Striatal topography of probability and magnitude information for decisions under uncertainty

The role of loss aversion in social conformity: psychological and neural representations

The impact of others' choices on decision-making is influenced by individual preferences. However, the specific roles of individual preferences in social decision-making remain unclear. In this study, we examine the contributions of risk and loss preferences as well as social influence in decision-making under uncertainty using a gambling task. Our findings indicate that while both individual preferences and social influence affect decision-making in social contexts, loss aversion plays a dominant role, especially in individuals with high loss aversion. This phenomenon is accompanied by increased functional connectivity between the anterior insular cortex and the temporoparietal junction. These results highlight the critical involvement of loss aversion and the anterior insular cortex-temporoparietal junction neural pathway in social decision-making under uncertainty. Our findings provide a computational account of how individual preferences and social information collectively shape our social decision-making behaviors.

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.

Common and Distinct Neural Correlates of Intertemporal and Risky Decision-Making: Meta-Analytical Evidence for the Dual-System Theory

The relationship between intertemporal and risky decision-making has received considerable attention in decision research. Single-process theories suggest that choices involving delay and risk are simply two manifestations of the same psychological mechanism, which implies similar patterns of neural activation. Conversely, the dual-system theory suggests that delayed and risky choices are two contrasting types of processes, which implies distinct brain networks. How these two types of choices relate to each other remains unclear. The current study addressed this issue by performing a meta-analysis of 28 intertemporal decision-making studies (862 subjects) and 51 risky decision-making studies (1539 subjects). We found no common area activated in the conjunction analysis of the delayed and risky rewards. Based on the contrast analysis, delayed rewards were associated with stronger activation in the left dorsal insula, while risky rewards were associated with activation in the bilateral ventral striatum and the right anterior insula. The results align with the dual-system theory with separate neural networks for delayed and risky rewards.

Adolescent Decision-Making Under Risk: Neural Correlates and Sex Differences

An increased propensity for risk taking is a hallmark of adolescent behavior with significant health and social consequences. Here, we elucidated cortical and subcortical regions associated with risky and risk-averse decisions and outcome evaluation using the Balloon Analog Risk Task in a large sample of adolescents (n = 256, 56% female, age 14 ± 0.6), including the level of risk as a parametric modulator. We also identified sex differences in neural activity. Risky decisions engaged regions that are parts of the salience, dorsal attention, and frontoparietal networks, but only the insula was sensitive to increasing risks in parametric analyses. During risk-averse decisions, the same networks covaried with parametric levels of risk. The dorsal striatum was engaged by both risky and risk-averse decisions, but was not sensitive to escalating risk. Negative-outcome processing showed greater activations than positive-outcome processing. Insula, lateral orbitofrontal cortex, middle, rostral, and superior frontal areas, rostral and caudal anterior cingulate cortex were activated only by negative outcomes, with a subset of regions associated with negative outcomes showing greater activation in females. Taken together, these results suggest that safe decisions are predicted by more accurate neural representation of increasing risk levels, whereas reward-related processes play a relatively minor role.

Dorsomedial striatal contributions to different forms of risk/reward decision making

Optimal decision making involving reward uncertainty is integral to adaptive goal-directed behavior. In some instances, these decisions are guided by internal representations of reward history, whereas in other situations, external cues inform a decision maker about how likely certain actions are to yield reward. Different regions of the frontal lobe form distributed networks with striatal and amygdalar regions that facilitate different types of risk/reward decision making. The dorsal medial striatum (DMS) is one key output region of the prefrontal cortex, yet there have been few preclinical studies investigating the involvement of the DMS in different forms of risk/reward decision making. The present study addressed this issue, wherein separate groups of male rats were trained on one of two tasks where they chose between a small/certain or a large/risky reward. In a probabilistic discounting task, reward probabilities changed systematically over blocks of trials (100-6.25% or 6.25-100%), requiring rats to use internal representations of reward history to guide choice. Cue-guided decision-making was assessed with a "Blackjack" task, where different auditory cues indicated the odds associated with the large/risky option (50 or 12.5%). Inactivation of the DMS with GABA agonists impaired adjustments in choice biases during probabilistic discounting, resulting in either increases or decreases in risky choice as the probabilities associated with the large/risky reward decreased or increased over a session. In comparison, DMS inactivation increased risky choices on poor-odds trials on the Blackjack task, which was associated with a reduced impact that non-rewarded choices had on subsequent choices. DMS inactivation also impaired performance of an auditory conditional discrimination. These findings highlight a previously uncharacterized role for the DMS in facilitating flexible action selection during multiple forms of risk/reward decision making.

