Comparing apples and oranges: using reward-specific and reward-general subjective value representation in the brain

Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses

In everyday life, people need to respond appropriately to many types of emotional stimuli. Here, we investigate whether human occipital-temporal cortex (OTC) shows co-representation of the semantic category and affective content of visual stimuli. We also explore whether OTC transformation of semantic and affective features extracts information of value for guiding behavior. Participants viewed 1620 emotional natural images while functional magnetic resonance imaging data were acquired. Using voxel-wise modeling we show widespread tuning to semantic and affective image features across OTC. The top three principal components underlying OTC voxel-wise responses to image features encoded stimulus animacy, stimulus arousal and interactions of animacy with stimulus valence and arousal. At low to moderate dimensionality, OTC tuning patterns predicted behavioral responses linked to each image better than regressors directly based on image features. This is consistent with OTC representing stimulus semantic category and affective content in a manner suited to guiding behavior.

Quantitative vs. Qualitative Outcomes: A Longitudinal Study of Risk and Ambiguity in Monetary and Medical Decision-Making

How do decision-makers choose between alternatives offering outcomes that are not easily quantifiable? Previous literature on decisions under uncertainty focused on alternatives with quantifiable outcomes, for example monetary lotteries. In such scenarios, decision-makers make decisions based on success chance, outcome magnitude, and individual preferences for uncertainty. It is not clear, however, how individuals construct subjective values when outcomes are not directly quantifiable. To explore how decision-makers choose when facing non-quantifiable outcomes, we focus here on medical decisions with qualitative outcomes. Specifically, we ask whether decision-makers exhibit the same attitudes towards two types of uncertainty - risk and ambiguity - across domains with quantitative and qualitative outcomes. To answer this question, we designed an online decision-making task where participants made binary choices between alternatives offering either guaranteed lower outcomes or potentially higher outcomes that are associated with some risk and ambiguity. The outcomes of choices were either different magnitudes of monetary gains or levels of improvement in a medical condition. We recruited 429 online participants and repeated the survey in two waves, which allowed us to compare the between-domain attitude consistency with within-domain consistency, over time. We found that risk and ambiguity attitudes were moderately correlated across domains. Over time, risk attitudes had slightly higher correlations compared to across domains, while in ambiguity over-time correlations were slightly weaker. These findings are consistent with the conceptualization of risk attitude as more trait-like, and ambiguity attitudes as more state-like. We discuss the implications and applicability of our novel modeling approach to broader contexts with non-quantifiable outcomes.

Neural Reward Representations Enable Utilitarian Welfare Maximization

From deciding which meal to prepare for our guests to trading off the proenvironmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision-makers facing conflicting preferences of others should choose the option that maximizes the sum of the subjective value (utility) of the entire group. This notion requires comparing the intensities of preferences across individuals. However, it remains unclear whether such comparisons are possible at all and (if they are possible) how they might be implemented in the brain. Here, we show that female and male participants can both learn the preferences of others by observing their choices and represent these preferences on a common scale to make utilitarian welfare decisions. On the neural level, multivariate support vector regressions revealed that a distributed activity pattern in the ventromedial prefrontal cortex (VMPFC), a brain region previously associated with reward processing, represented the preference strength of others. Strikingly, also the utilitarian welfare of others was represented in the VMPFC and relied on the same neural code as the estimated preferences of others. Together, our findings reveal that humans can behave as if they maximized utilitarian welfare using a specific utility representation and that the brain enables such choices by repurposing neural machinery processing the reward others receive.

OFC neurons do not represent the negative value of a conditioned inhibitor

The orbitofrontal cortex (OFC) is often proposed to function as a value integrator; however, alternative accounts focus on its role in representing associative structures that specify the probability and sensory identity of future outcomes. These two accounts make different predictions about how this area should respond to conditioned inhibitors of reward, since in the former, neural activity should reflect the negative value of the inhibitor, whereas in the latter, it should track the estimated probability of a future reward based on all cues present. Here, we assessed these predictions by recording from small groups of neurons in the lateral OFC of rats during training in a conditioned inhibition design. Rats showed negative summation when the inhibitor was compounded with a novel excitor, suggesting that they learned to respond to the conditioned inhibitor appropriately. Against this backdrop, we found unit and population responses that scaled with expected reward value on excitor + inhibitor compound trials. However, the responses of these neurons did not differentiate between the conditioned inhibitor and a neutral cue when both were presented in isolation. Further, when the ensemble patterns were analyzed, activity to the conditioned inhibitor did not classify according to putative negative value. Instead, it classified with a same-modality neutral cue when presented alone and as a unique item when presented in compound with a novel excitor. This pattern of results supports the notion that OFC encodes a model of the causal structure of the environment rather than either the modality or the value of cues.

Reduced neural encoding of utility prediction errors in cocaine addiction

Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants' exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.

Lateral orbitofrontal cortex integrates predictive information across multiple cues to guide behavior

Individuals are often faced with multiple cues that concurrently predict the same outcome, and combining these predictions may benefit behavior. Previous work has studied the neural basis of decision-making, predominantly using isolated sensory stimuli, and so the mechanisms that allow us to leverage multiple cues remain unclear. In two experiments, we used neuroimaging and network-targeted brain stimulation to probe how the brain integrates outcome predictions to guide adaptive behavior. We identified neural signatures of outcome integration in the lateral orbitofrontal cortex (OFC), where concurrently presented cues evoke stronger pattern-based representations of expected outcomes. Moreover, perturbing lateral OFC network activity impairs subjects' ability to leverage predictions from multiple cues to facilitate responding. Intriguingly, we found similar behavioral and brain mechanisms for reward-predicting cues and for cues predicting the absence of reward. These findings highlight a causal role for the lateral OFC in utilizing outcome predictions from multiple cues to guide behavior.

