Pain feels different in different social contexts, yet the mechanisms behind social pain modulation remain poorly understood. To elucidate the impact of social context on pain processing, we investigated how group membership, one of the most important social context factors, shapes pain relief behaviourally and neurally in humans undergoing functional neuroimaging. Participants repeatedly received pain relief from a member of their own group (ingroup treatment) or a member of a disliked outgroup (outgroup treatment). We observed a decrease in pain ratings and anterior insula (AI) pain responses after outgroup treatment, but not after ingroup treatment. Moreover, path analyses revealed that the outgroup treatment induced a stronger relief learning in the AI, which in turn altered pain processing, in particular if the participant entered the treatment with a negative impression toward the outgroup individual. The finding of enhanced analgesia after outgroup treatment is relevant for intergroup clinical settings. More generally, we found that group membership affects pain responses through neural learning and we thus elucidate one possible mechanism through which social context impacts pain processing.
In this study, we investigated how spatial, temporal, and effort dimensions collectively influence reward discounting across different age groups. Using a 3D simulated environment, participants aged 7 to 77 chose between a nearby tree with fewer apples and a distant tree with more apples, requiring more time and effort to reach. Our findings revealed that participants initially preferred the distant tree with more apples, but as the difference in rewards decreased, they became increasingly more likely to choose the closer, less effortful option. This shift suggests a gradual re-evaluation of effort versus reward as the perceived value difference diminishes. Contrary to our hypothesis, younger participants were less likely to discount effortful rewards compared to middle-aged and older adults. These results underscore the complexity of decision-making across different dimensions and age groups, highlighting the need for more ecologically valid research in understanding how humans make trade-offs in naturalistic scenarios.
Abstract Studies in Social Neuroeconomics have consistently reported activation in social cognition regions during interactive economic games suggesting mentalizing during economic choice. It remains important to test the involvement of neural activity associated with mentalizing in an economic games context within the same sample of participants performing the same task. We designed a novel version of the classic false-belief task in which participants observed interactions between agents in the ultimatum and trust games and were subsequently asked to infer the agents’ beliefs. We compared activation patterns during the economic-games false-belief task to those during the classic false-belief task using conjunction analyses. We find significant overlap in left TPJ, and dmPFC, as well as temporal pole during two task phases: belief formation and belief inference. Moreover, gPPI analyses show that during belief formation right TPJ is a target of both left TPJ and right temporal pole (TP) seed regions, while during belief inferences all seed regions show interconnectivity with each other. These results indicate that across different task types and phases, mentalizing is associated with activation and connectivity across central nodes of the social cognition network. Importantly, this is the case in the context of the novel economic-games and classic false-belief tasks.
While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.
The ability to correctly estimate the probability of one's choices being correct is fundamental to optimally re-evaluate previous choices or to arbitrate between different decision strategies. Experimental evidence nonetheless suggests that this metacognitive process—confidence judgment- is susceptible to numerous biases. Here, we investigate the effect of outcome valence (gains or losses) on confidence while participants learned stimulus-outcome associations by trial-and-error. In two experiments, participants were more confident in their choices when learning to seek gains compared to avoiding losses, despite equal difficulty and performance between those two contexts. Computational modelling revealed that this bias is driven by the context-value, a dynamically updated estimate of the average expected-value of choice options, necessary to explain equal performance in the gain and loss domain. The biasing effect of context-value on confidence, revealed here for the first time in a reinforcement-learning context, is therefore domain-general, with likely important functional consequences. We show that one such consequence emerges in volatile environments, where the (in)flexibility of individuals' learning strategies differs when outcomes are framed as gains or losses. Despite apparent similar behavior- profound asymmetries might therefore exist between learning to avoid losses and learning to seek gains.
The right posterior parietal cortex (PPC) is implicated in spatial attention, but its specific role in emotional spatial attention remains unclear. In this study, we combined inhibitory transcranial magnetic stimulation (TMS) with a fear-conditioning paradigm to test the role of the right PPC in attentional control of task-irrelevant threatening distractors. In a sham-controlled within-subject design, 1-Hz repetitive TMS was applied to the left and right PPC after which participants performed a visual search task with a distractor that was either associated with a loud noise burst (threat) or not (non-threat). Results demonstrated attentional capture across all conditions as evidenced by the typical reaction time costs of the distractor. However, only after inhibitory rTMS to the right PPC reaction time cost in the threatening distractor condition was increased relative to the non-threatening distractor condition, suggesting that attention lingered longer on the threatening distractor. We propose that the right PPC is involved in disengagement of attention from emotionally salient stimuli in order to re-orient attention to task relevant stimuli and may have implications for anxiety disorders associated with difficulties to disengage from threatening stimuli.
Anxiety is a common affective state, characterized by the subjectively unpleasant feelings of dread over an anticipated event. Anxiety is suspected to have important negative consequences on cognition, decision-making, and learning. Yet, despite a recent surge in studies investigating the specific effects of anxiety on reinforcement-learning, no coherent picture has emerged. Here, we investigated the effects of incidental anxiety on instrumental reinforcement-learning, while addressing several issues and defaults identified in a focused literature review. We used a rich experimental design, featuring both a learning and a transfer phase, and a manipulation of outcomes valence (gains vs losses). In two variants (N = 2 × 50) of this experimental paradigm, incidental anxiety was induced with an established threat-of-shock paradigm. Model-free results show that incidental anxiety effects seem limited to a small, but specific increase in postlearning performance measured by a transfer task. A comprehensive modeling effort revealed that, irrespective of the effects of anxiety, individuals give more weight to positive than negative outcomes, and tend to experience the omission of a loss as a gain (and vice versa). However, in line with results from our targeted literature survey, isolating specific computational effects of anxiety on learning per se proved to be challenging. Overall, our results suggest that learning mechanisms are more complex than traditionally presumed, and raise important concerns about the robustness of the effects of anxiety previously identified in simple reinforcement-learning studies. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
There are two regularities we have learned from experimental studies of choice under risk. The first is that the majority of people weigh objective probabilities non-linearly. The second regularity, although less commonly acknowledged, is that there is a large amount of heterogeneity in how people distort probabilities. Despite of this, little effort has been made to identify the source of heterogeneity. In this paper, we explore the possibility that the probability distortions are linked to the personality profile of the decision maker. Using four widely utilized personality tests, we classify participants into three distinct personality types and find that these types have different risk characteristics. Particularly, the trait of motivation plays a role in explaining the attraction of gambling, while the trait of impulsiveness affects the discriminability of non-extreme probabilities. Our results suggest heterogeneity in probability distortions may be explained by personality profiles, which can be elicited though standard questionnaires.
The green growth paradigm has gained much attention from various governments worldwide as a guiding strategy for national and sectoral growth strategies. There is, however, little knowledge on how to integrate green growth into key natural resource sectors, such as water. This paper explains the origins and underlying concepts of green growth, and assesses its potential in the Jordanian water sector. Using a green growth diagnostic model, we analyze six key industries in the Jordanian water sector that can be an engine for green growth and the achievement of key sector-related Sustainable Development Goals (SDGs). In addition, four innovative business models are presented which exemplify the best practices and future directions of the water sector in Jordan. The results and recommendations support the strategic decision-making process of linking economic growth and sustainability, and encouraging private investments.
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