response to almost any kind of stimulus, for example, a flag, a brand name or a political candidate."

One challenge for the research team was find a way to repeatedly and reliably evoke different emotional states from the participants. Traditional approaches, such as showing subjects emotion-inducing film clips, would likely have been unsuccessful because the impact of film clips diminishes with repeated display. The researchers solved the problem by recruiting actors from CMU's School of Drama.

"Our big breakthrough was my colleague Karim Kassam's idea of testing actors, who are experienced at cycling through emotional states. We were fortunate, in that respect, that CMU has a superb drama school," said George Loewenstein, the Herbert A. Simon University Professor of Economics and Psychology.

For the study, 10 actors were scanned at CMU's Scientific Imaging & Brain Research Center while viewing the words of nine emotions: anger, disgust, envy, fear, happiness, lust, pride, sadness and shame. While inside the fMRI scanner, the actors were instructed to enter each of these emotional states multiple times, in random order.

Another challenge was to ensure that the technique was measuring emotions per se, and not the act of trying to induce an emotion in oneself. To meet this challenge, a second phase of the study presented participants with pictures of neutral and disgusting photos that they had not seen before. The computer model, constructed from using statistical information to analyze the fMRI activation patterns gathered for 18 emotional words, had learned the emotion patterns from self-induced emotions. It was able to correctly identify the emotional content of photos being viewed using the brain activity of the viewers.

To identify emotions within the brain, the researchers first used the participants' neural activation patterns in early scans to identify the emotions experienced by the same participants in later scans. The computer model achieved a rank accuracy of 0.84. Rank accuracy refers to the percentile rank of the correct emotion in an ordered list of the computer model guesses; random guessing would result in a rank accuracy of 0.50.

Next, the team took the machine learning analysis of the self-induced emotions to guess which emotion the subjects were experiencing when they were exposed to the disgusting photographs. The computer model achieved a rank accuracy of 0.91. With nine emotions to choose from, the model listed disgust as the most likely emotion 60 percent of the time and as one of its top two guesses 80 percent of the time.

Finally, they applied machine learning analysis of neural activation patterns from all but one of the participants to predict the emotions experienced by the hold-out participant. This answers an important question: If we took a new individual, put them in the scanner and exposed them to an emotional stimulus, how accurately could we identify their emotional reaction? Here, the model achieved a rank accuracy of 0.71, once again well above the chance guessing level of 0.50.

"Despite manifest differences between people's psychology, different people tend to neurally encode emotions in remarkably similar ways," noted Amanda Markey, a graduate student in the Department of Social and Decision Sciences.

A surprising finding from the research was that almost equivalent accuracy levels could be achieved even when the computer model made use of activation patterns in only one of a number of different subsections of the human brain.

"This suggests that emotion signatures aren't limited to specific brain regions, such as the amygdala, but produce characteristic patterns throughout a number of brain regions," said Vladimir Cherkassky, senior research programmer in the Psychology Department.

The research team also found that while on average the model ranked the correct emotion highest among its guesses, it was best at identifying happiness and least accurate in identifying envy. It rarely confused positive and negative emotions, suggesting that these have distinct neural signatures. And, it was least likely to misidentify lust as any other emotion, suggesting that lust produces a pattern of neural activity that is distinct from all other emotional experiences.

Just, the D.O. Hebb University Professor of Psychology, director of the university's Center for Cognitive Brain Imaging and leading neuroscientist, explained, "We found that three main organizing factors underpinned the emotion neural signatures, namely the positive or negative valence of the emotion, its intensity - mild or strong, and its sociality - involvement or non-involvement of another person. This is how emotions are organized in the brain."

In the future, the researchers plan to apply this new identification method to a number of challenging problems in emotion research, including identifying emotions that individuals are actively attempting to suppress and multiple emotions experienced simultaneously, such as the combination of joy and envy one might experience upon hearing about a friend's good fortune.

Groundbreaking discoveries such as identifying emotions based on neural activation patterns have helped to establish Carnegie Mellon as a world leader in brain and behavioral sciences. To build on its foundation of research excellence in psychology, neuroscience and computational science, CMU recently launched a Brain, Mind and Learning initiative to enhance the university's ability to innovate in the laboratory and continue to solve real-world problems.

Here is the abstract and link to the article discussed in this post.

Identifying Emotions on the Basis of Neural Activation

Karim S. Kassam, Amanda R. Markey, Vladimir L. Cherkassky, George Loewenstein, Marcel Adam Just

Abstract

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Read the full article.

This short review article from Frontiers in Human Neuroscience looks at the ways in which affect, cognition, and emotional regulation play together in the formation of empathy. More importantly, they examine how each of the three components is affected by group membership and how that can lead to in-group bias (or an US vs THEM mindset).