Sensitivity to intrinsic rewards is domain general and related to mental health

Humans frequently engage in intrinsically rewarding activities (for example, consuming art, reading). Despite such activities seeming diverse, we show that sensitivity to intrinsic rewards is domain general and associated with mental health. In this cross-sectional study, participants online (N = 483) were presented with putative visual, cognitive and social intrinsic rewards as well as monetary rewards and neutral stimuli. All rewards elicited positive feelings (were 'liked'), generated consummatory behaviour (were 'wanted') and increased the likelihood of the action leading to them (were 'reinforcing'). Factor analysis revealed that ~40% of response variance across stimuli was explained by a general sensitivity to all rewards, but not to neutral stimuli. Affective aspects of mental health were associated with sensitivity to intrinsic, but not monetary, rewards. These results may help explain thriving and suffering: individuals with high reward sensitivity will engage in a variety of intrinsically rewarding activities, eventually finding those they excel at, whereas low sensitivity individuals will not.

Incomparability and Incommensurability in Choice: No Common Currency of Value?

Models of decision-making typically assume the existence of some common currency of value, such as utility, happiness, or inclusive fitness. This common currency is taken to allow comparison of options and to underpin everyday choice. Here we suggest instead that there is no universal value scale, that incommensurable values pervade everyday choice, and hence that most existing models of decision-making in both economics and psychology are fundamentally limited. We propose that choice objects can be compared only with reference to specific but nonuniversal "covering values." These covering values may reflect decision-makers' goals, motivations, or current states. A complete model of choice must accommodate the range of possible covering values. We show that abandoning the common-currency assumption in models of judgment and decision-making necessitates rank-based and "simple heuristics" models that contrast radically with conventional utility-based approaches. We note that if there is no universal value scale, then Arrow's impossibility theorem places severe bounds on the rationality of individual decision-making and hence that there is a deep link between the incommensurability of value, inconsistencies in human decision-making, and rank-based coding of value. More generally, incommensurability raises the question of whether it will ever be possible to develop single-quantity-maximizing models of decision-making.

Intrinsic rewards explain context-sensitive valuation in reinforcement learning

When observing the outcome of a choice, people are sensitive to the choice's context, such that the experienced value of an option depends on the alternatives: getting $1 when the possibilities were 0 or 1 feels much better than when the possibilities were 1 or 10. Context-sensitive valuation has been documented within reinforcement learning (RL) tasks, in which values are learned from experience through trial and error. Range adaptation, wherein options are rescaled according to the range of values yielded by available options, has been proposed to account for this phenomenon. However, we propose that other mechanisms-reflecting a different theoretical viewpoint-may also explain this phenomenon. Specifically, we theorize that internally defined goals play a crucial role in shaping the subjective value attributed to any given option. Motivated by this theory, we develop a new "intrinsically enhanced" RL model, which combines extrinsically provided rewards with internally generated signals of goal achievement as a teaching signal. Across 7 different studies (including previously published data sets as well as a novel, preregistered experiment with replication and control studies), we show that the intrinsically enhanced model can explain context-sensitive valuation as well as, or better than, range adaptation. Our findings indicate a more prominent role of intrinsic, goal-dependent rewards than previously recognized within formal models of human RL. By integrating internally generated signals of reward, standard RL theories should better account for human behavior, including context-sensitive valuation and beyond.

Frontopolar cortex represents complex features and decision value during choice between environments

Important decisions often involve choosing between complex environments that define future item encounters. Despite its importance for adaptive behavior and distinct computational challenges, decision-making research primarily focuses on item choice, ignoring environment choice altogether. Here we contrast previously studied item choice in ventromedial prefrontal cortex with lateral frontopolar cortex (FPl) linked to environment choice. Furthermore, we propose a mechanism for how FPl decomposes and represents complex environments during decision making. Specifically, we trained a choice-optimized, brain-naive convolutional neural network (CNN) and compared predicted CNN activation with actual FPl activity. We showed that the high-dimensional FPl activity decomposes environment features to represent the complexity of an environment to make such choice possible. Moreover, FPl functionally connects with posterior cingulate cortex for guiding environment choice. Further probing FPl's computation revealed a parallel processing mechanism in extracting multiple environment features.

DeePay: deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing

There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers' subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects' willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product's image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.

Prelimbic neuron assemblies with delayed activation encode the economic decision-making process in a bandit game

The medial prefrontal cortex (mPFC) is critical to an animal's value-based decision-making process. However, due to heterogeneity of local mPFC neurons, which neuron group and how it contributes to the alteration of the animal's decision is yet to be explored. And the effect of empty reward in this process is often neglected. Here, we adopted a two-port bandit game paradigm for mice and applied synchronized calcium imaging to the prelimbic area of the mPFC. The results showed that neurons recruited in the bandit game exhibit three distinct firing patterns. Specially, neurons with delayed activation (deA neurons¹) carried exclusive information on reward type and changes of choice value. We demonstrated that these deA neurons were essential for the construction of choice-outcome correlation and the trial-to-trial modification of decision. Additionally, we found that in a long-term gambling game, members of the deA neuron assembly were dynamically shifting while maintaining the function, and the importance of empty reward feedbacks were gradually elevated to the same level as reward. Together, these results revealed a vital role for prelimbic deA neurons in the gambling tasks and a new perspective on the encoding of economic decision-making.

Dynamic prospect theory: Two core decision theories coexist in the gambling behavior of monkeys and humans

Research in the multidisciplinary field of neuroeconomics has mainly been driven by two influential theories regarding human economic choice: prospect theory, which describes decision-making under risk, and reinforcement learning theory, which describes learning for decision-making. We hypothesized that these two distinct theories guide decision-making in a comprehensive manner. Here, we propose and test a decision-making theory under uncertainty that combines these highly influential theories. Collecting many gambling decisions from laboratory monkeys allowed for reliable testing of our model and revealed a systematic violation of prospect theory's assumption that probability weighting is static. Using the same experimental paradigm in humans, substantial similarities between these species were uncovered by various econometric analyses of our dynamic prospect theory model, which incorporates decision-by-decision learning dynamics of prediction errors into static prospect theory. Our model provides a unified theoretical framework for exploring a neurobiological model of economic choice in human and nonhuman primates.