Impaired adaptation of learning to contingency volatility in internalizing psychopathology

Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting probabilistic decision-making to second-order uncertainty might reflect a core deficit that cuts across anxiety and depression and holds regardless of whether outcomes are aversive or involve reward gain or loss. We used bifactor modeling of internalizing symptoms to separate symptom variance common to both anxiety and depression from that unique to each. Across two experiments, we modeled performance on a probabilistic decision-making under volatility task using a hierarchical Bayesian framework. Elevated scores on the common internalizing factor, with high loadings across anxiety and depression items, were linked to impoverished adjustment of learning to volatility regardless of whether outcomes involved reward gain, electrical stimulation, or reward loss. In particular, high common factor scores were linked to dampened learning following better-than-expected outcomes in volatile environments. No such relationships were observed for anxiety- or depression-specific symptom factors.

Neural Dynamics of the Combined Discounting of Delay and Probability During the Evaluation of a Delayed Risky Reward

Delay discounting and probability discounting are two important processes, but in daily life there are many more situations that involve delayed risky outcomes. Although neuroscience research has extensively investigated delay and probability discounting in isolation, little research has explored the neural correlates of the combined discounting of delay and probability. Using the event-related brain potentials (ERPs) technique, we designed a novel paradigm to investigate neural processes related to the combined discounting of delay and probability during the evaluation of a delayed risky reward. ERP results suggested distinct temporal dynamics for delay and probability processing during combined discounting. Both the early frontal P200 and the N2 reflected only probability, not delay, while the parietal P300 was sensitive to both probability and delay. Furthermore, the late positive potential (LPP) was sensitive to probability, but insensitive to delay. These results suggest that probability has a prolonged modulatory effect on reward evaluation in the information processing stream. These findings contribute to an understanding of the neural processes underlying the combined discounting of delay and probability. The limitation of this study is to only consider four delay and probability combinations. Future studies can explore the combined discounting of more probability and delay combinations to further test the robustness of the conclusion.

Amygdala-prefrontal connectivity modulates loss aversion bias in anxious individuals

Anxious individuals tend to make pessimistic judgments in decision making under uncertainty. While this phenomenon is commonly attributed to risk aversion, loss aversion is a critical but often overlooked factor. In this study, we simultaneously examined risk aversion and loss aversion during decision making in high and low trait anxious individuals in a variable gain/loss gambling task during functional magnetic resonance imaging. Although high relative to low anxious individuals showed significant increased risk aversive behavior reflected by decreased overall gamble decisions, there was no group difference in subjective aversion to risk. Instead, loss aversion rather than risk aversion dominantly contributed to predict behavioral decisions, which was associated with attenuated functional connectivity between the amygdala-based emotional system and the prefrontal control regions. Our findings suggest a dominant role of loss aversion in maladaptive risk assessment of anxious individuals, underpinned by disorganization of emotion-related and cognitive-control-related brain networks.

Pain and social decision-making: New insights from the social framing effect

This paper focuses on the social function of painful experience as revealed by recent studies on social decision-making. Observing others suffering from physical pain evokes empathic reactions that can lead to prosocial behavior (e.g., helping others at a cost to oneself), which might be regarded as the social value of pain derived from evolution. Feelings of guilt may also be elicited when one takes responsibility for another’s pain. These social emotions play a significant role in various cognitive processes and may affect behavioral preferences. In addition, the influence of others’ pain on decision-making is highly sensitive to social context. Combining neuroimaging techniques with a novel decision paradigm, we found that when asking participants to trade-off personal benefits against providing help to other people, verbally describing the causal relationship between their decision and other people’s pain (i.e., framing) significantly changed participants’ preferences. This social framing effect was associated with neural activation in the temporoparietal junction (TPJ), which is a brain area that is important in social cognition and in social emotions. Further, transcranial direct current stimulation (tDCS) on this region successfully modulated the magnitude of the social framing effect. These findings add to the knowledge about the role of perception of others’ pain in our social life.

