Decision neuroscience: neuroeconomics

Mind, brain and education-Neuromechanisms during child development

BACKGROUND AND AIMS

Educational neuroscience has emerged as an interdisciplinary field aimed at elucidating the neurobiological underpinnings of learning and educational outcomes. By synthesizing findings from diverse research endeavours, this Editorial aims to delineate the intricate interplay between neural processes and educational experiences, shedding light on the factors that shape cognitive development and learning trajectories in children.

RESULTS

This Editorial highlights significant advancements, spanning investigations into neural mechanisms, cognitive development and educational interventions on the basis of four exemplary topics and their effects on academic learning and achievement: student's academic self-concept, (cyber-)bullying, reading skills/dyslexia and a growth mindset intervention. Summaries of the four empirical contributions in this special issue are presented and discussed in relation to how they provide insight into the dynamic interplay between neural mechanisms and environmental influences, underscoring the role of early experiences in sculpting brain development and shaping educational outcomes. Furthermore, the integration of neuroscientific techniques (e.g., fMRI, eye-tracking) with educational research methodologies has provided novel insights into the neural correlates of learning processes, executive functions and socio-emotional development during childhood.

CONCLUSIONS

In conclusion, the pivotal role of Educational Neuroscience in bridging the gap between neuroscience and education is highlighted. By elucidating the neurobiological foundations of learning, this interdisciplinary field offers valuable insights for informing evidence-based educational practices and interventions tailored to individual learning profiles. Moving forward, continued collaboration between researchers, educators and policymakers is essential to harnessing the full potential of Educational Neuroscience in promoting cognitive growth and academic success across diverse learner populations.

Ketamine reverses stress-induced hypersensitivity to sunk costs

How mood interacts with information processing in the brain is thought to mediate the maladaptive behaviors observed in depressed individuals. However, the neural mechanisms underlying impairments in emotion-cognition interactions are poorly understood. This includes influencing the balance between how past-sensitive vs. future-looking one is during decision-making. Recent insights from the field of neuroeconomics offer novel approaches to study changes in such valuation processes in a manner that is biologically tractable and readily translatable across species. We recently discovered that rodents are sensitive to "sunk costs" - a feature of higher cognition previously thought to be unique to humans. The sunk costs bias describes the phenomenon in which an individual overvalues and escalates commitment to continuing an ongoing endeavor, even if suboptimal, as a function of irrecoverable past (sunk) losses - information that, according to classic economic theory, should be ignored. In the present study, mice were exposed to chronic social defeat stress paradigm, a well-established animal model used for the study of depression. Mice were then tested on our longitudinal neuroeconomic foraging task, Restaurant Row. We found mice exposed to this severe stressor displayed an increased sensitivity to sunk costs, without altering overall willingness to wait. Mice were then randomly assigned to receive a single intraperitoneal injection of either saline or ketamine (20 mg/kg). We discovered that stress-induced hypersensitivity to sunk costs was renormalized following a single dose of ketamine. Interestingly, in non-defeated mice, ketamine treatment completely abolished sunk cost sensitivity, causing mice to no longer value irrecoverable losses during re-evaluation decisions who instead based choices solely on the future investment required to obtain a goal. These findings suggest that the antidepressant effects of ketamine may be mediated in part through changes in the processing of past-sensitive information during on-going decision-making, reducing its weight as a potential source of cognitive dissonance that could modulate behavior and instead promoting more future-thinking behavior.

Blunted Neurobehavioral Loss Anticipation Predicts Relapse to Stimulant Drug Use

BACKGROUND

Patients with stimulant use disorder experience high rates of relapse. While neurobehavioral mechanisms involved in initiating drug use have been studied extensively, less research has focused on relapse.

METHODS

To assess motivational processes involved in relapse and diagnosis, we acquired functional magnetic resonance imaging responses to nondrug (monetary) gains and losses in detoxified patients with stimulant use disorder (n = 68) and community control participants (n = 42). In a prospective multimodal design, we combined imaging of brain function, brain structure, and behavior to longitudinally track subsequent risk for relapse.

RESULTS

At the 6-month follow-up assessment, 27 patients remained abstinent, but 33 had relapsed. Patients with blunted anterior insula (AIns) activity during loss anticipation were more likely to relapse, an association that remained robust after controlling for potential confounds (i.e., craving, negative mood, years of use, age, and gender). Lower AIns activity during loss anticipation was associated with lower self-reported negative arousal to loss cues and slower behavioral responses to avoid losses, which also independently predicted relapse. Furthermore, AIns activity during loss anticipation was associated with the structural coherence of a tract connecting the AIns and the nucleus accumbens, as was functional connectivity between the AIns and nucleus accumbens during loss processing. However, these neurobehavioral responses did not differ between patients and control participants.