Primary rewards and aversive outcomes have comparable effects on attentional bias

Attention is biased toward stimuli previously associated with reward. The same is true for aversive conditioning; stimuli previously associated with an aversive outcome also bias attention, suggesting that motivational salience guides attention. Most research that supports this conclusion has manipulated monetary gain-a secondary reinforcer-for reward learning, and electric shocks-a primary punisher-for aversive conditioning, making it difficult to directly compare their influence on attention. Therefore, in the present study, we matched for reinforcer dimensions by using primary taste as reinforcers/punishers and assessed their influence on attention. In a training phase, participants learned to associate three colors with sweet juice (reward), salt water (aversive), and no outcome (neutral), respectively. The two primary reinforcers were equated for valence based on choices made in a prior decision-making task. In a later test phase, these three colors were used for targets and distractors in a task in which participants oriented to a shape-defined target. An attentional bias in favor of the aversively conditioned and reward-associated colors was evident when comparing to the neutral color. Importantly, a direct comparison of rewarded and aversive stimuli revealed no significant differences. These results suggest that when matched for reinforcer dimensions and valence, reward and aversive outcomes bias attention in a similar manner and their effects are comparable, providing further evidence in support of the motivational salience account of learning-dependent attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A tripartite view of the posterior cingulate cortex

The posterior cingulate cortex (PCC) is one of the least understood regions of the cerebral cortex. By contrast, the anterior cingulate cortex has been the subject of intensive investigation in humans and model animal systems, leading to detailed behavioural and computational theoretical accounts of its function. The time is right for similar progress to be made in the PCC given its unique anatomical and physiological properties and demonstrably important contributions to higher cognitive functions and brain diseases. Here, we describe recent progress in understanding the PCC, with a focus on convergent findings across species and techniques that lay a foundation for establishing a formal theoretical account of its functions. Based on this converging evidence, we propose that the broader PCC region contains three major subregions - the dorsal PCC, ventral PCC and retrosplenial cortex - that respectively support the integration of executive, mnemonic and spatial processing systems. This tripartite subregional view reconciles inconsistencies in prior unitary theories of PCC function and offers promising new avenues for progress.

The neural correlates of value representation: From single items to bundles

One of the core questions in Neuro-economics is to determine where value is represented. To date, most studies have focused on simple options and identified the ventromedial prefrontal cortex (VMPFC) as the common value region. We report the findings of an fMRI study in which we asked participants to make pairwise comparisons involving options of varying complexity: single items (Control condition), bundles made of the same two single items (Scaling condition) and bundles made of two different single items (Bundling condition). We construct a measure of choice consistency to capture how coherent the choices of a participant are with one another. We also record brain activity while participants make these choices. We find that a common core of regions involving the left VMPFC, the left dorsolateral prefrontal cortex (DLPFC), regions associated with complex visual processing and the left cerebellum track value across all conditions. Also, regions in the DLPFC, the ventrolateral prefrontal cortex (VLPFC) and the cerebellum are differentially recruited across conditions. Last, variations in activity in VMPFC and DLPFC value-tracking regions are associated with variations in choice consistency. This suggests that value based decision-making recruits a core set of regions as well as specific regions based on task demands. Further, correlations between consistency and the magnitude of signal change with lateral portions of the PFC suggest the possibility that activity in these regions may play a causal role in decision quality.

An fMRI-Based Brain Marker of Individual Differences in Delay Discounting

Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.

Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity

The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed "Electrical FingerPrint of rIFG"; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.

The relationship between sleep and appetitive conditioning: A systematic review and meta-analysis

This systematic review and meta-analysis (PROSPERO registration animal/human studies: CRD42021234793/CRD42021234790) examined the relationship between sleep and appetitive conditioning. Inclusion criteria included: a) appetitive conditioning paradigm; b) measure of conditioning; c) sleep measurement and/or sleep loss; d) human and/etor non-human animal samples; and e) written in English. Searches of seven databases returned 3777 publications. The final sample consisted of 42 studies using primarily animal samples and involving food- and drug-related conditioning tasks. We found sleep loss disrupted appetitive conditioning of food rewards (p < 0.001) but potentiated appetitive conditioning of drug rewards (p < 0.001). Furthermore, sleep loss negatively impacted extinction learning irrespective of the reward type. Post-learning sleep was associated with increases in REM sleep (p = 0.02). Findings suggest sleep loss potentiates the impact of psychoactive substances in a manner likely to produce an increased risk of problematic substance use. In obese/overweight populations, sleep loss may be associated with deficits in the conditioning and extinction of reward-related behaviours. Further research should assess the relationship between sleep and appetitive conditioning in humans.

Hypothalamic interaction with reward-related regions during subjective evaluation of foods

The reward system implemented in the midbrain, ventral striatum, orbitofrontal cortex, and ventromedial prefrontal cortex evaluates and compares various types of rewards given to the organisms. It has been suggested that autonomic factors influence reward-related processing via the hypothalamus, but how the hypothalamus modulates the reward system remains elusive. In this functional magnetic resonance imaging study, the hypothalamus was parcellated into individual hypothalamic nuclei performing different autonomic functions using boundary mapping parcellation analyses. The effective interaction during subjective evaluation of foods in a reward task was then investigated between the human hypothalamic nuclei and the reward-related regions. We found significant brain activity decrease in the paraventricular nucleus (PVH) and lateral nucleus in the hypothalamus in food evaluation compared with monetary evaluation. A psychophysiological interaction analysis revealed dual interactions between the PVH and (1) midbrain region and (2) ventromedial prefrontal cortex, with the former correlated with the stronger tendency of participants toward food-seeking. A dynamic causal modeling analysis further revealed unidirectional interactions from the PVH to the midbrain and ventromedial prefrontal cortex. These results suggest that the PVH in the human hypothalamus interacts with the reward-related regions in the cerebral cortex via multiple pathways (i.e., the midbrain pathway and ventromedial prefrontal pathway) to evaluate rewards for subsequent decision-making.

