A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

[EN] Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.This research was supported by grants from the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Ontario Ministry of Economic Development and Innovation (MEDI). We thank Dr. Hongying Wang for invaluable help with drug administration and animal careHassani, SA.; Oemisch, M.; Balcarras, M.; Westendorff, S.; Ardid-Ramírez, JS.; Van Der Meer, MA.; Tiesinga, P.... (2017). 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Influences of State and Trait Affect on Behavior, Feedback-Related Negativity, and P3b in the Ultimatum Game

The present study investigates how different emotions can alter social bargaining behavior. An important paradigm to study social bargaining is the Ultimatum Game. There, a proposer gets a pot of money and has to offer part of it to a responder. If the responder accepts, both players get the money as proposed by the proposer. If he rejects, none of the players gets anything. Rational choice models would predict that responders accept all offers above 0. However, evidence shows that responders typically reject a large proportion of all unfair offers. We analyzed participants’ behavior when they played the Ultimatum Game as responders and simultaneously collected electroencephalogram data in order to quantify the feedback-related negativity and P3b components. We induced state affect (momentarily emotions unrelated to the task) via short movie clips and measured trait affect (longer-lasting emotional dispositions) via questionnaires. State happiness led to increased acceptance rates of very unfair offers. Regarding neurophysiology, we found that unfair offers elicited larger feedback-related negativity amplitudes than fair offers. Additionally, an interaction of state and trait affect occurred: high trait negative affect (subsuming a variety of aversive mood states) led to increased feedback-related negativity amplitudes when participants were in an angry mood, but not if they currently experienced fear or happiness. We discuss that increased rumination might be responsible for this result, which might not occur, however, when people experience happiness or fear. Apart from that, we found that fair offers elicited larger P3b components than unfair offers, which might reflect increased pleasure in response to fair offers. Moreover, high trait negative affect was associated with decreased P3b amplitudes, potentially reflecting decreased motivation to engage in activities. We discuss implications of our results in the light of theories and research on depression and anxiety

The neural signatures of egocentric bias in normative decision-making

Bargaining parties often disagree on what fair is, due to the reason that people are prone to believe that what favors oneself is fair, i.e., an egocentric bias. In this study, we investigated the neural signatures underlying egocentric bias in fairness decision-making, conjoining an adapted ultimatum game (UG) with event-related fMRI and functional connectivity. Participants earned monetary rewards with a partner in a production stage, wherein their contributions to the earnings were manipulated. Afterwards, the joint earnings were randomly divided, and the distribution was presented simultaneously with contribution information to participants, who accepted/rejected distributions of earnings as the same manner in standard UG. We identified an egocentric bias in fairness decisions, such that participants frequently rejected self-contributed disadvantageous outcomes, but much less so in response to other-contributed advantageous outcomes, although both involved mismatch between contribution and payoff. This bias was underpinned by regions involved in representing fairness norms, including the anterior insula and dorsal anterior cingulate cortex (dACC). Furthermore, the thalamus activity was predictive of the bias, such that the level of egocentric bias decreased as a function of the activation level of the thalamus. Finally, our functional-connectivity findings indicated that the thalamus worked together with insula and dACC to modulate behavioral egocentric bias in fairness-related decisions. Our findings uncover the neural basis underlying the modulation of egocentric bias in normative decision-making, and highlight the role of neural circuits associated with norm enforcement in this phenomenon