John P. O’Doherty

Neural evidence for inequality-averse social preferences

A popular hypothesis in the social sciences is that humans have social preferences to reduce inequality in outcome distributions because it has a negative impact on their experienced reward. Although there is a large body of behavioural and anthropological evidence consistent with the predictions of these theories, there is no direct neural evidence for the existence of inequality-averse preferences. Such evidence would be especially useful because some behaviours that are consistent with a dislike for unequal outcomes could also be explained by concerns for social image or reciprocity, which do not require a direct aversion towards inequality. Here we use functional MRI to test directly for the existence of inequality-averse social preferences in the human brain. Inequality was created by recruiting pairs of subjects and giving one of them a large monetary endowment. While both subjects evaluated further monetary transfers from the experimenter to themselves and to the other participant, we measured neural responses in the ventral striatum and ventromedial prefrontal cortex, two areas that have been shown to be involved in the valuation of monetary and primary rewards in both social and non-social contexts. Consistent with inequality-averse models of social preferences, we find that activity in these areas was more responsive to transfers to others than to self in the ‘high-pay’ subject, whereas the activity of the ‘low-pay’ subject showed the opposite pattern. These results provide direct evidence for the validity of this class of models, and also show that the brain’s reward circuitry is sensitive to both advantageous and disadvantageous inequality.

Activation in posterior superior temporal sulcus parallels parameter inducing the percept of animacy

An essential, evolutionarily stable feature of brain function is the detection of animate entities, and one of the main cues to identify them is their movement. We developed a model of a simple interaction between two objects, in which an increase of the correlation between their movements varied the amount of interactivity and animacy observers attributed to them. Functional magnetic resonance imaging revealed that activation in the posterior superior temporal sulcus and gyrus (pSTS/pSTG) increased in relation to the degree of correlated motion between the two objects. This activation increase was not different when subjects performed an explicit or implicit task while observing these interacting objects. These data suggest that the pSTS and pSTG play a role in the automatic identification of animate entities, by responding directly to an objective movement characteristic inducing the percept of animacy, such as the amount of interactivity between two moving objects.

Contributions of the ventromedial prefrontal cortex to goal‐directed action selection

In this article, it will be argued that one of the key contributions of the ventromedial prefrontal cortex (vmPFC) to goal‐directed action selection lies both in retrieving the value of goals that are the putative outcomes of the decision process and in establishing a relative preference ranking for these goals by taking into account the value of each of the different goals under consideration in a given decision‐making scenario. These goal‐value signals are then suggested to be used as an input into the on‐line computation of action values mediated by brain regions outside of the vmPFC, such as parts of the parietal cortex, supplementary motor cortex, and dorsal striatum. Collectively, these areas can be considered to be constituent elements of a multistage decision process whereby the values of different goals must first be represented and ranked before the value of different courses of action available for the pursuit of those goals can be computed.

A neural basis for the effect of candidate appearance on election outcomes

Election outcomes correlate with judgments based on a candidates visual appearance, suggesting that the attributions viewers make based on appearance, so-called thin-slice judgments, influence voting. Yet, it is not known whether the effect of appearance on voting is more strongly influenced by positive or negative attributions, nor which neural mechanisms subserve this effect. We conducted two independent brain imaging studies to address this question. In Study 1, images of losing candidates elicited greater activation in the insula and ventral anterior cingulate than images of winning candidates. Winning candidates elicited no differential activation at all. This suggests that negative attributions from appearance exert greater influence on voting than do positive. We further tested this hypothesis in Study 2 by asking a separate group of participants to judge which unfamiliar candidate in a pair looked more attractive, competent, deceitful and threatening. When negative attribution processing was enhanced (specifically, under judgment of threat), images of losing candidates again elicited greater activation in the insula and ventral anterior cingulate. Together, these findings support the view that negative attributions play a critical role in mediating the effects of appearance on voter decisions, an effect that may be of special importance when other information is absent.

The behavioral and neural mechanisms underlying the tracking of expertise.

Summary Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms’ expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent’s prediction was concordant rather than discordant with the subject’s own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network—medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus—represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning.