CONCLUSIONS

Taken together, the results of the current study show that neurobehavioral markers predicted relapse above and beyond conventional self-report measures, with a cross-validated accuracy of 72.7%. These findings offer convergent multimodal evidence that implicates blunted avoidance motivation in relapse to stimulant use and may therefore guide interventions targeting individuals who are most vulnerable to relapse.

Conceptualisation of Uncertainty in Decision Neuroscience Research: Do We Really Know What Types of Uncertainties The Measured Neural Correlates Relate To?

In the article "What are neural correlates neural correlates of?" published in the journal BioSocieties, Gabriel Abend points out that neuroscientists cannot avoid philosophical questions concerning the conceptualization and operationalization of social-psychological phenomena they deal with at the physiological level. In this article, we build on Abend's thesis and, through a systematic literature review of decision neuroscience studies, test it with the example of the social-psychological phenomenon of uncertainty in decision making. In this paper, we provide an overview of studies that appropriately attempt to conceptualise uncertainty, and then use these studies to analyse papers looking for neural correlates of uncertainty. Based on a systematic review of studies, we investigate what types of uncertainty authors in the field of decision neuroscience address and define, what criteria they use to distinguish between these types, what problems are associated with their conceptualization, and whether the neural correlates of different types of uncertainty can be accurately identified. The paper concludes that, particularly in the economic context, a collaboration between the natural and social sciences works well, and neuroscience studies use economic conceptualizations of uncertainty that are further developed by sophisticated decision tasks. However, the paper also highlights problematic aspects that obscure the understanding of the phenomena under study. These include the lack of criteria for distinguishing between different types of phenomena, the unclear use of the general concept of uncertainty, and the confusion of phenomena or their erroneous synonymous use.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques

(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634-0.767, and the corresponding value range for hypomanic symptoms was 0.770-0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants' mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.

Transition to substance use disorders: impulsivity for reward and learning from reward

Substance dependence constitutes a profound societal burden. Although large numbers of individuals use licit or illicit substances, few transition to dependence. The specific factors influencing this transition are not well understood. Substance-dependent individuals tend to be swayed by the immediate rewards of drug taking, but are often insensitive to delayed negative consequences of their behavior. Dependence is consequently associated with impulsivity for reward and atypical learning from feedback. Behavioral impulsivity is indexed using tasks measuring spontaneous decision-making and capacity to control impulses. While evidence indicates drug taking exacerbates behavioral impulsivity for reward, animal and human studies of drug naïve populations demonstrate it might precede any drug-related problems. Research suggests dependent individuals are also more likely to learn from rewarding (relative to punishing) feedback. This may partly explain why substance-dependent individuals fail to modify their behavior in response to negative outcomes. This enhanced learning from reward may constitute a further pre-existing risk factor for substance dependence. Although impulsivity for reward and preferential learning from rewarding feedback are both underpinned by a compromised dopaminergic system, few studies have examined the relationship between these two mechanisms. The interplay of these processes may help enrich understanding of why some individuals transition to substance dependence.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

Successful metabolic control in diabetes type 1 depends on individual neuroeconomic and health risk-taking decision endophenotypes: a new target in personalized care

BACKGROUND

Neurobehavioral decision profiles have often been neglected in chronic diseases despite their direct impact on major public health issues such as treatment adherence. This remains a major concern in diabetes, despite intensive efforts and public awareness initiatives regarding its complications. We hypothesized that high rates of low adherence are related to risk-taking profiles associated with decision-making phenotypes. If this hypothesis is correct, it should be possible to define these endophenotypes independently based both on dynamic measures of metabolic control (HbA1C) and multidimensional behavioral profiles.

METHODS

In this study, 91 participants with early-stage type 1 diabetes fulfilled a battery of self-reported real-world risk behaviors and they performed an experimental task, the Balloon Analogue Risk Task (BART).

RESULTS

K-means and two-step cluster analysis suggest a two-cluster solution providing information of distinct decision profiles (concerning multiple domains of risk-taking behavior) which almost perfectly match the biological partition, based on the division between stable or improving metabolic control (MC, N = 49) v. unstably high or deteriorating states (NoMC, N = 42). This surprising dichotomy of behavioral phenotypes predicted by the dynamics of HbA1C was further corroborated by standard statistical testing. Finally, the BART game enabled to identify groups differences in feedback learning and consequent behavioral choices under ambiguity, showing distinct group choice behavioral patterns.

