Terry Lohrenz

The rupture and repair of cooperation in borderline personality disorder.

To sustain or repair cooperation during a social exchange, adaptive creatures must understand social gestures and the consequences when shared expectations about fair exchange are violated by accident or intent. We recruited 55 individuals afflicted with borderline personality disorder (BPD) to play a multiround economic exchange game with healthy partners. Behaviorally, individuals with BPD showed a profound incapacity to maintain cooperation, and were impaired in their ability to repair broken cooperation on the basis of a quantitative measure of coaxing. Neurally, activity in the anterior insula, a region known to respond to norm violations across affective, interoceptive, economic, and social dimensions, strongly differentiated healthy participants from individuals with BPD. Healthy subjects showed a strong linear relation between anterior insula response and both magnitude of monetary offer received from their partner (input) and the amount of money repaid to their partner (output). In stark contrast, activity in the anterior insula of BPD participants was related only to the magnitude of repayment sent back to their partner (output), not to the magnitude of offers received (input). These neural and behavioral data suggest that norms used in perception of social gestures are pathologically perturbed or missing altogether among individuals with BPD. This game-theoretic approach to psychopathology may open doors to new ways of characterizing and studying a range of mental illnesses.

To Detect and Correct: Norm Violations and Their Enforcement

Compliance with social norms requires neural signals related both to the norm and to deviations from it. Recent work using economic games between two interacting subjects has uncovered brain responses related to norm compliance and to an individuals strategic outlook during the exchange. These brain responses possess a provocative relationship to those associated with negative emotional outcomes, and hint at computational depictions of emotion processing.

Computational Substrates of Norms and Their Violations during Social Exchange

Social norms in humans constrain individual behaviors to establish shared expectations within a social group. Previous work has probed social norm violations and the feelings that such violations engender; however, a computational rendering of the underlying neural and emotional responses has been lacking. We probed norm violations using a two-party, repeated fairness game (ultimatum game) where proposers offer a split of a monetary resource to a responder who either accepts or rejects the offer. Using a norm-training paradigm where subject groups are preadapted to either high or low offers, we demonstrate that unpredictable shifts in expected offers creates a difference in rejection rates exhibited by the two responder groups for otherwise identical offers. We constructed an ideal observer model that identified neural correlates of norm prediction errors in the ventral striatum and anterior insula, regions that also showed strong responses to variance-prediction errors generated by the same model. Subjective feelings about offers correlated with these norm prediction errors, and the two signals displayed overlapping, but not identical, neural correlates in striatum, insula, and medial orbitofrontal cortex. These results provide evidence for the hypothesis that responses in anterior insula can encode information about social norm violations that correlate with changes in overt behavior (changes in rejection rates). Together, these results demonstrate that the brain regions involved in reward prediction and risk prediction are also recruited in signaling social norm violations.

Neural signatures of strategic types in a two-person bargaining game

The management and manipulation of our own social image in the minds of others requires difficult and poorly understood computations. One computation useful in social image management is strategic deception: our ability and willingness to manipulate other peoples beliefs about ourselves for gain. We used an interpersonal bargaining game to probe the capacity of players to manage their partners beliefs about them. This probe parsed the group of subjects into three behavioral types according to their revealed level of strategic deception; these types were also distinguished by neural data measured during the game. The most deceptive subjects emitted behavioral signals that mimicked a more benign behavioral type, and their brains showed differential activation in right dorsolateral prefrontal cortex and left Brodmann area 10 at the time of this deception. In addition, strategic types showed a significant correlation between activation in the right temporoparietal junction and expected payoff that was absent in the other groups. The neurobehavioral types identified by the game raise the possibility of identifying quantitative biomarkers for the capacity to manipulate and maintain a social image in another persons mind.

Sub-Second Dopamine Detection in Human Striatum

Fast-scan cyclic voltammetry at carbon fiber microelectrodes allows rapid (sub-second) measurements of dopamine release in behaving animals. Herein, we report the modification of existing technology and demonstrate the feasibility of making sub-second measurements of dopamine release in the caudate nucleus of a human subject during brain surgery. First, we describe the modification of our electrodes that allow for measurements to be made in a human brain. Next, we demonstrate in vitro and in vivo, that our modified electrodes can measure stimulated dopamine release in a rat brain equivalently to previously determined rodent electrodes. Finally, we demonstrate acute measurements of dopamine release in the caudate of a human patient during DBS electrode implantation surgery. The data generated are highly amenable for future work investigating the relationship between dopamine levels and important decision variables in human decision-making tasks.

