The mirror mechanism: recent findings and perspectives

Mirror neurons are a specific type of visuomotor neuron that discharge both when a monkey executes a motor act and when it observes a similar motor act performed by another individual. In this article, we review first the basic properties of these neurons. We then describe visual features recently investigated which indicate that, besides encoding the goal of motor acts, mirror neurons are modulated by location in space of the observed motor acts, by the perspective from which the others' motor acts are seen, and by the value associated with the object on which others' motor acts are performed. In the last part of this article, we discuss the role of the mirror mechanism in planning actions and in understanding the intention underlying the others' motor acts. We also review some human studies suggesting that motor intention in humans may rely, as in the monkey, on the mirror mechanism.

Orbitofrontal Cortex: A Neural Circuit for Economic Decisions

Economic choice behavior entails the computation and comparison of subjective values. A central contribution of neuroeconomics has been to show that subjective values are represented explicitly at the neuronal level. With this result at hand, the field has increasingly focused on the difficult question of where in the brain and how exactly subjective values are compared to make a decision. Here, we review a broad range of experimental and theoretical results suggesting that good-based decisions are generated in a neural circuit within the orbitofrontal cortex (OFC). The main lines of evidence supporting this proposal include the fact that goal-directed behavior is specifically disrupted by OFC lesions, the fact that different groups of neurons in this area encode the input and the output of the decision process, the fact that activity fluctuations in each of these cell groups correlate with choice variability, and the fact that these groups of neurons are computationally sufficient to generate decisions. Results from other brain regions are consistent with the idea that good-based decisions take place in OFC and indicate that value signals inform a variety of mental functions. We also contrast the present proposal with other leading models for the neural mechanisms of economic decisions. Finally, we indicate open questions and suggest possible directions for future research.

Ventromedial prefrontal cortex supports affective future simulation by integrating distributed knowledge

Although the future often seems intangible, we can make it more concrete by imagining prospective events. Here, using functional MRI, we demonstrate a mechanism by which the ventromedial prefrontal cortex supports such episodic simulations, and thereby contributes to affective foresight: This region supports processes that (i) integrate knowledge related to the elements that constitute an episode and (ii) represent the episode's emergent affective quality. The ventromedial prefrontal cortex achieves such integration via interactions with distributed cortical regions that process the individual elements. Its activation then signals the affective quality of the ensuing episode, which goes beyond the combined affective quality of its constituting elements. The integrative process further augments long-term retention of the episode, making it available at later time points. This mechanism thus renders the future tangible, providing a basis for farsighted behavior.

Parabolic discounting of monetary rewards by physical effort

When humans and other animals make decisions in their natural environments prospective rewards have to be weighed against costs. It is well established that increasing costs lead to devaluation or discounting of reward. While our knowledge about discount functions for time and probability costs is quite advanced, little is known about how physical effort discounts reward. In the present study we compared three different models in a binary choice task in which human participants had to squeeze a handgrip to earn monetary rewards: a linear, a hyperbolic, and a parabolic model. On the group as well as the individual level, the concave parabolic model explained most variance of the choice data, thus contrasting with the typical hyperbolic discounting of reward value by delay. Research on effort discounting is not only important to basic science but also holds the potential to quantify aberrant motivational states in neuropsychiatric disorders.

The Subjective Value of Cognitive Effort is Encoded by a Domain-General Valuation Network

Cognitive control is necessary for goal-directed behavior, yet people treat cognitive control demand as a cost, which discounts the value of rewards in a similar manner as other costs, such as delay or risk. It is unclear, however, whether the subjective value (SV) of cognitive effort is encoded in the same putatively domain-general brain valuation network implicated in other cost domains, or instead engages a distinct frontoparietal network, as implied by recent studies. Here, we provide rigorous evidence that the valuation network, with core foci in the ventromedial prefrontal cortex and ventral striatum, also encodes SV during cognitive effort-based decision-making in healthy, male and female adult humans. We doubly dissociate this network from frontoparietal regions that are instead recruited as a function of decision difficulty. We show that the domain-general valuation network jointly and independently encodes both reward benefits and cognitive effort costs. We also demonstrate that cognitive effort SV signals predict choice and are influenced by state and trait motivation, including sensitivity to reward and anticipated task performance. These findings unify cognitive effort with other cost domains, and suggest candidate neural mechanisms underlying state and trait variation in willingness to expend cognitive effort.SIGNIFICANCE STATEMENT Subjective effort costs are increasingly understood to diminish cognitive control over task performance and can thus undermine functioning across health and disease. Yet, we are only beginning to understand how decisions about cognitive effort are made. A key question is how subjective values are computed. Recent work suggests that the value of cognitive effort might be computed by networks that are distinct from those involved in other domains like intertemporal and risky decision-making, implying distinct mechanisms. Here we demonstrate that the domain-general network also encodes effort-discounted value, linking cognitive effort closely with other domains. Our results thus elucidate key mechanisms supporting decisions about cognitive effort, and point to candidate neural targets for intervention in disorders involving impaired cognitive motivation.