CONCLUSIONS

These findings suggest that distinct biobehavioral endophenotypes can be related to the success of metabolic control. These findings also have strong implications for programs to improve patient adherence, directly addressing risk-taking profiles.

Viscoelasticity of reward and control systems in adolescent risk taking

Heightened risk-taking tendencies during adolescence have been hypothesized to be attributable to physiological differences of maturation in key brain regions. The socioemotional system (e.g., nucleus accumbens), which is instrumental in reward response, shows a relatively earlier development trajectory than the cognitive control system (e.g., medial prefrontal cortex), which regulates impulse response. This developmental imbalance between heightened reward seeking and immature cognitive control potentially makes adolescents more susceptible to engaging in risky activities. Here, we assess brain structure in the socioemotional and cognitive control systems through viscoelastic stiffness measured with magnetic resonance elastography (MRE) and volumetry, as well as risk-taking tendencies measured using two experimental tasks in 40 adolescents (mean age ​= ​13.4 years old). MRE measures of regional brain stiffness reflect brain health and development via myelin content and glial matrix makeup, and have been shown to be highly sensitive to cognitive processes as compared to measures of regional brain volume and diffusion weighted imaging metrics. We find here that the viscoelastic and volumetric differences between the nucleus accumbens and the prefrontal cortex are correlated with increased risk-taking behavior in adolescents. These differences in development between the two brain systems can be used as an indicator of those adolescents who are more prone to real world risky activities and a useful measure for characterizing response to intervention.

Neural mechanisms of decision-making in aging

The present review synthesizes findings on decision neuroscience and aging by focusing on decision processes that have been extensively studied in neuroeconomics and critically assessing the driving mechanisms of age-related change. The paper first highlights age-related changes to key brain structures that have been implicated in decision-making, then, reviews specific decision components and discusses investigations of age-related changes to their neural mechanisms. The review also weighs evidence for organic brain aging versus age-related changes to social and psychological factors in mediating age effects. Reviewed findings are discussed in the context of theories and frameworks that have been used to explain trajectories of change in decision-making across adulthood. This article is categorized under: Psychology > Development and Aging Psychology > Reasoning and Decision-Making Neuroscience > Cognition.

Rehabilitation of Executive function in Paediatric Traumatic brain injury (REPeaT): protocol for a randomized controlled trial for treating working memory and decision-making

BACKGROUND

Working memory allows us to hold information in an active state for short periods of time, and is essential in facilitating goal directed cognitive functioning. Difficulties in working memory and decision-making are common post childhood Traumatic Brain Injury (TBI). Despite this, there is a paucity of research pertaining to implementation and effectiveness of interventions to reduce these common difficulties which impact significantly on one's ability to function independently. One such intervention, Cogmed Working Memory Training Program, has shown success in improving working memory in other childhood clinical populations, but has received little evaluation in the TBI area. This study aims to evaluate whether Cogmed improves working memory and decision-making post childhood TBI and whether these benefits generalize to functional areas.

METHODS

The study is a randomized controlled trial (RCT) of the Cogmed (RM version) intervention for children post-TBI. Children aged 7-15 years are initially screened for working memory impairments. Eligible participants are then randomized into either the treatment group (Cogmed) or the active-control group (Lexia Reading). Each group trains online for 50 min each day, 5 days per week, for 5 consecutive weeks. The online training is supported by online clinician meetings each week. Outcome neuropsychological and functional assessments are carried out immediately at the completion of the intervention and at 6 months follow-up.

DISCUSSION

This study follows gold standard methodology in intervention research; uses a novel measure of decision-making; measures the effects of intervention on functional outcomes immediately and longer-term post intervention; uses online clinician support in order to allow more families easy access to the program; and promotes the use of technology to improve health services. If efficacious in improving working memory, decision-making, and functional outcomes, our team will then take a key role in implementing Cogmed into clinical care.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12617000085370 . Trial Registration Date: 16/01/2017. Protocol Version/Date: HREC 35181G/18.08.2017. Study Status: Ongoing.

Context-Sensitive Computational Mechanisms of Decision Making

Real-world information is primarily sensory in nature, and understandably people attach value to the sensory information to prepare for appropriate behavioral responses. This review presents research from value-based, perceptual, and social decision-making domains, so far studied using isolated paradigms and their corresponding computational models. For example, in perceptual decision making, the sensory evidence accumulation rather than value computation becomes central to choice behavior. Furthermore, we identify cross-linkages between the perceptual and value-based domains to help us better understand generic processes pertaining to individual decision making. The purpose of this review is 2-fold. First, we identify the need for integrated study of different domains of decision making. Second, given that both our perception and valuation are influenced by the surrounding context, we suggest the integration of different types of information in decision making could be done by studying contextual influences in decision making. Future research needs to attempt toward a system-level understanding of various subprocesses involved in decision making.