Differentiable contributions of human amygdalar subregions in the computations underlying reward and avoidance learning

To understand how the human amygdala contributes to associative learning, it is necessary to differentiate the contributions of its subregions. However, major limitations in the techniques used for the acquisition and analysis of functional magnetic resonance imaging (fMRI) data have hitherto precluded segregation of function with the amygdala in humans. Here, we used high‐resolution fMRI in combination with a region‐of‐interest‐based normalization method to differentiate functionally the contributions of distinct subregions within the human amygdala during two different types of instrumental conditioning: reward and avoidance learning. Through the application of a computational‐model‐based analysis, we found evidence for a dissociation between the contributions of the basolateral and centromedial complexes in the representation of specific computational signals during learning, with the basolateral complex contributing more to reward learning, and the centromedial complex more to avoidance learning. These results provide unique insights into the computations being implemented within fine‐grained amygdala circuits in the human brain.

In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles

Summary The ability to infer intentions of other agents, called theory of mind (ToM), confers strong advantages for individuals in social situations. Here, we show that ToM can also be maladaptive when people interact with complex modern institutions like financial markets. We tested participants who were investing in an experimental bubble market, a situation in which the price of an asset is much higher than its underlying fundamental value. We describe a mechanism by which social signals computed in the dorsomedial prefrontal cortex affect value computations in ventromedial prefrontal cortex, thereby increasing an individual’s propensity to ‘ride’ financial bubbles and lose money. These regions compute a financial metric that signals variations in order flow intensity, prompting inference about other traders’ intentions. Our results suggest that incorporating inferences about the intentions of others when making value judgments in a complex financial market could lead to the formation of market bubbles.

Stimulus devaluation induced by stopping action.

Impulsive behavior in humans partly relates to inappropriate overvaluation of reward-associated stimuli. Hence, it is desirable to develop methods of behavioral modification that can reduce stimulus value. Here, we tested whether one kind of behavioral modification--the rapid stopping of actions in the face of reward-associated stimuli--could lead to subsequent devaluation of those stimuli. We developed a novel paradigm with three consecutive phases: implicit reward learning, a stop-signal task, and an auction procedure. In the learning phase, we associated abstract shapes with different levels of reward. In the stop-signal phase, we paired half those shapes with occasional stop-signals, requiring the rapid stopping of an initiated motor response, while the other half of shapes was not paired with stop signals. In the auction phase, we assessed the subjective value of each shape via willingness-to-pay. In 2 experiments, we found that participants bid less for shapes that were paired with stop-signals compared to shapes that were not. This suggests that the requirement to try to rapidly stop a response decrements stimulus value. Two follow-on control experiments suggested that the result was specifically due to stopping action rather than aversiveness, effort, conflict, or salience associated with stop signals. This study makes a theoretical link between research on inhibitory control and value. It also provides a novel behavioral paradigm with carefully operationalized learning, treatment, and valuation phases. This framework lends itself to both behavioral modification procedures in clinical disorders and research on the neural underpinnings of stimulus devaluation.

Multiple Systems for Value Learning

Although choice is often unitary on theoretical accounts, there is much empirical evidence that decisions are produced by multiple, cooperating or competing neural and psychological mechanisms. We review the evidence that decisions in humans and other animals are influenced by three systems for value learning: Pavlovian, habitual, and goal-directed. These systems are behaviorally dissociable, are mediated by at least partly differentiable brain systems, and embody distinct computational principles. We discuss how the interactions between these systems for behavioral control can produce errors, inefficiencies, and disorders involving compulsion, and how these systems relate to other dual- or multiple-system models in neuroeconomics.

Neural computations mediating one-shot learning in the human brain.

Incremental learning, in which new knowledge is acquired gradually through trial and error, can be distinguished from one-shot learning, in which the brain learns rapidly from only a single pairing of a stimulus and a consequence. Very little is known about how the brain transitions between these two fundamentally different forms of learning. Here we test a computational hypothesis that uncertainty about the causal relationship between a stimulus and an outcome induces rapid changes in the rate of learning, which in turn mediates the transition between incremental and one-shot learning. By using a novel behavioral task in combination with functional magnetic resonance imaging (fMRI) data from human volunteers, we found evidence implicating the ventrolateral prefrontal cortex and hippocampus in this process. The hippocampus was selectively “switched” on when one-shot learning was predicted to occur, while the ventrolateral prefrontal cortex was found to encode uncertainty about the causal association, exhibiting increased coupling with the hippocampus for high-learning rates, suggesting this region may act as a “switch,” turning on and off one-shot learning as required.