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

Significance There is an abundance of circumstantial evidence (primarily work in nonhuman animal models) suggesting that dopamine transients serve as experience-dependent learning signals. This report establishes, to our knowledge, the first direct demonstration that subsecond fluctuations in dopamine concentration in the human striatum combine two distinct prediction error signals: (i) an experience-dependent reward prediction error term and (ii) a counterfactual prediction error term. These data are surprising because there is no prior evidence that fluctuations in dopamine should superpose actual and counterfactual information in humans. The observed compositional encoding of “actual” and “possible” is consistent with how one should “feel” and may be one example of how the human brain translates computations over experience to embodied states of subjective feeling. In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.

Nonpolitical Images Evoke Neural Predictors of Political Ideology

Summary Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy [1, 2]. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms [3–7] that may serve to defend against environmental challenges like contamination and physical threat [8–12]. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion) [13]. We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants’ conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject’s political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated.

Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles

Significance Asset price bubbles are an important example of human group decision making gone awry, but the behavioral and neural underpinnings of bubble dynamics remain mysterious. In multisubject markets determined by 11–23 subjects, with 2–3 subjects simultaneously scanned using functional MRI, we show how behavior and brain activity interact during bubbles. Nucleus accumbens (NAcc) activity tracks the price bubble and predicts future price changes. Traders who buy more aggressively based on NAcc signals earn less. High-earning traders have early warning signals in the anterior insular cortex before prices reach a peak, and sell coincidently with that signal, precipitating the crash. These experiments could help understand other cases in which human groups badly miscompute the value of actions or events. Groups of humans routinely misassign value to complex future events, especially in settings involving the exchange of resources. If properly structured, experimental markets can act as excellent probes of human group-level valuation mechanisms during pathological overvaluations—price bubbles. The connection between the behavioral and neural underpinnings of such phenomena has been absent, in part due to a lack of enabling technology. We used a multisubject functional MRI paradigm to measure neural activity in human subjects participating in experimental asset markets in which endogenous price bubbles formed and crashed. Although many ideas exist about how and why such bubbles may form and how to identify them, our experiment provided a window on the connection between neural responses and behavioral acts (buying and selling) that created the bubbles. We show that aggregate neural activity in the nucleus accumbens (NAcc) tracks the price bubble and that NAcc activity aggregated within a market predicts future price changes and crashes. Furthermore, the lowest-earning subjects express a stronger tendency to buy as a function of measured NAcc activity. Conversely, we report a signal in the anterior insular cortex in the highest earners that precedes the impending price peak, is associated with a higher propensity to sell in high earners, and that may represent a neural early warning signal in these subjects. Such markets could be a model system to understand neural and behavior mechanisms in other settings where emergent group-level activity exhibits mistaken belief or valuation.

Computational phenotyping of two-person interactions reveals differential neural response to depth-of-thought.

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subjects depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans.

Belief about nicotine selectively modulates value and reward prediction error signals in smokers

Significance Nicotine is the primary addictive substance in tobacco, which stimulates neural pathways mediating reward processing. However, pure biochemical explanations are not sufficient to account for the difficulty in quitting and remaining smoke-free among smokers, and in fact cognitive factors are now considered to contribute critically to addiction. Using model-based functional neuroimaging, we show that smokers’ prior beliefs about nicotine specifically impact learning signals defined by principled computational models of mesolimbic dopamine systems. We further demonstrate that these specific changes in neural signaling are accompanied by measurable changes in smokers’ choice behavior. Our findings suggest that subjective beliefs can override the physical presence of a powerful drug like nicotine by modulating learning signals processed in the brain’s reward system. Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers’ prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers’ beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of “no nicotine in cigarette” (compared with “nicotine in cigarette”) strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers’ choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.