A Common Neural Account for Social and Nonsocial Decisions

To date, social and nonsocial decisions have been studied largely in isolation. Consequently, the extent to which social and nonsocial forms of decision uncertainty are integrated using shared neurocomputational resources remains elusive. Here, we address this question using simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) in healthy human participants (young adults of both sexes) and a task in which decision evidence in social and nonsocial contexts varies along comparable scales. First, we identify time-resolved build-up of activity in the EEG, akin to a process of evidence accumulation (EA), across both contexts. We then use the endogenous trial-by-trial variability in the slopes of these accumulating signals to construct parametric fMRI predictors. We show that a region of the posterior-medial frontal cortex (pMFC) uniquely explains trial-wise variability in the process of evidence accumulation in both social and nonsocial contexts. We further demonstrate a task-dependent coupling between the pMFC and regions of the human valuation system in dorso-medial and ventro-medial prefrontal cortex across both contexts. Finally, we report domain-specific representations in regions known to encode the early decision evidence for each context. These results are suggestive of a domain-general decision-making architecture, whereupon domain-specific information is likely converted into a "common currency" in medial prefrontal cortex and accumulated for the decision in the pMFC.SIGNIFICANCE STATEMENT Little work has directly compared social-versus-nonsocial decisions to investigate whether they share common neurocomputational origins. Here, using combined electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) and computational modeling, we offer a detailed spatiotemporal account of the neural underpinnings of social and nonsocial decisions. Specifically, we identify a comparable mechanism of temporal evidence integration driving both decisions and localize this integration process in posterior-medial frontal cortex (pMFC). We further demonstrate task-dependent coupling between the pMFC and regions of the human valuation system across both contexts. Finally, we report domain-specific representations in regions encoding the early, domain-specific, decision evidence. These results suggest a domain-general decision-making architecture, whereupon domain-specific information is converted into a common representation in the valuation system and integrated for the decision in the pMFC.

The bright and dark sides of egoism

Despite its negative reputation, egoism - the excessive concern for one's own welfare - can incite prosocial behavior. So far, however, egoism-based prosociality has received little attention. Here, we first provide an overview of the conditions under which egoism turns into a prosocial motive, review the benefits and limitations of egoism-based prosociality, and compare them with empathy-driven prosocial behavior. Second, we summarize studies investigating the neural processing of egoism-based prosocial decisions, studies investigating the neural processing of empathy-based prosocial decisions, and the small number of studies that compared the neural processing of prosocial decisions elicited by the different motives. We conclude that there is evidence for differential neural networks involved in egoism and empathy-based prosocial decisions. However, this evidence is not yet conclusive, because it is mainly based on the comparison of different experimental paradigms which may exaggerate or overshadow the effect of the different motivational states. Finally, we propose paradigms and research questions that should be tackled in future research that could help to specify how egoism can be used to enhance other prosocial behavior and motivation, and the how it could be tamed.

Can we have a second helping? A preregistered direct replication study on the neurobiological mechanisms underlying self-control

Self‐control is of vital importance for human wellbeing. Hare et al. (2009) were among the first to provide empirical evidence on the neural correlates of self‐control. This seminal study profoundly impacted theory and empirical work across multiple fields. To solidify the empirical evidence supporting self‐control theory, we conducted a preregistered replication of this work. Further, we tested the robustness of the findings across analytic strategies. Participants underwent functional magnetic resonance imaging while rating 50 food items on healthiness and tastiness and making choices about food consumption. We closely replicated the original analysis pipeline and supplemented it with additional exploratory analyses to follow‐up on unexpected findings and to test the sensitivity of results to key analytical choices. Our replication data provide support for the notion that decisions are associated with a value signal in ventromedial prefrontal cortex (vmPFC), which integrates relevant choice attributes to inform a final decision. We found that vmPFC activity was correlated with goal values regardless of the amount of self‐control and it correlated with both taste and health in self‐controllers but only taste in non‐self‐controllers. We did not find strong support for the hypothesized role of left dorsolateral prefrontal cortex (dlPFC) in self‐control. The absence of statistically significant group differences in dlPFC activity during successful self‐control in our sample contrasts with the notion that dlPFC involvement is required in order to effectively integrate longer‐term goals into subjective value judgments. Exploratory analyses highlight the sensitivity of results (in terms of effect size) to the analytical strategy, for instance, concerning the approach to region‐of‐interest analysis.

Amygdala-cortical collaboration in reward learning and decision making

Adaptive reward-related decision making requires accurate prospective consideration of the specific outcome of each option and its current desirability. These mental simulations are informed by stored memories of the associative relationships that exist within an environment. In this review, I discuss recent investigations of the function of circuitry between the basolateral amygdala (BLA) and lateral (lOFC) and medial (mOFC) orbitofrontal cortex in the learning and use of associative reward memories. I draw conclusions from data collected using sophisticated behavioral approaches to diagnose the content of appetitive memory in combination with modern circuit dissection tools. I propose that, via their direct bidirectional connections, the BLA and OFC collaborate to help us encode detailed, outcome-specific, state-dependent reward memories and to use those memories to enable the predictions and inferences that support adaptive decision making. Whereas lOFC→BLA projections mediate the encoding of outcome-specific reward memories, mOFC→BLA projections regulate the ability to use these memories to inform reward pursuit decisions. BLA projections to lOFC and mOFC both contribute to using reward memories to guide decision making. The BLA→lOFC pathway mediates the ability to represent the identity of a specific predicted reward and the BLA→mOFC pathway facilitates understanding of the value of predicted events. Thus, I outline a neuronal circuit architecture for reward learning and decision making and provide new testable hypotheses as well as implications for both adaptive and maladaptive decision making.