Full Citation:
Eres R and Molenberghs P. (2013). The influence of group membership on the neural correlates involved in empathy. Frontiers in Human Neuroscience, 7:176. doi: 10.3389/fnhum.2013.00176

The influence of group membership on the neural correlates involved in empathy

Robert Eres and Pascal Molenberghs
School of Psychology, The University of Queensland, St. Lucia, QLD, Australia  

ABSTRACT: 

Empathy involves affective, cognitive, and emotion regulative components. The affective component relies on the sharing of emotional states with others and is discussed here in relation to the human Mirror System. On the other hand, the cognitive component is related to understanding the mental states of others and draws upon literature surrounding Theory of Mind (ToM). The final component, emotion regulation, depends on executive function and is responsible for managing the degree to which explicit empathic responses are made. This mini-review provides information on how each of the three components is individually affected by group membership and how this leads to in-group bias.

Introduction 

In their Perception-Action Model of empathy, Preston and de Waal (2002)state that “the attended perception of the object's state automatically activates the subject's representations of the state, situation, and object, and that activation of these representations automatically primes or generates the associated autonomic and somatic responses, unless inhibited.” Their view of empathy included various phenomena such as emotional contagion, cognitive empathy, guilt, and helping which according to their model all relied on the perception-action mechanism. While typically empathy has been investigated using behavioral paradigms, more recently it is becoming tangible to investigate the neural architecture that underlies this process (Preston and de Waal, 2002; Boston, 2007; Singer and Lamm, 2009; Decety, 2011; Shamay-Tsoory, 2011; Bernhardt and Singer, 2012). Decety (2011) recently proposed a three component basis for empathic experiences, highlighting affective, cognitive, and emotion regulative components. These components are deemed necessary for experiencing empathy where the affective component is identified as a bottom-up, or automatic, process and the cognitive and emotion regulative components are identified as top-down modulators. That is, sharing the pain of others occurs automatically but behavioral responses are differentiated by cognitive factors (for example, perspective taking) and emotion regulative factors (for example, motivation). Social neuroscience has also begun investigating the modulating factors that interfere with empathic responses such as inter-individual differences (Singer et al., 2004; Hein and Singer, 2008), closeness (Beeney et al., 2011), and groups (Ito and Bartholow, 2009; Chiao and Mathur, 2010). Group membership describes a group of people sharing similar and recognizable characteristics where an individual can categorize others as belonging to that particular social group (Abrams, 2012). The focus of the present review is to identify how group membership affects each of the three components of empathy and to illustrate how this accumulates to a biased view of how we see the world. 

Affective Empathy: the Ability to Share the Affective States of Others

The main problem in understanding empathy from a neuroscience perspective is explaining how we can overcome the physical distance between our brain and that of others. How can we make sure we experience the same emotions as others and how can we understand the emotions of others by just observing their behaviors? Simulation theory suggests that we understand other people's actions and emotions by mirroring their actions and feelings onto our own mind state (Preston and de Waal, 2002; Rizzolatti and Fabbri-Destro, 2008; Keysers and Gazzola, 2009; Rizzolatti and Sinigaglia, 2010). According to the classical view, perception-action coupling of motor actions is supported by mirror neurons located in areas such as the inferior parietal lobule (IPL) and posterior inferior frontal gyrus (Iacoboni et al., 1999; Rizzolatti et al., 2001), however, fMRI studies have shown that additional regions such as superior temporal sulcus (STS), dorsal and ventral premotor cortex and superior parietal lobule are also involved in perception-action coupling of motor actions (Molenberghs et al., 2009, 2010; Caspers et al., 2010).