Subjective value representations during effort, probability and time discounting across adulthood

Every day, humans make countless decisions that require the integration of information about potential benefits (i.e. rewards) with other decision features (i.e. effort required, probability of an outcome or time delays). Here, we examine the overlap and dissociation of behavioral preferences and neural representations of subjective value in the context of three different decision features (physical effort, probability and time delays) in a healthy adult life span sample. While undergoing functional neuroimaging, participants (N = 75) made incentive compatible choices between a smaller monetary reward with lower physical effort, higher probability, or a shorter time delay versus a larger monetary reward with higher physical effort, lower probability, or a longer time delay. Behavioral preferences were estimated from observed choices, and subjective values were computed using individual hyperbolic discount functions. We found that discount rates were uncorrelated across tasks. Despite this apparent behavioral dissociation between preferences, we found overlapping subjective value-related activity in the medial prefrontal cortex across all three tasks. We found no consistent evidence for age differences in either preferences or the neural representations of subjective value across adulthood. These results suggest that while the tolerance of decision features is behaviorally dissociable, subjective value signals share a common representation across adulthood.

The dorsomedial prefrontal cortex computes task-invariant relative subjective value for self and other

Few studies have addressed the neural computations underlying decisions made for others despite the importance of this ubiquitous behavior. Using participant-specific behavioral modeling with univariate and multivariate fMRI approaches, we investigated the neural correlates of decision-making for self and other in two independent tasks, including intertemporal and risky choice. Modeling subjective valuation indicated that participants distinguished between themselves and others with dissimilar preferences. Activity in the dorsomedial prefrontal cortex (dmPFC) and ventromedial prefrontal cortex (vmPFC) was consistently modulated by relative subjective value. Multi-voxel pattern analysis indicated that activity in the dmPFC uniquely encoded relative subjective value and generalized across self and other and across both tasks. Furthermore, agent cross-decoding accuracy between self and other in the dmPFC was related to self-reported social attitudes. These findings indicate that the dmPFC emerges as a medial prefrontal node that utilizes a task-invariant mechanism for computing relative subjective value for self and other.

Integration of multiple determinants in the neuronal computation of economic values

Economic goods may vary on multiple dimensions (determinants). A central conjecture in decision neuroscience is that choices between goods are made by comparing subjective values computed through the integration of all relevant determinants. Previous work identified three groups of neurons in the orbitofrontal cortex (OFC) of monkeys engaged in economic choices: (1) offer value cells, which encode the value of individual offers; (2) chosen value cells, which encode the value of the chosen good; and (3) chosen juice cells, which encode the identity of the chosen good. In principle, these populations could be sufficient to generate a decision. Critically, previous work did not assess whether offer value cells (the putative input to the decision) indeed encode subjective values as opposed to physical properties of the goods, and/or whether offer value cells integrate multiple determinants. To address these issues, we recorded from the OFC while monkeys chose between risky outcomes. Confirming previous observations, three populations of neurons encoded the value of individual offers, the value of the chosen option, and the value-independent choice outcome. The activity of both offer value cells and chosen value cells encoded values defined by the integration of juice quantity and probability. Furthermore, both populations reflected the subjective risk attitude of the animals. We also found additional groups of neurons encoding the risk associated with a particular option, the risky nature of the chosen option, and whether the trial outcome was positive or negative. These results provide substantial support for the conjecture described above and for the involvement of OFC in good-based decisions.

Neuroanatomy predicts individual risk attitudes

Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers.

Value signals in the prefrontal cortex predict individual preferences across reward categories

Humans can choose between fundamentally different options, such as watching a movie or going out for dinner. According to the utility concept, put forward by utilitarian philosophers and widely used in economics, this may be accomplished by mapping the value of different options onto a common scale, independent of specific option characteristics (Fehr and Rangel, 2011; Levy and Glimcher, 2012). If this is the case, value-related activity patterns in the brain should allow predictions of individual preferences across fundamentally different reward categories. We analyze fMRI data of the prefrontal cortex while subjects imagine the pleasure they would derive from items belonging to two distinct reward categories: engaging activities (like going out for drinks, daydreaming, or doing sports) and snack foods. Support vector machines trained on brain patterns related to one category reliably predict individual preferences of the other category and vice versa. Further, we predict preferences across participants. These findings demonstrate that prefrontal cortex value signals follow a common scale representation of value that is even comparable across individuals and could, in principle, be used to predict choice.