Neural Correlates of Self-Construal Priming in the Ultimatum Game

Research from cultural and social psychology has identified a central role of self-construal, or the way one views themselves in relation to others, in social cognition. Accordingly, it is plausible that self-construal plays an instrumental role in important aspects of decision-making relating to fairness considerations. Prior research has shown that priming methodology is a useful tool to experimentally isolate the effect of self-construal on social decision-making processes. In the current study we investigated the neural effects of self-construal priming on fairness considerations, using an Ultimatum Game setup (N = 97). Based on previous findings, we predicted an interaction between the self-construal prime and gender on Ultimatum Game behavior; males primed with interdependence would reject the offer relatively more compared to independence, and vice versa for females. As previous neuro-imaging research has established an instrumental role of the anterior insula (AI) and ventromedial prefrontal cortex (vmPFC) in the rejection of unfair offers, we expected higher rejection rates to be mirrored by increased activity in these regions. However, the analyses did not confirm these predictions. As further inspection of the data revealed a habituation effect, we performed a follow-up analysis on the first block (N = 59). This subsequent analysis revealed that priming interdependence resulted in reduced AI activity compared to priming independence, although no behavioral differences were observed. The difference was theorized to result from motivations as conflict avoidance and harmony maintenance, commonly associated with interdependence. Furthermore, the analysis revealed greater vmPFC activity for females compared to males for rejected offers, although this effect was not robust when controlled for trait self-construal. These follow-up analyses suggest that self-construal priming influences insula activity, as well as implicating an underlying role of trait self-construal in observed gender differences in vmPFC activity relating to fairness considerations.

Electrophysiological indexes of option characteristic processing

Decision making is vital to human behavior and can be divided into multiple stages including option assessment, behavioral output, and feedback evaluation. Studying how people evaluate option characteristics in the option assessment stage would provide important knowledge on human decision making. Using the event-related potential (ERP) method, the present study investigated the neural mechanism of evaluating two types of option characteristics (i.e., reward magnitude and degree of uncertainty) in the temporal dimension. Thirty-five volunteers participated in a monetary gambling task, where they either accepted or rejected gambles. The ERP results showed a double dissociation pattern, with the early P1 component being sensitive to magnitude but insensitive to degree of uncertainty, while both the N2 and P3 components showed the opposite pattern. The results suggest that these two fundamental option features are assessed rapidly and separately in the human brain. Specifically, small magnitude elicited a larger P1 than did large magnitude, indicating that the perceptual and attentional processing of options is modulated by magnitude. Both the N2 and P3 amplitudes evoked by the risky context were larger than those evoked by the ambiguous one, reflecting that more cognitive conflicts and resources are involved in the former condition. Furthermore, the P1, but not the N2 or P3, amplitude was sensitive to decisions, suggesting that early attentional processes may contribute to human decision making. These findings may provide insight into the temporal mechanisms of option characteristic processing.

Neural Dynamics of Processing Probability Weight and Monetary Magnitude in the Evaluation of a Risky Reward

Risky decision-making involves risky reward valuation, choice, and feedback processes. However, the temporal dynamics of risky reward processing are not well understood. Using event-related brain potential, we investigated the neural correlates of probability weight and money magnitude in the evaluation of a risky reward. In this study, each risky choice consisted of two risky options, which were presented serially to separate decision-making and option evaluation processes. The early P200 component reflected the process of probability weight, not money magnitude. The medial frontal negativity (MFN) reflected both probability weight and money magnitude processes. The late positive potential (LPP) only reflected the process of probability weight. These results demonstrate distinct temporal dynamics for probability weight and money magnitude processes when evaluating a risky outcome, providing a better understanding of the possible mechanism underlying risky reward processing.

Valence and magnitude ambiguity in feedback processing

BACKGROUND

Outcome feedback which indicates behavioral consequences are crucial for reinforcement learning and environmental adaptation. Nevertheless, outcome information in daily life is often totally or partially ambiguous. Studying how people interpret this kind of information would provide important knowledge about the human evaluative system.

METHODS

This study concentrates on the neural processing of partially ambiguous feedback, that is, either its valence or magnitude is unknown to participants. To address this topic, we sequentially presented valence and magnitude information; electroencephalography (EEG) response to each kind of presentation was recorded and analyzed. The event-related potential components feedback-related negativity (FRN) and P3 were used as indices of neural activity.