Neural Mechanisms Underlying Compensatory and Noncompensatory Strategies in Risky Choice

Individuals may differ systematically in their applied decision strategies, which has critical implications for decision neuroscience but is yet scarcely studied. Our study's main focus was therefore to investigate the neural mechanisms underlying compensatory versus noncompensatory strategies in risky choice. Here, we compared people using a compensatory expected value maximization with people using a simplified noncompensatory loss-minimizing choice strategy. To this end, we used a two-choice paradigm including a set of "simple" items (e.g., simple condition), in which one option was superior on all attributes, and a set of "conflict" items, in which one option was superior on one attribute but inferior on other attributes. A binomial mixture analysis of the decisions elicited by these items differentiated between decision-makers using either a compensatory or a noncompensatory strategy. Behavioral differences were particularly pronounced in the conflict condition, and these were paralleled by neural results. That is, we expected compensatory decision-makers to use an integrated value comparison during choice in the conflict condition. Accordingly, the compensatory group tracked the difference in expected value between choice options reflected in neural activation in the parietal cortex. Furthermore, we expected noncompensatory, compared with compensatory, decision-makers to experience increased conflict when attributes provided conflicting information. Accordingly, the noncompensatory group showed greater dorsomedial PFC activation only in the conflict condition. These pronounced behavioral and neural differences indicate the need for decision neuroscience to account for individual differences in risky choice strategies and to broaden its scope to noncompensatory risky choice strategies.

Individual differences in the neural signature of subjective value among older adults

Some healthy older adults show departures from standard decision-making patterns exhibited by younger adults. We asked if such departures are uniform or if heterogeneous aging processes can designate which older adults show differing decision patterns. Thirty-three healthy older adults with varying decision-making patterns on a complex decision task (the Iowa Gambling Task) completed an intertemporal choice task while undergoing functional magnetic resonance imaging. We examined whether value representation in the canonical valuation network differed across older adults based on complex decision-making ability. Older adults with advantageous decision patterns showed increased activity in the valuation network, including the ventromedial prefrontal cortex (VMPFC) and striatum. In contrast, older adults with disadvantageous decision patterns showed reduced or absent activation in the VMPFC and striatum, and these older adults also showed greater blood oxygen level dependent signal temporal variability in the striatum. Our results suggest that a reduced representation of value in the brain, possibly driven by increased neural noise, relates to suboptimal decision-making in a subset of older adults, which could translate to poor decision-making in many aspects of life, including finance, health and long-term care. Understanding the connection between suboptimal decision-making and neural value signals is a step toward mitigating age-related decision-making impairments.

Neural loss aversion differences between depression patients and healthy individuals: A functional MRI investigation

Neuroeconomics employs neuroscience techniques to explain decision-making behaviours. Prospect theory, a prominent model of decision-making, features a value function with parameters for risk and loss aversion. Recent work with normal participants identified activation related to loss aversion in brain regions including the amygdala, ventral striatum, and ventromedial prefrontal cortex. However, the brain network for loss aversion in pathologies such as depression has yet to be identified. The aim of the current study is to employ the value function from prospect theory to examine behavioural and neural manifestations of loss aversion in depressed and healthy individuals to identify the neurobiological markers of loss aversion in economic behaviour. We acquired behavioural data and fMRI scans while healthy controls and patients with depression performed an economic decision-making task. Behavioural loss aversion was higher in patients with depression than in healthy controls. fMRI results revealed that the two groups shared a brain network for value function including right ventral striatum, ventromedial prefrontal cortex, and right amygdala. However, the neural loss aversion results revealed greater activations in the right dorsal striatum and the right anterior insula for controls compared with patients with depression, and higher activations in the midbrain region ventral tegmental area for patients with depression compared with controls. These results suggest that while the brain network for loss aversion is shared between depressed and healthy individuals, some differences exist with respect to differential activation of additional areas. Our findings are relevant to identifying neurobiological markers for altered decision-making in the depressed.

Functional connectivity with ventromedial prefrontal cortex reflects subjective value for social rewards

According to many studies, the ventromedial prefrontal cortex (VMPFC) encodes the subjective value of disparate rewards on a common scale. Yet, a host of other reward factors-likely represented outside of VMPFC-must be integrated to construct such signals for valuation. Using functional magnetic resonance imaging (fMRI), we tested whether the interactions between posterior VMPFC and functionally connected brain regions predict subjective value. During fMRI scanning, participants rated the attractiveness of unfamiliar faces. We found that activation in dorsal anterior cingulate cortex, anterior VMPFC and caudate increased with higher attractiveness ratings. Using data from a post-scan task in which participants spent money to view attractive faces, we quantified each individual's subjective value for attractiveness. We found that connectivity between posterior VMPFC and regions frequently modulated by social information-including the temporal-parietal junction (TPJ) and middle temporal gyrus-was correlated with individual differences in subjective value. Crucially, these additional regions explained unique variation in subjective value beyond that extracted from value regions alone. These findings indicate not only that posterior VMPFC interacts with additional brain regions during valuation, but also that these additional regions carry information employed to construct the subjective value for social reward.

