Ben Seymour

Empathic neural responses are modulated by the perceived fairness of others

The neural processes underlying empathy are a subject of intense interest within the social neurosciences. However, very little is known about how brain empathic responses are modulated by the affective link between individuals. We show here that empathic responses are modulated by learned preferences, a result consistent with economic models of social preferences. We engaged male and female volunteers in an economic game, in which two confederates played fairly or unfairly, and then measured brain activity with functional magnetic resonance imaging while these same volunteers observed the confederates receiving pain. Both sexes exhibited empathy-related activation in pain-related brain areas (fronto-insular and anterior cingulate cortices) towards fair players. However, these empathy-related responses were significantly reduced in males when observing an unfair person receiving pain. This effect was accompanied by increased activation in reward-related areas, correlated with an expressed desire for revenge. We conclude that in men (at least) empathic responses are shaped by valuation of other peoples social behaviour, such that they empathize with fair opponents while favouring the physical punishment of unfair opponents, a finding that echoes recent evidence for altruistic punishment.

Model-Based Influences on Humans' Choices and Striatal Prediction Errors

Summary The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

When fear is near: Threat imminence elicits prefrontal-periaqueductal gray shifts in humans

Humans, like other animals, alter their behavior depending on whether a threat is close or distant. We investigated spatial imminence of threat by developing an active avoidance paradigm in which volunteers were pursued through a maze by a virtual predator endowed with an ability to chase, capture, and inflict pain. Using functional magnetic resonance imaging, we found that as the virtual predator grew closer, brain activity shifted from the ventromedial prefrontal cortex to the periaqueductal gray. This shift showed maximal expression when a high degree of pain was anticipated. Moreover, imminence-driven periaqueductal gray activity correlated with increased subjective degree of dread and decreased confidence of escape. Our findings cast light on the neural dynamics of threat anticipation and have implications for the neurobiology of human anxiety-related disorders.

Temporal difference models describe higher-order learning in humans

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network.

In fear extinction, an animal learns that a conditioned stimulus (CS) no longer predicts a noxious stimulus [unconditioned stimulus (UCS)] to which it had previously been associated, leading to inhibition of the conditioned response (CR). Extinction creates a new CS–noUCS memory trace, competing with the initial fear (CS–UCS) memory. Recall of extinction memory and, hence, CR inhibition at later CS encounters is facilitated by contextual stimuli present during extinction training. In line with theoretical predictions derived from animal studies, we show that, after extinction, a CS-evoked engagement of human ventromedial prefrontal cortex (VMPFC) and hippocampus is context dependent, being expressed in an extinction, but not a conditioning, context. Likewise, a positive correlation between VMPFC and hippocampal activity is extinction context dependent. Thus, a VMPFC–hippocampal network provides for context-dependent recall of human extinction memory, consistent with a view that hippocampus confers context dependence on VMPFC.

Differential Encoding of Losses and Gains in the Human Striatum

Studies on human monetary prediction and decision making emphasize the role of the striatum in encoding prediction errors for financial reward. However, less is known about how the brain encodes financial loss. Using Pavlovian conditioning of visual cues to outcomes that simultaneously incorporate the chance of financial reward and loss, we show that striatal activation reflects positively signed prediction errors for both. Furthermore, we show functional segregation within the striatum, with more anterior regions showing relative selectivity for rewards and more posterior regions for losses. These findings mirror the anteroposterior valence-specific gradient reported in rodents and endorse the role of the striatum in aversive motivational learning about financial losses, illustrating functional and anatomical consistencies with primary aversive outcomes such as pain.

Anxiety Reduction through Detachment: Subjective, Physiological, and Neural Effects

The ability to volitionally regulate emotions helps to adapt behavior to changing environmental demands and can alleviate subjective distress. We show that a cognitive strategy of detachment attenuates subjective and physiological measures of anticipatory anxiety for pain and reduces reactivity to receipt of pain itself. Using functional magnetic resonance imaging, we locate the potentialsite andsourceof this modulation of anticipatory anxiety in the medial prefrontal/anterior cingulate and anterolateral prefrontal cortex, respectively.

From Threat to Fear: The Neural Organization of Defensive Fear Systems in Humans

Postencounter and circa-strike defensive contexts represent two adaptive responses to potential and imminent danger. In the context of a predator, the postencounter reflects the initial detection of the potential threat, whereas the circa-strike is associated with direct predatory attack. We used functional magnetic resonance imaging to investigate the neural organization of anticipation and avoidance of artificial predators with high or low probability of capturing the subject across analogous postencounter and circa-strike contexts of threat. Consistent with defense systems models, postencounter threat elicited activity in forebrain areas, including subgenual anterior cingulate cortex (sgACC), hippocampus, and amygdala. Conversely, active avoidance during circa-strike threat increased activity in mid-dorsal ACC and midbrain areas. During the circa-strike condition, subjects showed increased coupling between the midbrain and mid-dorsal ACC and decreased coupling with the sgACC, amygdala, and hippocampus. Greater activity was observed in the right pregenual ACC for high compared with low probability of capture during circa-strike threat. This region showed decreased coupling with the amygdala, insula, and ventromedial prefrontal cortex. Finally, we found that locomotor errors correlated with subjective reports of panic for the high compared with low probability of capture during the circa-strike threat, and these panic-related locomotor errors were correlated with midbrain activity. These findings support models suggesting that higher forebrain areas are involved in early-threat responses, including the assignment and control of fear, whereas imminent danger results in fast, likely “hard-wired,” defensive reactions mediated by the midbrain.

The misbehavior of value and the discipline of the will

Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas instrumental conditioning concerns action learning. However, it is only through Pavlovian responses that Pavlovian prediction learning is evident, and these responses can act against the instrumental interests of the subjects. This can be seen in both experimental and natural circumstances. In this paper we study the consequences of importing this competition into a reinforcement learning context, and demonstrate the resulting effects in an omission schedule and a maze navigation task. The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined.

Emotion, decision making, and the amygdala

Emotion plays a critical role in many contemporary accounts of decision making, but exactly what underlies its influence and how this is mediated in the brain remain far from clear. Here, we review behavioral studies that suggest that Pavlovian processes can exert an important influence over choice and may account for many effects that have traditionally been attributed to emotion. We illustrate how recent experiments cast light on the underlying structure of Pavlovian control and argue that generally this influence makes good computational sense. Corresponding neuroscientific data from both animals and humans implicate a central role for the amygdala through interactions with other brain areas. This yields a neurobiological account of emotion in which it may operate, often covertly, to optimize rather than corrupt economic choice.