Scientific Understanding of Consciousness
Altruistic Brain Wiring
Science 04 Mar 2016: Vol. 351, Issue 6277, pp. 1028-1029
Wiring the altruistic brain
Sebastian Gluth, Laura Fontanesi
Department of Psychology, University of Basel, Missionsstrasse 62a, 4055 Basel, Switzerland.
Contrary to classical economic supposition, understanding people's preferences and decisions is not as simple as observing their actions. Indeed, there are many reasons for behaving altruistically, such as being moved by someone's suffering (empathy) or feeling obliged to return a favor (reciprocity). One of the major challenges for social psychologists and neuroscientists is to characterize the different motives underlying our interactions with other people. A recent Science article shows that knowing how distinct areas in the human brain communicate with each other can tell us why someone behaves altruistically.
To induce different motives for altruism, the authors developed a clever experimental design in which a participant first interacts with two partners (who received specific instructions from the authors) in one of two scenarios. In one experimental group, a participant observes one partner receiving painful shocks, thereby eliciting an empathic response in the participant (empathy group). In the other group, a participant observes a partner sacrificing money to save the participant from receiving painful shocks, thereby eliciting a desire in the participant to return the kind behavior (reciprocity group). In both groups, the participant is also paired with a second partner who serves as a control—that is, a second person who does not receive painful shocks in the empathy group, or who does not sacrifice money to relieve the participant's shock treatment in the reciprocity group.
Following this phase of motive induction, all participants performed a money allocation task. They chose between maximizing a partner's monetary payoff by reducing their own (prosocial behavior) or holding on to the money at a cost to the other person (selfish behavior). During this task, the participants' brains were scanned by functional magnetic resonance imaging (fMRI)—which uses changes in local blood flow as an indicator of neural activity changes—while they chose how to allocate the money. As expected, participants sacrificed more money to the empathy or reciprocity partner than to the control partner. Critically, this increased altruism did not differ between the groups, so that the hidden motive could not be accurately inferred from behavior alone. Hein et al. analyzed the neural data using a conventional contrast of brain activation during altruistic decisions toward the empathy/reciprocity partner versus the control partner. In line with previous work, a brain network including anterior cingulate cortex (ACC), anterior insula (AI), and ventral striatum (VS) was identified. But again, this analysis did not reveal any differences between the two motives. However, when the authors examined how these brain areas interact with each other during altruistic decisions using dynamic causal modeling (DCM), they observed a distinction. The connectivity patterns differed remarkably between empathy and reciprocity, and, by using a novel DCM-based classification technique, the authors successfully categorized participants to their motive-induction treatment (see the figure). Hence, the reasons for being gracious to someone appear to be hidden in how cortical and subcortical structures communicate with each other.
The study is an intriguing example of the richness and power of fMRI data, when we do not simply look for “activated blobs” in brain images, but investigate the flow of information between brain areas. In this spirit, the authors report that the functional networks of the empathy and the control condition are very similar to each other but dissimilar to the reciprocity network. This indicates that empathy-motivated altruism is perhaps (phylogenetically) more common than reciprocal altruism.
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Science 04 Mar 2016: Vol. 351, Issue 6277, pp. 1074-1078
Grit Hein, et.al.
Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Switzerland.
Division of Systems Neuroscience of Psychopathology, Translational Research Center, University Hospital of Psychiatry, University of Bern, Switzerland.
Japanese Science and Technology Agency, PRESTO, Japan.
Department of Psychology, Pusan National University, Pusan, South Korea.
Goal-directed human behaviors are driven by motives. Motives are, however, purely mental constructs that are not directly observable. Here, we show that the brain’s functional network architecture captures information that predicts different motives behind the same altruistic act with high accuracy. In contrast, mere activity in these regions contains no information about motives. Empathy-based altruism is primarily characterized by a positive connectivity from the anterior cingulate cortex (ACC) to the anterior insula (AI), whereas reciprocity-based altruism additionally invokes strong positive connectivity from the AI to the ACC and even stronger positive connectivity from the AI to the ventral striatum. Moreover, predominantly selfish individuals show distinct functional architectures compared to altruists, and they only increase altruistic behavior in response to empathy inductions, but not reciprocity inductions.
The theory of revealed preference provides the choice-theoretic foundations for modern economics. In this view, preferences cannot be identified independently of behavior, and motives play no causal role in economists’ explanatory toolbox—a view that is in direct contradiction to the neuroeconomic approach. In psychology, motives are also considered to be independent drivers of goal-directed human behavior. Motives are, however, mental constructs that are not directly observable and frequently not even accessible introspectively, meaning that asking people does not provide relevant information about motives. Therefore, human motives have been typically inferred from individuals’ behavior by assuming that different motives lead to different behaviors.
Here we ask whether different motives have a distinct neurophysiological representation that is generalizable across individuals. That is, even if we had no information about individuals’ behaviors or if these behaviors would not allow us to make inferences about motives, could we still identify and predict their motives merely on the basis of their functional neural network architecture?
We tackled this question in the context of human altruistic decisions. Subjects participated in an allocation task in which they could make selfish or altruistic decisions. We studied the role of two key motives for altruistic behaviors—the empathy motive and the reciprocity motive, two important drivers for human altruism. We induced these motives experimentally in two different groups of subjects, i.e., subjects were randomly assigned to either the empathy induction group or the reciprocity induction group. After the motive inductions, subjects participated in the allocation task in which they could allocate money to other individuals at a cost to themselves. All subjects faced the same allocation task regardless of the previous motive-induction group. Therefore, their underlying motive cannot be inferred from the mere fact that they behave altruistically. Can we now predict the induced motive solely on the basis of the subject’s functional neural architecture?
We used dynamic causal models (DCMs) of functional magnetic resonance imaging data collected during the allocation task and used the estimated DCM parameters to predict subjects’ “hidden” motivational state with machine learning—an approach known as generative embedding. More specifically, the DCM analyses of subjects’ brain data during altruistic decision-making gave us information about individuals’ network architecture in the different motive conditions. These parameters then provided the “raw” material for our predictions and for the mechanistic insights that follow from our examination.
Motives are purely mental constructs that are not directly observable. Here we show, however, that distinct motives have a distinct neurophysiological representation in the brain. Although the empathy and the reciprocity motive increase the frequency of altruistic acts by the same amount relative to the baseline condition, they are associated with different patterns of brain connectivity that enabled us to predict the different motives with relatively high accuracy. We predicted each subject’s induced motive with a classifier whose parameters were not influenced by that subject’s brain data (nor by that subject’s behavioral data). Instead, the parameters of the classifier were solely informed by other subjects’ brain data. This means that the motive-specific brain connectivity patterns are generalizable across subjects. The distinct and across-subject–generalizable neural representation of the different motives thus provides evidence for a distinct neurophysiological existence of motives.
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