Motor response vigour and visual fixation patterns reflect subjective valuation during intertemporal choice

Value-based decision-making is of central interest in cognitive neuroscience and psychology, as well as in the context of neuropsychiatric disorders characterised by decision-making impairments. Studies examining (neuro-)computational mechanisms underlying choice behaviour typically focus on participants’ decisions. However, there is increasing evidence that option valuation might also be reflected in motor response vigour and eye movements, implicit measures of subjective utility. To examine motor response vigour and visual fixation correlates of option valuation in intertemporal choice, we set up a task where the participants selected an option by pressing a grip force transducer, simultaneously tracking fixation shifts between options. As outlined in our preregistration (https://osf.io/k6jct), we used hierarchical Bayesian parameter estimation to model the choices assuming hyperbolic discounting, compared variants of the softmax and drift diffusion model, and assessed the relationship between response vigour and the estimated model parameters. The behavioural data were best explained by a drift diffusion model specifying a non-linear scaling of the drift rate by the subjective value differences. Replicating previous findings, we found a magnitude effect for temporal discounting, such that higher rewards were discounted less. This magnitude effect was further reflected in motor response vigour, such that stronger forces were exerted in the high vs. the low magnitude condition. Bayesian hierarchical linear regression further revealed higher grip forces, faster response times and a lower number of fixation shifts for trials with higher subjective value differences. An exploratory analysis revealed that subjective value sums across options showed an even more pronounced association with trial-wise grip force amplitudes. Our data suggest that subjective utility or implicit valuation is reflected in motor response vigour and visual fixation patterns during intertemporal choice. Taking into account response vigour might thus provide deeper insight into decision-making, reward valuation and maladaptive changes in these processes, e.g. in the context of neuropsychiatric disorders.

Neural Encoding of Food and Monetary Reward Delivery

Different types of rewards such as food and money can similarly drive our behavior owing to shared brain processes encoding their subjective value. However, while the value of money is abstract and needs to be learned, the value of food is rooted in the innate processing of sensory properties and nutritional utilization. Yet, the actual consumption of food and the receipt of money have never been directly contrasted in the same experiment, questioning what unique neural processes differentiate those reward types. To fill this gap, we examined the distinct and common neural responses to the delivery of food and monetary rewards during fMRI. In a novel experimental approach, we parametrically manipulated the subjective value of food and monetary rewards by modulating the quantities of administered palatable milkshake and monetary gains. The receipt of increasing amounts of milkshake and money recruited the ventral striatum and the ventromedial prefrontal cortex, previously associated with value encoding. Notably, the consumption and the subsequent evaluation of increasing quantities of milkshake relative to money revealed an extended recruitment of brain regions related to taste, somatosensory processing, and salience. Moreover, we detected a decline of reward encoding in the ventral tegmental area, nucleus accumbens, and vmPFC, indicating that these regions may be susceptible to time-dependent effects upon accumulation of food and money rewards. Relative to monetary gains, the consumption and evaluation of palatable milkshakes engaged complex neural processing over and above value tracking, emphasizing the critical contribution of taste and other sensory properties to the processing of food rewards. Furthermore, our results highlight the need to closely monitor metabolic states and neural responses to the accumulation of rewards to pinpoint the mechanisms underlying time-dependent dynamics of reward-related processing.

A Neuroeconomics Approach to Obesity

Obesity is a heterogeneous condition that is affected by physiological, behavioral, and environmental factors. Value-based decision making is a useful framework for integrating these factors at the individual level. The disciplines of behavioral economics and reinforcement learning provide tools for identifying specific cognitive and motivational processes that may contribute to the development and maintenance of obesity. Neuroeconomics complements these disciplines by studying the neural mechanisms underlying these processes. We surveyed recent literature on individual decision characteristics that are most frequently implicated in obesity: discounting the value of future outcomes, attitudes toward uncertainty, and learning from rewards and punishments. Our survey highlighted both consistent and inconsistent behavioral findings. These findings underscore the need to examine multiple processes within individuals to identify unique behavioral profiles associated with obesity. Such individual characterization will inform future studies on the neurobiology of obesity as well as the design of effective interventions that are individually tailored.

The reward positivity is sensitive to affective liking

The EEG feature known as the Reward Positivity (RewP) is elicited by reward receipt and appears to reflect sensitively and specifically positive prediction errors during reinforcement learning. Yet, the RewP also is modulated by state and trait affect, suggesting that it has a more complex computational role than simple reinforcement surprise. We conducted a series of experiments aimed to investigate underlying affect processing reflected in the RewP during a reinforcement learning task. In the first experiment (N = 25), we manipulated the type of rewards a person could win (simple points or hedonically-appraised pictures). Although there were no differences in the amplitudes of the RewP for different types of rewards, there was a significant correlation between the individual rating of liking for the images and RewP amplitude. In a second experiment (N = 25), we manipulated reinforcement rates (easy vs. hard) and affective picture content (liked vs. ambivalent) to examine the potential interaction of prediction error and liking on RewP amplitude. We again found a significant relationship between liking and RewP amplitude, however, only in the hard condition. These findings suggest that the RewP reflects cortical computations of reward surprise as well as hedonic liking, identifying it as a possible nexus where multidimensional value is computed.

Motivational signals disrupt metacognitive signals in the human ventromedial prefrontal cortex

A growing body of evidence suggests that, during decision-making, BOLD signal in the ventromedial prefrontal cortex (VMPFC) correlates both with motivational variables – such as incentives and expected values – and metacognitive variables – such as confidence judgments – which reflect the subjective probability of being correct. At the behavioral level, we recently demonstrated that the value of monetary stakes bias confidence judgments, with gain (respectively loss) prospects increasing (respectively decreasing) confidence judgments, even for similar levels of difficulty and performance. If and how this value-confidence interaction is reflected in the VMPFC remains unknown. Here, we used an incentivized perceptual decision-making fMRI task that dissociates key decision-making variables, thereby allowing to test several hypotheses about the role of the VMPFC in the value-confidence interaction. While our initial analyses seemingly indicate that the VMPFC combines incentives and confidence to form an expected value signal, we falsified this conclusion with a meticulous dissection of qualitative activation patterns. Rather, our results show that strong VMPFC confidence signals observed in trials with gain prospects are disrupted in trials with no – or negative (loss) – monetary prospects. Deciphering how decision variables are represented and interact at finer scales seems necessary to better understand biased (meta)cognition.