RESULTS

Consistent with previous literature, the FRN elicited by ambiguous valence was not significantly different from that elicited by negative valence. On the other hand, the FRN elicited by ambiguous magnitude was larger than both the large and small magnitude, indicating the motivation to seek unambiguous magnitude information. The P3 elicited by ambiguous valence and ambiguous magnitude was not significantly different from that elicited by negative valence and small magnitude, respectively, indicating the emotional significance of feedback ambiguity. Finally, the aforementioned effects also manifested in the stage of information integration.

CONCLUSION

These findings indicate both similarities and discrepancies between the processing of valence ambiguity and that of magnitude ambiguity, which may help understand the mechanisms of ambiguous information processing.

A benefit-cost framework of motivation for a specific activity

How can an individual be motivated to perform a target exercise or activity? This question arises in training, therapeutic, and education settings alike, yet despite-or even because of-the large range of extant motivation theories, finding a clear answer to this question can be challenging. Here we propose an application-friendly framework of motivation for a specific activity or exercise that incorporates core concepts from several well-regarded psychological and economic theories of motivation. The key assumption of this framework is that motivation for performing a given activity is determined by the expected benefits and the expected costs of (performance of) the activity. Benefits comprise positive feelings, gains, and rewards experienced during performance of the activity (intrinsic benefits) or achieved through the activity (extrinsic benefits). Costs entail effort requirements, time demands, and other expenditure (intrinsic costs) as well as unwanted associated outcomes and missing out on alternative activities (extrinsic costs). The expected benefits and costs of a given exercise are subjective and state dependent. We discuss convergence of the proposed framework with a selection of extant motivation theories and briefly outline neurobiological correlates of its main components and assumptions. One particular strength of our framework is that it allows to specify five pathways to increasing motivation for a target exercise, which we illustrate and discuss with reference to previous empirical data.

Role of contingency in striatal response to incentive in adolescents with anxiety

This study examines the effect of contingency on reward function in anxiety. We define contingency as the aspect of a situation in which the outcome is determined by one's action-that is, when there is a direct link between one's action and the outcome of the action. Past findings in adolescents with anxiety or at risk for anxiety have revealed hypersensitive behavioral and neural responses to higher value rewards with correct performance. This hypersensitivity to highly valued (salient) actions suggests that the value of actions is determined not only by outcome magnitude, but also by the degree to which the outcome is contingent on correct performance. Thus, contingency and incentive value might each modulate reward responses in unique ways in anxiety. Using fMRI with a monetary reward task, striatal response to cue anticipation is compared in 18 clinically anxious and 20 healthy adolescents. This task manipulates orthogonally reward contingency and incentive value. Findings suggest that contingency modulates the neural response to incentive magnitude differently in the two groups. Specifically, during the contingent condition, right-striatal response tracks incentive value in anxious, but not healthy, adolescents. During the noncontingent condition, striatal response is bilaterally stronger to low than to high incentive in anxious adolescents, while healthy adolescents exhibit the expected opposite pattern. Both contingency and reward magnitude differentiate striatal activation in anxious versus healthy adolescents. These findings may reflect exaggerated concern about performance and/or alterations of striatal coding of reward value in anxious adolescents. Abnormalities in reward function in anxiety may have treatment implications.

Dynamic routing of task-relevant signals for decision making in dorsolateral prefrontal cortex

Neurons in the dorsolateral prefrontal cortex (DLPFC) encode a diverse array of sensory and mnemonic signals, but little is known about how this information is dynamically routed during decision making. We analyzed the neuronal activity in the DLPFC of monkeys performing a probabilistic reversal task where information about the probability and magnitude of reward was provided by the target color and numerical cues, respectively. The location of the target of a given color was randomized across trials and therefore was not relevant for subsequent choices. DLPFC neurons encoded signals related to both task-relevant and irrelevant features, but only task-relevant mnemonic signals were encoded congruently with choice signals. Furthermore, only the task-relevant signals related to previous events were more robustly encoded following rewarded outcomes. Thus, multiple types of neural signals are flexibly routed in the DLPFC so as to favor actions that maximize reward.

