Political Extremism in a Global Perspective

Examining data from the World Value Survey about left-right political orientation, the paper explores political extremism among common people worldwide. Our analysis reveals (i) a positive correlation between left-wing and right-wing extremism across countries, (ii) an average rise in political extremism globally in the last decade, (iii) greater political extremism in less developed countries, (iv) and a surge, during the last decade, in political extremism for less developed countries and for countries where development has not met expectations. Besides offering a picture of how successful political extremism is globally, our investigation provides insight into the driving forces behind this phenomenon

Interaction of goal-directed and pavlovian systems in aversive domains

Recent neuroscientific models of human behavior distinguish between different cognitive controllers: two instrumental systems (goal-directed and habitual) that maximize utility through learned actions, and a so-called Pavlovian system, which implements innate reactive responses. Although the interaction between instrumental and Pavlovian controllers has been suggested as a key process underlying emotional phenomena and surprising forms of misbehavior, few is known about it, especially in the sensorimotor aversive domain. With a combined experimental and computational approach, we study the interactions between instrumental (goal-directed) and Pavlovian processes in the aversive domain. First, we present a human experiment in which goal-directed and Pavlovian systems compete in order to control responses. The results indicate that Pavlovian processes can significantly interfere with goal-directed behavior. Second, we compare four alternative Bayesian models for their accuracy in modeling human performance. The results indicate a better fit for an architecture in which the Pavlovian controller can use both model-based and model-free features

The Value of Foresight: How Prospection Affects Decision-Making

Traditional theories of decision-making assume that utilities are based on the intrinsic value of outcomes; in turn, these values depend on associations between expected outcomes and the current motivational state of the decision-maker. This view disregards the fact that humans (and possibly other animals) have prospection abilities, which permit anticipating future mental processes and motivational and emotional states. For instance, we can evaluate future outcomes in light of the motivational state we expect to have when the outcome is collected, not (only) when we make a decision. Consequently, we can plan for the future and choose to store food to be consumed when we expect to be hungry, not immediately. Furthermore, similarly to any expected outcome, we can assign a value to our anticipated mental processes and emotions. It has been reported that (in some circumstances) human subjects prefer to receive an unavoidable punishment immediately, probably because they are anticipating the dread associated with the time spent waiting for the punishment. This article offers a formal framework to guide neuroeconomic research on how prospection affects decision-making. The model has two characteristics. First, it uses model-based Bayesian inference to describe anticipation of cognitive and motivational processes. Second, the utility-maximization process considers these anticipations in two ways: to evaluate outcomes (e.g., the pleasure of eating a pie is evaluated differently at the beginning of a dinner, when one is hungry, and at the end of the dinner, when one is satiated), and as outcomes having a value themselves (e.g., the case of dread as a cost of waiting for punishment). By explicitly accounting for the relationship between prospection and value, our model provides a framework to reconcile the utility-maximization approach with psychological phenomena such as planning for the future and dread

Planning in view of future needs: a bayesian model of anticipated motivation

Traditional neuroeconomic theories of decision-making assume that utilities are based on intrinsic values of outcomes and that those values depend on how salient are outcomes in relation to the current motivational state. The fact that humans, and possibly also other animals, are able to plan in view of future motivations is not accounted by this view. So far, it is not clear which are the structures and the computational mechanisms employed by the brain during these processes. In this article, we present a Bayesian computational model that describes how the brain considers future motivations and assigns value to outcomes in relation to this information. We compare our model of anticipated motivation with a model that implements the standard perspective in decision-making and assigns value only based on the animal\u27s current motivations. The results of our simulations indicate an advantage of the model of anticipated motivation in volatile environments. Finally we connect our computational proposal to animal and human studies on prospection and foresight abilities and to neurophysiological investigations on their neural underpinnings