The human ventromedial prefrontal cortex helps to determine value and confidence in certain decisions, but only in situations when there is a potential for a (monetary) reward.

The Role of the Ventromedial Prefrontal Cortex in Preferential Decisions for Own- and Other-Age Faces

Own-age bias is a well-known bias reflecting the effects of age, and its role has been demonstrated, particularly, in face recognition. However, it remains unclear whether an own-age bias exists in facial impression formation. In the present study, we used three datasets from two published and one unpublished functional magnetic resonance imaging (fMRI) study that employed the same pleasantness rating task with fMRI scanning and preferential choice task after the fMRI to investigate whether healthy young and older participants showed own-age effects in face preference. Specifically, we employed a drift-diffusion model to elaborate the existence of own-age bias in the processes of preferential choice. The behavioral results showed higher rating scores and higher drift rate for young faces than for older faces, regardless of the ages of participants. We identified a young-age effect, but not an own-age effect. Neuroimaging results from aggregation analysis of the three datasets suggest a possibility that the ventromedial prefrontal cortex (vmPFC) was associated with evidence accumulation of own-age faces; however, no clear evidence was provided. Importantly, we found no age-related decline in the responsiveness of the vmPFC to subjective pleasantness of faces, and both young and older participants showed a contribution of the vmPFC to the parametric representation of the subjective value of face and functional coupling between the vmPFC and ventral visual area, which reflects face preference. These results suggest that the preferential choice of face is less susceptible to the own-age bias across the lifespan of individuals.

Sequential consumer choice as multi-cued retrieval

Whether adding songs to a playlist or groceries during an online shop, how do we decide what to choose next? We develop a model that predicts such open-ended, sequential choices using a process of cued retrieval from long-term memory. Using the past choice to cue subsequent retrievals, this model predicts the sequential purchases and response times of nearly 5 million grocery purchases made by more than 100,000 online shoppers. Products can be associated in different ways, such as by their episodic association or semantic overlap, and we find that consumers query multiple forms of associative knowledge when retrieving options. Attending to certain knowledge sources, as estimated by our model, predicts important retrieval errors, such as the propensity to forget or add unwanted products. Our results demonstrate how basic memory retrieval mechanisms shape choices in real-world, goal-directed tasks.

Prefrontal-Striatal Mechanisms of Behavioral Impulsivity During Consumption of Delayed Real Liquid Rewards

Intertemporal choice involves the evaluation of future rewards and reflects behavioral impulsivity. After choosing a delayed reward in an intertemporal choice, a behavioral agent waits for, receives, and then consumes the reward. The current study focused on the consumption of the delayed reward and examined the neural mechanisms of behavioral impulsivity. In humans consuming delayed real liquid rewards in an intertemporal choice, the ventral striatum (VS) showed differential activity between anterior (aVS) and posterior (pVS) regions depending on the degree of behavioral impulsivity. Additionally, impulsive individuals showed activity in the anterior prefrontal cortex (aPFC). An analysis of task-related effective connectivity based on psychophysiological interaction (PPI) revealed that PPI was robust from the aPFC to pVS, but not in the opposite direction. On the other hand, strong bidirectional PPIs were observed between the aVS and pVS, but PPIs from the pVS to aVS were enhanced in impulsive individuals. These results suggest that behavioral impulsivity is reflected in aPFC-VS mechanisms during the consumption of delayed real liquid rewards.

A Neurocomputational Model for Intrinsic Reward

Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modeling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a “computational hedonometer” that reflects both behavior and neural activity that quantifies the affective value of experience.

SIGNIFICANCE STATEMENT Traditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex (vmPFC) activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals.

Testing models at the neural level reveals how the brain computes subjective value

In recent years, models have played an increasingly important role for understanding the brain in cognitive, behavioral, and systems neuroscience. Decision neuroscience in particular has benefitted greatly from the application of economic models of choice preferences to neural data. However, an often-overlooked aspect is that many models of preferences have a generic problem—they make extremely similar behavioral predictions. Here, we demonstrate that to understand the mechanisms of valuation in the brain, it is useful to compare models of choice preferences not only at the behavioral but also at the neural level.

Decisions are based on the subjective values of choice options. However, subjective value is a theoretical construct and not directly observable. Strikingly, distinct theoretical models competing to explain how subjective values are assigned to choice options often make very similar behavioral predictions, which poses a major difficulty for establishing a mechanistic, biologically plausible explanation of decision-making based on behavior alone. Here, we demonstrate that model comparison at the neural level provides insights into model implementation during subjective value computation even though the distinct models parametrically identify common brain regions as computing subjective value. We show that frontal cortical regions implement a model based on the statistical distributions of available rewards, whereas intraparietal cortex and striatum compute subjective value signals according to a model based on distortions in the representations of probabilities. Thus, better mechanistic understanding of how cognitive processes are implemented arises from model comparisons at the neural level, over and above the traditional approach of comparing models at the behavioral level alone.

Neural underpinnings of food choice and consumption in obesity

BACKGROUND/OBJECTIVES

Obesity is associated with unhealthy food choices. Food selection is driven by the subjective valuation of available options, and the perceived and actual rewards accompanying consumption. These cognitive operations are mediated by brain regions including the ventromedial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), and ventral striatum (vStr). This study investigated the relationship between body mass index (BMI) and functional activations in the vmPFC, dACC, and vStr during food selection and consumption.

SUBJECTS/METHODS

After overnight fasting, 26 individuals (BMI: 18-40 kg/m²) performed a food choice task while being scanned with functional magnetic resonance imaging (fMRI). Each trial involved selecting one beverage from a pair of presented options, followed by delivery of a 3 mL aliquot of the selected option using an MR-compatible gustometer. We also tracked subjective preference for each beverage throughout the experiment.

RESULTS

During food choice, individuals with greater BMI had less activation in the dorsolateral prefrontal cortex when selecting a high-value option and less vmPFC activation upon its consumption. Independent of BMI, during food choice the dACC and anterior insula elicited higher activation when a less preferred beverage was selected. Activation of the dACC and a broader frontoparietal network was also observed when deciding between options more similar in value. During consumption, receipt of a more preferred beverage was associated with greater vmPFC response, and attenuation of the dACC.