A computational analysis of the neural bases of Bayesian inference

Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities code and compute Bayesian probabilities. Here, we introduce an urn-ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference. Bayesian model comparison is conducted to compare various models in terms of their ability to explain trial-by-trial variation in ERP responses at different points in time and over different regions of the scalp. Specifically, we are interested in dissociating specific ERP responses in terms of Bayesian updating and predictive surprise. Bayesian updating refers to changes in probability distributions given new observations, while predictive surprise equals the surprise about observations under current probability distributions. Components of the late positive complex (P3a, P3b, Slow Wave) provide dissociable measures of Bayesian updating and predictive surprise. Specifically, the updating of beliefs about hidden states yields the best fit for the anteriorly distributed P3a, whereas the updating of predictions of observations accounts best for the posteriorly distributed Slow Wave. In addition, parietally distributed P3b responses are best fit by predictive surprise. These results indicate that the three components of the late positive complex reflect distinct neural computations. As such they are consistent with the Bayesian brain hypothesis, but these neural computations seem to be subject to nonlinear probability weighting. We integrate these findings with the free-energy principle that instantiates the Bayesian brain hypothesis.

The Value of Numbers in Economic Rewards

Previous work has identified a distributed network of neural systems involved in appraising the value of rewards, such as when winning $100 versus $1. These studies, however, confounded monetary value and the number used to represent it, which leads to the possibility that some elements in the network may be specialized for processing numeric rather than monetary value. To test this hypothesis, we manipulated numeric magnitude and units to construct a range of economic rewards for simple decisions (e.g., 1¢, $1, 100¢, $100). Consistent with previous research in numerical cognition, results showed that blood-oxygen-level-dependent (BOLD) activity in intraparietal sulcus was correlated with changes in numeric magnitude, independent of monetary value, whereas activity in orbitofrontal cortex was correlated with monetary value, independent of numeric magnitude. Finally, region-of-interest analyses revealed that the BOLD response to numeric magnitude, but not monetary value, described a compressive function. Together, these findings highlight the importance of numerical cognition for understanding how the brain processes monetary rewards.

Understanding decision neuroscience: a multidisciplinary perspective and neural substrates

The neuroscience of decision making is a rapidly evolving multidisciplinary research area that employs neuroscientific techniques to explain various parameters associated with decision making behavior. In this chapter, we emphasize the role of multiple disciplines such as psychology, economics, neuroscience, and computational approaches in understanding the phenomenon of decision making. Further, we present a theoretical approach that suggests understanding the building blocks of decision making as bottom-up processes and integrate these with top-down modulatory factors. Relevant neurophysiological and neuroimaging findings that have used the building-block approach are reviewed. A unifying framework emphasizing multidisciplinary views would bring further insights into the active research area of decision making. Pointing to future directions for research, we focus on the role of computational approaches in such a unifying framework.

Disruption of orbitofronto-striatal functional connectivity underlies maladaptive persistent behaviors in alcohol-dependent patients

OBJECTIVE

Alcohol dependence is characterized by persistent alcohol-seeking despite negative consequences. Previous studies suggest that maladaptive persistent behaviors reflect alcohol-induced brain changes that cause alterations in the cortico-striatal-limbic circuit.

METHODS

Twenty one alcohol dependent patients and 24 age-matched healthy controls performed a decision-making task during functional MRI. We defined the medial orbitofrontal cortex (mOFC) as a region-of-interest and performed seed-based functional connectivity analysis.

RESULTS

Healthy controls were more flexible in adapting an alternative behavioral strategy, which correlated with stronger mOFC-dorsal striatum functional connectivity. In contrast, alcohol dependent patients persisted to the first established behavioral strategy. The mOFC-dorsal striatum functional connectivity was impaired in the alcohol-dependent patients, but increased in correlation with the duration of abstinence.

CONCLUSION

Our findings support that the disruption of the mOFC-striatal circuitry contribute to the maldaptive persistent behaviors in alcohol dependent patients.

Categorization = decision making + generalization

We rarely, if ever, repeatedly encounter exactly the same situation. This makes generalization crucial for real world decision making. We argue that categorization, the study of generalizable representations, is a type of decision making, and that categorization learning research would benefit from approaches developed to study the neuroscience of decision making. Similarly, methods developed to examine generalization and learning within the field of categorization may enhance decision making research. We first discuss perceptual information processing and integration, with an emphasis on accumulator models. We then examine learning the value of different decision making choices via experience, emphasizing reinforcement learning modeling approaches. Next we discuss how value is combined with other factors in decision making, emphasizing the effects of uncertainty. Finally, we describe how a final decision is selected via thresholding processes implemented by the basal ganglia and related regions. We also consider how memory related functions in the hippocampus may be integrated with decision making mechanisms and contribute to categorization.