Ventromedial prefrontal cortex encodes emotional value

The ventromedial prefrontal cortex (vmPFC) plays a critical role in processing appetitive stimuli. Recent investigations have shown that reward value signals in the vmPFC can be altered by emotion regulation processes; however, to what extent the processing of positive emotion relies on neural regions implicated in reward processing is unclear. Here, we investigated the effects of emotion regulation on the valuation of emotionally evocative images. Two independent experimental samples of human participants performed a cognitive reappraisal task while undergoing fMRI. The experience of positive emotions activated the vmPFC, whereas the regulation of positive emotions led to relative decreases in vmPFC activation. During the experience of positive emotions, vmPFC activation tracked participants' own subjective ratings of the valence of stimuli. Furthermore, vmPFC activation also tracked normative valence ratings of the stimuli when participants were asked to experience their emotions, but not when asked to regulate them. A separate analysis of the predictive power of vmPFC on behavior indicated that even after accounting for normative stimulus ratings and condition, increased signal in the vmPFC was associated with more positive valence ratings. These results suggest that the vmPFC encodes a domain-general value signal that tracks the value of not only external rewards, but also emotional stimuli.

The caudate signals bad reputation during trust decisions

The ability to initiate and sustain trust is critical to health and well-being. Willingness to trust is in part determined by the reputation of the putative trustee, gained via direct interactions or indirectly through word of mouth. Few studies have examined how the reputation of others is instantiated in the brain during trust decisions. Here we use an event-related functional MRI (fMRI) design to examine what neural signals correspond to experimentally manipulated reputations acquired in direct interactions during trust decisions. We hypothesized that the caudate (dorsal striatum) and putamen (ventral striatum) and amygdala would signal differential reputations during decision-making. Twenty-nine healthy adults underwent fMRI scanning while completing an iterated Trust Game as trusters with three fictive trustee partners who had different tendencies to reciprocate (i.e., likelihood of rewarding the truster), which were learned over multiple exchanges with real-time feedback. We show that the caudate (both left and right) signals reputation during trust decisions, such that caudate is more active to partners with two types of "bad" reputations, either indifferent partners (who reciprocate 50% of the time) or unfair partners (who reciprocate 25% of the time), than to those with "good" reputations (who reciprocate 75% of the time). Further, individual differences in caudate activity related to biases in trusting behavior in the most uncertain situation, i.e. when facing an indifferent partner. We also report on other areas that were activated by reputation at p < 0.05 whole brain corrected. Our findings suggest that the caudate is involved in signaling and integrating reputations gained through experience into trust decisions, demonstrating a neural basis for this key social process.

Sleep deprivation alters valuation signals in the ventromedial prefrontal cortex

Even a single night of total sleep deprivation (SD) can have dramatic effects on economic decision making. Here we tested the novel hypothesis that SD influences economic decisions by altering the valuation process. Using functional magnetic resonance imaging we identified value signals related to the anticipation and the experience of monetary and social rewards (attractive female faces). We then derived decision value signals that were predictive of each participant’s willingness to exchange money for brief views of attractive faces in an independent market task. Strikingly, SD altered decision value signals in ventromedial prefrontal cortex (VMPFC) in proportion to the corresponding change in economic preferences. These changes in preference were independent of the effects of SD on attention and vigilance. Our results provide novel evidence that signals in VMPFC track the current state of the individual, and thus reflect not static but constructed preferences.

Nucleus accumbens mediates relative motivation for rewards in the absence of choice

To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed functional magnetic resonance imaging in a large sample of participants while they anticipated earning rewards from two different modalities: monetary and candy rewards. An index for relative motivation toward different reward types was constructed using reaction times to the target for earning rewards. Activation in the nucleus accumbens (NAcc) and anterior insula (aINS) predicted individual variation in relative motivation between our reward modalities. NAcc activation, however, mediated the effects of aINS, indicating the NAcc is the likely source of this relative weighting. These results demonstrate that neural idiosyncrasies in reward efficacy exist even in the absence of explicit choices, and extend the role of NAcc as a critical brain region for such choice-free motivation.