The human mirror system does not passively respond to the observation of actions but is influenced by the mindset of the observer (Molenberghs et al., 2012c). Crucially for this review, previous studies have shown that group membership can modulate perception-action coupling. For example, a recent fMRI study (Molenberghs et al., 2012b) investigated the effect group membership has on our ability to accurately represent action perception. Participants were randomly divided into red or blue teams and they were told they had to compete against a member of the other team by pressing a button response as quickly as possible. In a subsequent experiment, participants were shown video clips of either in-group or out-group members making button-press responses as quickly as possible in a similar competitive situation, where their job was to identify which team member pressed the button fastest. On average both groups in the video clips pressed the buttons equally fast but behavioral analysis showed that participants responded that their team members pressed the button faster. Additional fMRI analyses showed differential neural activation when presented with actions of in-group members compared with out-group members. That is, for those participants who showed an in-group bias behaviorally (those participants that said their team members were faster), greater activity in the IPL was shown when observing in-group members perform the action compared with members from the out-group (Molenberghs et al., 2012b). The IPL plays an important role in perception action coupling and its modulation by group membership suggests we simulate the actions of in-group members more easily. This is in line with a recent EEG study by Gutsell and Inzlicht (2010), who found larger EEG mu suppression (which has previously been associated with mirror neuron activity) when observing actions of in-group members compared to actions of out-group members. Interestingly, this effect increased with the amount of prejudice toward the out-group (Gutsell and Inzlicht, 2010). This reduced perception-action coupling for out-group members also extends to feelings of empathy. For example in a TMS study, Avenanti and colleagues (2010) found a reduction in motor-evoked potential (MEP) amplitude in the hand of participants (induced by TMS to the contralateral motor cortex) when watching an in-group member being painfully stimulated (compared to touch) but no such effect was found when watching out-group members in pain. This suggests that participants simulated the pain of the in-group member but not the pain of the out-group member.

Though predominantly focused on action-perception, vicarious experiences through mirroring have also been shown to extend to emotion and sensory domains as well (Carr et al., 2003; Keysers et al., 2004, 2010; Keysers and Fadiga, 2008; Keysers and Gazzola, 2009). Observing another person's emotional or sensory state elicits activity in a homologous area in the observer, supporting the notion that we vicariously experience the emotional and sensory states of others and represent these states onto our own emotional and sensory repertoires (Keysers and Gazzola, 2009). Indeed a recent meta-analysis including 125 fMRI studies on the mirror system found that perception-action coupling of emotional expressions through vicarious experience is not limited to the aforementioned mirror areas but also involves brain areas involved in, for example, experiencing pain such as the insula and cingulate cortex (Molenberghs et al., 2009). The role of the mirror system in action understanding and affective empathy is controversial (Saxe, 2005, 2006; Hickok, 2009; Decety, 2010) but our view here is that vicarious responses are at least partially involved in affective empathy through mirroring processes, though we acknowledge that they are only part of the story. For example Decety (2011) views affective empathy more broadly as just mirroring and his model of affective empathy also includes affective arousal which he identifies as “the automatic discrimination of a stimulus as appetitive or aversive, hostile or hospitable, pleasant or unpleasant, threatening or nurturing.”

Neuropsychological evidence suggests that greater vicarious empathic responses are elicited from own-ethnicity members compared with other-ethnicity members (Avenanti et al., 2006, 2010; Ito and Bartholow, 2009;Xu et al., 2009; Chiao and Mathur, 2010; Azevedo et al., 2012; Gutsell and Inzlicht, 2012; Sessa et al., 2013). For example, a recent fMRI study showed that when observing a member of the same ethnicity experiencing painful stimulation, greater activity in the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) were found compared with when a member from a different ethnicity was experiencing pain (Xu et al., 2009). Race, however, is not the only factor to influence empathic responses to in-groups and out-groups. Group membership has also been found to moderate activation of the AI in response to observing painful situations. Hein and Colleagues (2010) showed in their fMRI study that greater activation in the left AI was found when in-group members (those from the same sporting team) received pain compared with out-group members (those from another sporting team). This activity was also found to correlate positively with the willingness to share the pain with an in-group member compared with an out-group member. When and out-group member received pain, rather than an increase in AI activity, more activity occurred in the right ventral striatum [an area typically associated with pleasure and schadenfreude (Singer et al., 2006; Takahashi et al., 2009)], and this activity was negatively correlated with the willingness to share the pain of the out-group member (Hein et al., 2010). In a similar fMRI study, Cikara and colleagues (2011) monitored neural activity when participants watched video clips of two sporting teams (participant favorite vs. other) compete against each other. They found that when the participants' team won, increased activity in the ventral striatum was observed. More importantly, though, when the participants' team lost, greater activity in the AI and dACC were shown suggesting that participants were empathizing with the pain that the players of their favored team felt. However, sharing the emotions with others alone cannot explain the rich experience of empathy. Empathy also involves a cognitive and emotional regulative component. 

Cognitive Empathy or the Ability to Reason About Others' Mental States

Vicariously sharing other people's emotions helps us partially understand how other people are feeling, but to completely understand the beliefs, desires and intentions of others, one must also reason about the mental state of others. This cognitive aspect of empathy is typically associated with regions associated with mental state reasoning or so called Theory of Mind (ToM) and often involves regions such as the medial Prefrontal Cortex (mPFC), Temporoparietal Junction (TPJ), and adjacent posterior Superior Temporal Sulcus (pSTS) (Amodio and Frith, 2006; Saxe, 2006; Decety and Lamm, 2007; Frith, 2007; Keysers and Gazzola, 2007; Uddin et al., 2007;Shamay-Tsoory et al., 2009; Van Overwalle and Baetens, 2009; Cheon et al., 2010; Shamay-Tsoory, 2011).