CONCLUSIONS

An individual's preference for a food option modulates the brain activity associated with choosing and consuming it. The relationship between food preference and underlying brain activity is altered in obesity, with reduced engagement of cognition-related regions when presented with a highly valued option, but a blunted response in reward-related regions upon consumption.

Is the core function of orbitofrontal cortex to signal values or make predictions?

One dominant hypothesis about the function of the orbitofrontal cortex (OFC) is that the OFC signals the subjective values of possible outcomes to other brain areas for learning and decision making. This popular view generally neglects the fact that OFC is not necessary for simple value-based behavior (i.e., when values have been directly experienced). An alternative, emerging view suggests that OFC plays a more general role in representing structural information about the task or environment, derived from prior experience, and relevant to predicting behavioral outcomes, such as value. From this perspective, value signaling is simply one derivative of the core underlying function of OFC. New data in favor of both views have been accumulating rapidly. Here we review these new data in discussing the relative merits of these two ideas.

Sunk cost effects hinge on the neural recalibration of reference points in mental accounting

The context of reinforcement history drastically influences human value-based choices. Mental accounting theory concerns how prior outcomes are perceived, combined and assigned into specific "mental" accounts to influence subsequent decisions but remains agnostic about the underlying computational and neural mechanisms. In a two-stage sequential decision-making task, we found previously incurred costs and bonuses biased subjects' choices in the opposite directions with similar magnitudes. Such effects were consistent with a computational model where the reference point was recalibrated by prior gains and losses encoded in the ventral striatum activities. Moreover, individual's susceptibility to prior outcomes was captured by the response of the dorsolateral prefrontal cortex and its functional connectivity with the medial orbitofrontal cortex, whose activity tracked the value of the chosen option. Our findings provide both behavioral and neural evidence of how sunk costs, benefits, and prospects are integrated within the mental accounting framework to influence choice behavior.

Neuro-Computational Foundations of Moral Preferences

Moral preferences pervade many aspects of our lives, dictating how we ought to behave, whom we can marry, and even what we eat. Despite their relevance, one fundamental question remains unanswered: Where do individual moral preferences come from? It is often thought that all types of preferences reflect properties of domain-general neural decision mechanisms that employ a common "neural currency" to value choice options in many different contexts. This view, however, appears at odds with the observation that many humans consider it intuitively wrong to employ the same scale to compare moral value (e.g., of a human life) with material value (e.g., of money). In this paper, we directly test if moral subjective values are represented by similar neural processes as financial subjective values. In a study combining fMRI with a novel behavioral paradigm, we identify neural representations of the subjective values of human lives or financial payoffs by means of structurally identical computational models. Correlating isomorphic model variables from both domains with brain activity reveals specific patterns of neural activity that selectively represent values in the moral (rTPJ) or financial (vmPFC) domain. Intriguingly, our findings show that human lives and money are valued in (at least partially) distinct neural currencies, supporting theoretical proposals that human moral behavior is guided by processes that are distinct from those underlying behavior driven by personal material benefit.

Coherent theta activity in the medial and orbital frontal cortices encodes reward value

This study examined how the medial frontal (MFC) and orbital frontal (OFC) cortices process reward information. We simultaneously recorded local field potentials in the two areas as rats consumed liquid sucrose rewards. Both areas exhibited a 4-8 Hz 'theta' rhythm that was phase-locked to the lick cycle. The rhythm tracked shifts in sucrose concentrations and fluid volumes, demonstrating that it is sensitive to differences in reward magnitude. The coupling between the rhythm and licking was stronger in MFC than OFC and varied with response vigor and absolute reward value in the MFC. Spectral analysis revealed zero-lag coherence between the cortical areas, and found evidence for a directionality of the rhythm, with MFC leading OFC. Our findings suggest that consummatory behavior generates simultaneous theta range activity in the MFC and OFC that encodes the value of consumed fluids, with the MFC having a top-down role in the control of consumption.

Cardiac response in aversive and appetitive olfactory conditioning: Evidence for a valence-independent CS-elicited bradycardia

While the examination of conditioned cardiac responses is well established in human fear conditioning research, comparable studies using less-aversive or rather appetitive unconditioned stimuli (UCS) are sparse and results are mixed. Therefore, the aim of this study was a systematic analysis of cardiac reactions in aversive and appetitive conditioning. Olfactory stimuli were used as unconditioned stimuli as they are suitable reinforcers in both an aversive and an appetitive conditioning offering the opportunity for a comparison between conditioned responses. In total, n = 86 participants took part in both an aversive and an appetitive differential conditioning task with a counterbalanced order across participants. Aversive or appetitive odors, respectively, served as UCS and neutral geometrical figures as CS. Subjective ratings, skin conductance response (SCRs), and evoked cardiac reactions were analyzed and compared between tasks. Conditioned responses in subjective ratings could be observed in both aversive conditioning and appetitive conditioning, while SCRs discriminated between CS+ and CS- in aversive conditioning only. Regarding conditioned cardiac responses, the deceleration for the CS+ was longer than for the CS- in both tasks. In addition, a higher deceleration magnitude and a shorter acceleration for the CS+ as compared to the CS- were found in aversive but not in appetitive conditioning. There were medium-size correlations between aversive and appetitive CRs for subjective ratings and none for physiological responses. The results suggest similarities between cardiac response patterns in aversive and appetitive conditioning, which implies that bradycardia in conditioning might not be fear-specific but presents a valence-independent CS-elicited bradycardia.

