Local slow-wave activity over the right prefrontal cortex reveals individual risk preferences.

In everyday life, we have to make decisions under varying degrees of risk. Even though previous research has shown that the manipulation of sleep affects risky decision-making, it remains unknown whether individual, temporally stable neural sleep characteristics relate to individual differences in risk preferences. Here, we collected sleep data under normal conditions in fifty-four healthy adults using a portable high-density EEG at participants' home. Whole-brain corrected for multiple testing, we found that lower slow-wave activity (SWA, an indicator of sleep depth) in a cluster of electrodes over the right prefrontal cortex (PFC) is associated with higher individual risk propensity. Importantly, the association between local sleep depth and risk preferences remained significant when controlling for total sleep time and for time spent in deep sleep, i.e., sleep stages N2 and N3. Moreover, the association between risk preferences and SWA over the right PFC was very similar in all sleep cycles. Because the right PFC plays a central role in cognitive control functions, we speculate that local sleep depth in this area, as reflected by SWA, might serve as a dispositional indicator of self-regulatory ability, which in turn reflects risk preferences.

High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity.

It is a widely held belief that people's choices are less sensitive to changes in value as value increases. For example, the subjective difference between $11 and $12 is believed to be smaller than between $1 and $2. This idea is consistent with applications of the Weber-Fechner Law and divisive normalization to value-based choice and with psychological interpretations of diminishing marginal utility. According to random utility theory in economics, smaller subjective differences predict less accurate choices. Meanwhile, in the context of sequential sampling models in psychology, smaller subjective differences also predict longer response times. Based on these models, we would predict decisions between high-value options to be slower and less accurate. In contrast, some have argued on normative grounds that choices between high-value options should be made with less caution, leading to faster and less accurate choices. Here, we model the dynamics of the choice process across three different choice domains, accounting for both discriminability and response caution. Contrary to predictions, we mostly observe faster and more accurate decisions (i.e., higher drift rates) between high-value options. We also observe that when participants are alerted about incoming high-value decisions, they exert more caution and not less. We rule out several explanations for these results, using tasks with both subjective and objective values. These results cast doubt on the notion that increasing value reduces discriminability.

Who initiates punishment, who joins punishment? Disentangling types of third-party punishers by neural traits.

The act of punishing unfair behavior by unaffected observers (i.e., third-party punishment) is a crucial factor in the functioning of human societies. In everyday life, we see different types of individuals who punish. While some individuals initiate costly punishment against an unfair person independently of what other observers do (independent punishers), others condition their punishment engagement on the presence of another person who punishes (conditional punishers). Still others do not want to partake in any sort of punishment (nonpunishers). Although these distinct behavioral types have a divergent impact on human society, the sources of heterogeneity are poorly understood. We present novel laboratory evidence on the existence of these three types. We use anatomical brain characteristics in combination with stated motives to characterize these types. Findings revealed that independent punishers have larger gray matter volume in the right temporo-parietal junction compared to conditional punishers and nonpunishers, an area involved in social cognition. Conditional punishers are characterized by larger gray matter volume in the right dorsolateral prefrontal cortex, a brain area known to be involved in behavioral control and strategic reasoning, compared to independent punishers and nonpunishers. Finally, both independent punishers and nonpunishers are characterized by larger gray matter volume in an area involved in the processing of social and monetary rewards, that is, the bilateral caudate. By using a neural trait approach, we were able to differentiate these three types clearly based on their neural signatures, allowing us to shed light on the underlying psychological mechanisms.

Different Behavioral Types of Distributional Preferences Are Characterized by Distinct Neural Signatures.

There are many situations where resources are distributed between two parties and where the deciding party has information about the initial distribution and can change its outcome, for example, the allocation of budget for funds or bonuses, where the deciding party might have self-interested motives. Although the neural underpinnings of distributional preferences of resources have been extensively studied, it remains unclear if there are different types of distributional preferences and if these types underlie different disposing neural signatures. We used source-localized resting EEG in combination with a data-driven clustering approach to participants' behavior in a distribution game in order to disentangle the neural sources of the different types of distributional preferences. Our findings revealed four behavioral types: Maximizing types always changed initial distributions to maximize their personal outcomes, and compliant types always left initial distributions unchanged. Disadvantage-averse types only changed initial distributions if they received less than the other party did, and equalizing types primarily changed initial distributions to fair distributions. These behavioral types differed regarding neural baseline activation in the right inferior frontal gyrus. Maximizing and compliant types showed the highest baseline activation, followed by disadvantage-averse types and equalizing types. Furthermore, maximizing types showed significantly higher baseline activation in the left orbitofrontal cortex compared to compliant types. Taken together, our findings show that different types of distributional preferences are characterized by distinct neural signatures, which further imply differences in underlying psychological processes in decision-making.