Cognitive empathy can also be modulated by group membership. Adams et al. (2009) used an fMRI modified version of the “Reading the Mind in the Eyes Test” (Baron-Cohen et al., 2001) in which participants are presented with pictures of just the eyes of people and participants then have to judge what the person in the picture is thinking or feeling. Adams et al. (2009)used pictures of Asian and Caucasian people and then let native Japanese and white Americans judge the mental state of those people. They found a behavioral intra-cultural advantage for understanding the mental state of in-group members compared to out-group members and showed that this in-group bias was associated with increased activity in the posterior STS. In line with Adams et al. (2009), research surrounding ToM has consistently shown the importance of the STS in understanding the mental states of others (Fletcher et al., 1995; Allison et al., 2000; Gallagher and Frith, 2003; Amodio and Frith, 2006). Similarly, Cheon et al. (2011) found that Korean participants showed more empathy for in-group members experiencing emotional pain than out-group members and that this was related to increased activity in the TPJ. Similar studies have also illustrated the importance of the mPFC in in-group bias. For example, Mathur and colleagues (2010) found increased activation in the mPFC when watching in-group members experience emotional pain compared to out-group members and this increase predicted greater empathy and altruistic motivation for one's in-group. Another fMRI study found mPFC activation when participants watched pictures of social groups but not for extreme low-status groups (Harris and Fiske, 2006).

The mPFC also has an important role in social categorization, with increased activation in this region previously associated with in-group concepts compared to out-group concepts in both existing (Morrison et al., 2012) and newly created groups (Molenberghs and Morrison, 2012). Volz and colleagues (2009) also found that during an fMRI modified version of the minimal group paradigm (Tajfel et al., 1971) high in-group favoritism was associated with increased activation in the mPFC. Taken together, the aforementioned findings suggest that increased activation in cognitive empathy regions are associated with increased understanding of the mental state of in-group compared to out-group members (Adams et al., 2009;Mathur et al., 2010; Cheon et al., 2011), in-group minus out-group social categorization (Volz et al., 2009; Molenberghs and Morrison, 2012;Morrison et al., 2012) and in-group favoritism (Volz et al., 2009), suggesting further the modulating role of group membership on empathic experiences. 

Emotional Self-Regulation or the Control of Explicit Emotions

To reiterate, affective empathy is partially supported by simulating the emotional states of others whereas cognitive empathy relies partially on understanding another's mental state through cognitive reasoning. Given this capacity to experience the affective and mental states of others, it seems necessary that an additional network be set to moderate the degree to which we experience these effects or explicitly express these states. Without an emotion regulative network, shared emotional states may inhibit our ability to perform tasks that require emotional distance (e.g., a surgeon operating on a child or a defense lawyer supporting a psychopath) or it may interfere with our ability to hide automatic biases (e.g., a parent being derogative to a teacher of a different racial background). Essentially, there needs to be a neural function that inhibits or facilitates empathic responses more explicitly to allow for appropriate functioning in day-to-day life (Decety, 2011). Areas involved with emotion regulation such as the rostral anterior cingulate cortex (rACC), dorsolateral (dlPFC) and ventromedial (vmPFC) prefrontal cortex have previously been shown to modulate the effects of empathy (Amodio et al., 2006, 2008; Cheng et al., 2007; Beer et al., 2008; Ito and Bartholow, 2009; Decety et al., 2010;Decety, 2011).

For example, Cheng and colleagues (2007) investigated the neural processes underlying expert and naïve populations' reactions to a person experiencing painful (penetrated with acupuncture needles) and non-painful (Q-tip) stimulation. Evidence from their fMRI investigation revealed increased activity for the pain matrix network (dACC, insula, somatosensory cortex) in naïve participants. On the other hand, the experts (physicians with acupuncture experience) provided no activity in these areas, instead neural activity was recorded in vmPFC which is involved in emotion regulation (Decety, 2011) and TPJ which has previously been implicated in self-other differentiation and ToM (Decety and Lamm, 2007). These results suggest that the acupuncturists could influence their vicarious pain experience by down-regulating these responses through emotional regulation and increased self-other differentiation. Using a similar paradigm, Decety et al. (2010) used EEG to identify the time course of empathic responses and the regulation thereof. The authors identified that for naïve participants, early (N110) and late (P3) activity showed differential responses for painful and non-painful stimuli but when the experienced physicians viewed this stimulus set, there were no differences in early or late processes which suggests that emotion regulation can impede on early processing of painful stimulus presentation (Decety et al., 2010).