Using metabolic energy to quantify the subjective value of physical effort

Economists have known for centuries that to understand an individual's decisions, we must consider not only the objective value of the goal at stake, but its subjective value as well. However, achieving that goal ultimately requires expenditure of effort. Surprisingly, despite the ubiquitous role of effort in decision-making and movement, we currently do not understand how effort is subjectively valued in daily movements. Part of the difficulty arises from the lack of an objective measure of effort. Here, we use a physiological approach to address this knowledge gap. We quantified objective effort costs by measuring metabolic cost via expired gas analysis as participants performed a reaching task against increasing resistance. We then used neuroeconomic methods to quantify each individual's subjective valuation of effort. Rather than the diminishing sensitivity observed in reward valuation, effort was valued objectively, on average. This is significantly less than the near-quadratic sensitivity to effort observed previously in force-based motor tasks. Moreover, there was significant inter-individual variability with many participants undervaluing or overvaluing effort. These findings demonstrate that in contrast with monetary decisions in which subjective value exhibits diminishing marginal returns, effort costs are valued more objectively in low-effort reaching movements common in daily life.

Responses to Heartbeats in Ventromedial Prefrontal Cortex Contribute to Subjective Preference-Based Decisions

Forrest Gump or The Matrix? Preference-based decisions are subjective and entail self-reflection. However, these self-related features are unaccounted for by known neural mechanisms of valuation and choice. Self-related processes have been linked to a basic interoceptive biological mechanism, the neural monitoring of heartbeats, in particular in ventromedial prefrontal cortex (vmPFC), a region also involved in value encoding. We thus hypothesized a functional coupling between the neural monitoring of heartbeats and the precision of value encoding in vmPFC. Human participants of both sexes were presented with pairs of movie titles. They indicated either which movie they preferred or performed a control objective visual discrimination that did not require self-reflection. Using magnetoencephalography, we measured heartbeat-evoked responses (HERs) before option presentation and confirmed that HERs in vmPFC were larger when preparing for the subjective, self-related task. We retrieved the expected cortical value network during choice with time-resolved statistical modeling. Crucially, we show that larger HERs before option presentation are followed by stronger value encoding during choice in vmPFC. This effect is independent of overall vmPFC baseline activity. The neural interaction between HERs and value encoding predicted preference-based choice consistency over time, accounting for both interindividual differences and trial-to-trial fluctuations within individuals. Neither cardiac activity nor arousal fluctuations could account for any of the effects. HERs did not interact with the encoding of perceptual evidence in the discrimination task. Our results show that the self-reflection underlying preference-based decisions involves HERs, and that HER integration to subjective value encoding in vmPFC contributes to preference stability.SIGNIFICANCE STATEMENT Deciding whether you prefer Forrest Gump or The Matrix is based on subjective values, which only you, the decision-maker, can estimate and compare, by asking yourself. Yet, how self-reflection is biologically implemented and its contribution to subjective valuation are not known. We show that in ventromedial prefrontal cortex, the neural response to heartbeats, an interoceptive self-related process, influences the cortical representation of subjective value. The neural interaction between the cortical monitoring of heartbeats and value encoding predicts choice consistency (i.e., whether you consistently prefer Forrest Gump over Matrix over time. Our results pave the way for the quantification of self-related processes in decision-making and may shed new light on the relationship between maladaptive decisions and impaired interoception.

How usefulness shapes neural representations during goal-directed behavior

Value is often associated with reward, emphasizing its hedonic aspects. However, when circumstances change, value must also change (a compass outvalues gold, if you are lost). How are value representations in the brain reshaped under different behavioral goals? To answer this question, we devised a new task that decouples usefulness from its hedonic attributes, allowing us to study flexible goal-dependent mapping. Here, we show that, unlike sensory cortices, regions in the prefrontal cortex (PFC)-usually associated with value computation-remap their representation of perceptually identical items according to how useful the item has been to achieve a specific goal. Furthermore, we identify a coding scheme in the PFC that represents value regardless of the goal, thus supporting generalization across contexts. Our work questions the dominant view that equates value with reward, showing how a change in goals triggers a reorganization of the neural representation of value, enabling flexible behavior.

To be specific: The role of orbitofrontal cortex in signaling reward identity

The orbitofrontal cortex (OFC) plays a prominent role in signaling reward expectations. Two important features of rewards are their value (how good they are) and their specific identity (what they are). Whereas research on OFC has traditionally focused on reward value, recent findings point toward a pivotal role of reward identity in understanding OFC signaling and its contribution to behavior. Here, we review work in rodents, nonhuman primates, and humans on how the OFC represents expectations about the identity of rewards, and how these signals contribute to outcome-guided behavior. Moreover, we summarize recent findings suggesting that specific reward expectations in OFC are learned and updated by means of identity errors in the dopaminergic midbrain. We conclude by discussing how OFC encoding of specific rewards complements recent proposals that this region represents a cognitive map of relevant task states, which forms the basis for model-based behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Electroencephalographic Theta-Band Oscillatory Dynamics Represent Attentional Bias to Subjective Preferences in Value-Based Decisions

Background

Previous studies have shown that people always pay more attention to highly preferred items of choice, which is well defined by behavioral measurements and eye-tracking. However, less is known about the neural dynamics underlying the role that visual attention plays in value-based decisions, especially in those characterized by the “relative value” (ie, value difference) between two items displayed simultaneously in a binary choice.

Purpose

This study examined the neural temporal and neural oscillatory features underlying selective attention to subjective preferences in value-based decision making.

Methods

In this study, we recorded electroencephalography (EEG) measurements while participants performed a binary choice task in which they were instructed to respond to their preferred snack in high value difference (HVD) or low value difference (LVD) conditions.

Results

Behaviorally, participants showed faster responses and lower error rates in the HVD condition than in the LVD condition. In parallel, participants exerted a reduced prefrontal N2 component and attenuated frontal theta-band synchronization in the HVD condition as opposed to the LVD condition. Crucially, participants showed greater N2pc component and theta-band synchronization over the human posterior cortex in the HVD condition than in the LVD condition. Moreover, there was a direct correlation between frontal and posterior theta-band synchronization.

Conclusion

The results show that theta-band oscillatory dynamics may represent attentional bias to subjective preferences, and this effect can be modulated by the level attentional bias to subjective preferences, and this effect can be modulated by the level of value difference. Our research provides insights into a new avenue via which the processing of selective attention and value representation in the value-based decisions can be implicated in an integrative neural oscillatory mechanism.