Planning in view of future needs: a bayesian model of anticipated motivation

Traditional neuroeconomic theories of decision-making assume that utilities are based on intrinsic values of outcomes and that those values depend on how salient are outcomes in relation to the current motivational state. The fact that humans, and possibly also other animals, are able to plan in view of future motivations is not accounted by this view. So far, it is not clear which are the structures and the computational mechanisms employed by the brain during these processes. In this article, we present a Bayesian computational model that describes how the brain considers future motivations and assigns value to outcomes in relation to this information. We compare our model of anticipated motivation with a model that implements the standard perspective in decision-making and assigns value only based on the animal\u27s current motivations. The results of our simulations indicate an advantage of the model of anticipated motivation in volatile environments. Finally we connect our computational proposal to animal and human studies on prospection and foresight abilities and to neurophysiological investigations on their neural underpinnings

Aberrant force processing in schizophrenia

Initially considered as mere side effects of antipsychotic medication, there is now evidence that motor and somatosensory disturbances precede the onset of the illness and can be found in drug-naive patients. However, research on the topic is scarce. Here, we were interested in assessing the accuracy of the neural signal in detecting parametric variations of force linked to a voluntary motor act and a received tactile sensation, either self-generated or externally generated. Patients with a diagnosis of schizophrenia and healthy controls underwent functional magnetic resonance imaging while asked to press, or abstain from pressing, a lever in order to match a visual target force. Forces, exerted and received, varied on 10 levels from 0.5 N to 5 N in 0.5 N increments. Healthy participants revealed a positive correlation between force and activity in contralateral primary somatosensory area (S1) when performing a movement as well as when receiving a tactile sensation but only when this was externally, and not self-, generated. Patients showed evidence of altered force signaling in both motor and tactile conditions, as well as increased correlation with force when tactile sensation was self-generated. Findings are interpreted in line with accounts of predictive and sensory integration mechanisms and point toward alterations in the encoding of parametric forces in the motor and somatosensory domain in patients affected by schizophrenia

Multiple value signals in dopaminergic midbrain and their role in avoidance contexts

The role of dopaminergic brain regions in avoidance behaviour is unclear. Active avoidance requires motivation, and the latter is linked to increased activity in dopaminergic regions. However, avoidance is also often tethered to the prospect of punishment, a state typically characterized by below baseline levels of dopaminergic function. Avoidance has been considered from the perspective of two-factor theories where the prospect of safety is considered to act as a surrogate for reward, leading to dopamine release and enhanced motivational drive. Using fMRI we investigated predictions from two-factor theory by separating the neural representation of a conventional net expected value, which is negative in the case of avoidance, from an adjusted expected value which factors in a possibility of punishment and is larger for both big rewards and big (predictably avoidable) punishments. We show that neural responses in ventral striatum and ventral tegmental area/substantial nigra (VTA/SN) covaried with net expected value. Activity in VTA/SN also covaried with an adjusted expected value, as did activity in anterior insula. Consistent with two-factor theory models, the findings indicate that VTA/SN and insula process an adjusted expected value during avoidance behaviour

Risk preference and choice stochasticity during decisions for other people

In several contexts, such as finance and politics, people make choices that are relevant for others but irrelevant for oneself. Focusing on decision-making under risk, we compared monetary choices made for one’s own interest with choices made on behalf of an anonymous individual. Consistent with the previous literature, other-interest choices were characterized by an increased gambling propensity. We also investigated choice stochasticity, which captures how much decisions vary in similar conditions. An aspect related to choice stochasticity is how much decisions are tuned to the option values, and we found that this was higher during self-interest than during other-interest choices. This effect was observed only in individuals who reported a motivation to distribute rewards unequally, suggesting that it may (at least partially) depend on a motivation to make accurate decisions for others. Our results indicate that, during decision-making under risk, choices for other people are characterized by a decreased tuning to the values of the options, in addition to enhanced risk seeking

Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76–81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied—functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery

Dopamine Increases a Value-Independent Gambling Propensity

Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine’s role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline (ie, value-independent) gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options—a finding with implications for understanding a range of reward-related psychopathologies including addiction