Synchrony of anterior cingulate cortex and insular-striatal activation predicts ambiguity aversion in individuals with low impulsivity

Personal attitude toward ambiguity contributes to individual differences in decision making in uncertain situations. Operationally, these attitudes reflect the various coping strategies elected to overcome the limited information. A key brain region involved in cognitive control for performance adjustments is the dorsal anterior cingulate cortex (dACC). To test how dACC functional network connectivity would be modulated by uncertainty and differ between individuals, 24 healthy participants underwent functional MRI in 3 sequential runs: 1 resting-state and 2 decision-making task runs. Individuals with lower nonplanning impulsiveness made greater use of a Pass option and avoided uncertain ambiguous situations. Seed-based functional connectivity analysis during the task runs revealed that stronger activation synchrony between the left dACC and the right anterior insula correlated with greater use of a Pass response option. During the resting-state, stronger resting-state functional connectivity between the left dACC and the ventral striatum predicted the adoption of Pass as a behavioral strategy and correlated with stronger task-activated synchrony between the dACC and the right anterior insula. Our findings indicate that that the synchrony between the dACC and insula-striatal circuitry was greater in individuals with low compared with high nonplanning impulsiveness and contributed to adopting Pass as a useful behavioral strategy.

Spatial smoothing systematically biases the localization of reward-related brain activity

Neuroimaging methods with enhanced spatial resolution such as functional magnetic resonance imaging (FMRI) suggest that the subcortical striatum plays a critical role in human reward processing. Analysis of FMRI data requires several preprocessing steps, some of which entail tradeoffs. For instance, while spatial smoothing can enhance statistical power, it may also bias localization towards regions that contain more gray than white matter. In a meta-analysis and reanalysis of an existing dataset, we sought to determine whether spatial smoothing could systematically bias the spatial localization of foci related to reward anticipation in the nucleus accumbens (NAcc). An activation likelihood estimate (ALE) meta-analysis revealed that peak ventral striatal ALE foci for studies that used smaller spatial smoothing kernels (i.e. <6mm FWHM) were more anterior than those identified for studies that used larger kernels (i.e. >7mm FWHM). Additionally, subtraction analysis of findings for studies that used smaller versus larger smoothing kernels revealed a significant cluster of differential activity in the left relatively anterior NAcc (Talairach coordinates: -10, 9, -1). A second meta-analysis revealed that larger smoothing kernels were correlated with more posterior localizations of NAcc activation foci (p<0.015), but revealed no significant associations with other potentially relevant parameters (including voxel volume, magnet strength, and publication date). Finally, repeated analysis of a representative dataset processed at different smoothing kernels (i.e., 0-12mm) also indicated that smoothing systematically yielded more posterior activation foci in the NAcc (p<0.005). Taken together, these findings indicate that spatial smoothing can systematically bias the spatial localization of striatal activity. These findings have implications both for historical interpretation of past findings related to reward processing and for the analysis of future studies.

How costs influence decision values for mixed outcomes

The things that we hold dearest often require a sacrifice, as epitomized in the maxim “no pain, no gain.” But how is the subjective value of outcomes established when they consist of mixtures of costs and benefits? We describe theoretical models for the integration of costs and benefits into a single value, drawing on both the economic and the empirical literatures, with the goal of rendering them accessible to the neuroscience community. We propose two key assays that go beyond goodness of fit for deciding between the dominant additive model and four varieties of interactive models. First, how they model decisions between costs when reward is not on offer; and second, whether they predict changes in reward sensitivity when costs are added to outcomes, and in what direction. We provide a selective review of relevant neurobiological work from a computational perspective, focusing on those studies that illuminate the underlying valuation mechanisms. Cognitive neuroscience has great potential to decide which of the theoretical models is actually employed by our brains, but empirical work has yet to fully embrace this challenge. We hope that future research improves our understanding of how our brain decides whether mixed outcomes are worthwhile.