Relevant to emotion regulation is the ability to inhibit explicit emotional reactions. It is important to regulate explicit emotional expressions to maintain egalitarian status within society. An example of this was shown in an fMRI study by Richeson and colleagues (2003) who argued that people (especially those with high racial bias) during interracial contact must inhibit racial attitudes and this would result in depletion of executive functions (i.e., response inhibition) which in turn would lead to impaired performance on a subsequent task that requires these functions. They tested this hypothesis by measuring White participants internal beliefs toward racial groups (Blacks and Whites) using an Implicit Association Test (IAT). Additionally, they asked participants to comment on a few questions with a Black Experimenter (mixed-race interaction) and then participants completed a Stroop task to measure executive functioning (task inhibition). Results showed that those who scored higher on the IAT for racial bias, also showed more interference effects on the subsequent Stroop task. When followed up with an fMRI task where participants were presented with Black and White faces, they found increased activation in the ACC and the dlPFC when Black faces were presented, suggesting greater response inhibition during these trials. A significant positive relationship was also found between the increase in ACC and dlPFC activation and the IAT and Stroop task, where this increase in the right dlPFC mediated the effect between IAT and Stroop interference. Collating this evidence, it suggests that people who show higher interracial bias try to inhibit automatic stereotypes, ultimately leading to a reduction in cognitive resources.

Another nice example of emotion regulation was shown in an fMRI study by Cunningham and colleagues (2004). They showed White participants pictures of Black (out-group) and White (in-group) faces either very briefly (30 ms) or for a longer duration (525 ms). The authors predicted that when these pictures would be presented very briefly, participants would not have enough time to regulate their emotions (i.e., negative responses to the Black faces). The fMRI results showed there was increased activation in the amygdala for Black faces compared to White faces when the stimuli were presented very briefly but no such effect was found when the stimuli were presented for longer. Instead they found increased activation in the dlPFC and ACC in the long stimulus presentation condition. When correlating the scores of an IAT regarding race bias with that of neural activity, a positive relationship was shown between behavioral data and fMRI activity in the amygdala for Black and White faces. Similarly, Black-White differences in amygdala activity between the short and long image presentations were predicted by frontal activation. Taking these findings together, it suggests that an automatic race bias against Black faces in White participants is moderated using reflective cognitive processes that only take effect after a period of time. Given that it is not socially acceptable to show explicit in-group bias, the authors interpreted this effect as increased emotion regulation of an automatic bias.

However, social categorization can also override automatic biases. For example, Van Bavel et al. (2008) investigated whether arbitrary and temporary novel group membership could override the effects of predominant group memberships within society (i.e., race as described in their study). Therefore, they randomly assigned participants to a mixed-race team. Pairing behavioral paradigms with functional MRI, the authors measured activity in the fusiform face area (FFA), which has previously been shown to be modulated by face perception and visual expertise (Gauthier et al., 1999, 2000; Golby et al., 2001; Van Bavel et al., 2011), when participants were presented with pictures of faces of in-group and out-group members. The results revealed greater activity in bilateral FFA for in-group faces compared to out-group faces. Interestingly this effect was specific to in-group vs. out-group and was not modulated by race (see also Van Bavel and Cunningham, 2009 and Van Bavel et al., 2011 for similar results). This provides evidence that categorizing people from a different race into an in-group can inhibit automatic racial biases. 

Conclusion

The current review aimed to highlight how group membership modulates the affective, cognitive, and regulative components of empathy. We have shown that in-group bias is not only a result of increased vicarious simulation of the actions (Gutsell and Inzlicht, 2010; Molenberghs et al., 2012b) and feelings (Xu et al., 2009) of in-group compared to out-group members but also follows from increased activation in ToM regions (Adams et al., 2009; Mathur et al., 2010; Cheon et al., 2011) when trying to understand the mental state of in-group vs. out-group members. These group biases can be influenced by emotional regulation (Ito and Bartholow, 2009) depending on expertise (Cheng et al., 2007; Decety et al., 2010) and context (Richeson and Shelton, 2003; Cunningham et al., 2004) so that we respond in a socially acceptable way to our environment. Lastly, it seems that arbitrary re-categorization can override automatic biases such as race (Van Bavel et al., 2008). Seeing as group membership modulates responses at each component of empathy, future investigations should identify methods of reversing these biases at each of the three distinguishable levels.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was supported by an ARC Discovery Early Career Research Award (DE130100120) and ARC Discovery Project Grant (DP130100559) awarded to Pascal Molenberghs.

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