Neural correlates of emotion regulation in the ventral prefrontal cortex and the encoding of subjective value and economic utility

In many studies of the interaction between cognitive control and emotion, the orbitofrontal cortex/ventromedial prefrontal cortex (mOFC/vmPFC) has been associated with an inhibitory function on limbic areas activated by emotionally arousing stimuli, such as the amygdala. This has led to the hypothesis of an inhibitory or regulatory role of mOFC/vmPFC. In studies of cognition and executive function, however, this area is deactivated by focused effort, raising the issue of the nature of the putative regulatory process associated with mOFC/vmPFC. This issue is here revisited in light of findings in the neuroeconomics field demonstrating the importance of mOFC/vmPFC to encoding the subjective value of stimuli or their economic utility. Many studies show that mOFC/vmPFC activity may affect response by activating personal preferences, instead of resorting to effortful control mechanisms typically associated with emotion regulation. Based on these findings, I argue that a simple automatic/controlled dichotomy is insufficient to describe the data on emotion and control of response adequately. Instead, I argue that the notion of subjective value from neuroeconomics studies and the notion of attentional orienting may play key roles in integrating emotion and cognition. mOFC/vmPFC may work together with the inferior parietal lobe, the cortical region associated with attentional orienting, to convey information about motivational priorities and facilitate processing of inputs that are behaviorally relevant. I also suggest that the dominant mode of function of this ventral network may be a distinct type of process with intermediate properties between the automatic and the controlled, and which may co-operate with effortful control processes in order to steer response.

An Analysis of Decision under Risk in Rats

In 1979, Daniel Kahneman and Amos Tversky published a ground-breaking paper titled "Prospect Theory: An Analysis of Decision under Risk," which presented a behavioral economic theory that accounted for the ways in which humans deviate from economists' normative workhorse model, Expected Utility Theory [1, 2]. For example, people exhibit probability distortion (they overweight low probabilities), loss aversion (losses loom larger than gains), and reference dependence (outcomes are evaluated as gains or losses relative to an internal reference point). We found that rats exhibited many of these same biases, using a task in which rats chose between guaranteed and probabilistic rewards. However, prospect theory assumes stable preferences in the absence of learning, an assumption at odds with alternative frameworks such as animal learning theory and reinforcement learning [3-7]. Rats also exhibited trial history effects, consistent with ongoing learning. A reinforcement learning model in which state-action values were updated by the subjective value of outcomes according to prospect theory reproduced rats' nonlinear utility and probability weighting functions and also captured trial-by-trial learning dynamics.

A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks

Previous research has provided qualitative evidence for overlap in a number of brain regions across the subjective value network (SVN) and the default mode network (DMN). In order to quantitatively assess this overlap, we conducted a series of coordinate-based meta-analyses (CBMA) of results from 466 functional magnetic resonance imaging experiments on task-negative or subjective value-related activations in the human brain. In these analyses, we first identified significant overlaps and dissociations across activation foci related to SVN and DMN. Second, we investigated whether these overlapping subregions also showed similar patterns of functional connectivity, suggesting a shared functional subnetwork. We find considerable overlap between SVN and DMN in subregions of central ventromedial prefrontal cortex (cVMPFC) and dorsal posterior cingulate cortex (dPCC). Further, our findings show that similar patterns of bidirectional functional connectivity between cVMPFC and dPCC are present in both networks. We discuss ways in which our understanding of how subjective value (SV) is computed and represented in the brain can be synthesized with what we know about the DMN, mind-wandering, and self-referential processing in light of our findings.

An fMRI investigation of delay discounting in patients with schizophrenia

Schizophrenia (SZ) is associated with a reduced ability to set meaningful goals to reach desired outcomes. The delay-discounting (DD) task, in which one chooses between sooner smaller and later larger rewards, has proven useful in revealing executive function and reward deficits in various clinical groups. We used fMRI in patients with SZ and healthy controls (HC) to compare brain activation during performance of a DD task. Prior to the neuroimaging session, we obtained each participant's rate of DD, k, on a DD task and used it to select a version of the DD task for each participant's fMRI session. Because of the importance of comparing fMRI results from groups matched on performance, we used a criterion value of R (2) > 0.60 for response consistency on the DD task to analyze fMRI activation to DD task versus control trials from consistent SZ (n = 14) and consistent HC (n = 14). We also compared activation between the groups on contrasts related to trial difficulty. Finally, we contrasted the inconsistent SZ (n = 9) with the consistent HC and consistent SZ; these results should be interpreted with caution because of inconsistent SZ's aberrant performance on the task. Compared with consistent HC, consistent SZ showed reduced activation to DD task versus control trials in executive function and reward areas. In contrast, consistent SZ showed more activation in the precuneus and posterior cingulate, regions of the default mode network (DMN) that are typically deactivated during tasks, and in the insula, a region linked to emotional processing. Furthermore, consistent SZ had abnormal activation of lateral and medial frontal regions in relation to trial difficulty. These results point to disruption of several neural networks during decision making, including the executive, reward, default mode, and emotional networks, and suggest processes that are impaired during decision making in schizophrenia.

Neural mechanisms of economic choices in mice

Economic choices entail computing and comparing subjective values. Evidence from primates indicates that this behavior relies on the orbitofrontal cortex. Conversely, previous work in rodents provided conflicting results. Here we present a mouse model of economic choice behavior, and we show that the lateral orbital (LO) area is intimately related to the decision process. In the experiments, mice chose between different juices offered in variable amounts. Choice patterns closely resembled those measured in primates. Optogenetic inactivation of LO dramatically disrupted choices by inducing erratic changes of relative value and by increasing choice variability. Neuronal recordings revealed that different groups of cells encoded the values of individual options, the binary choice outcome and the chosen value. These groups match those previously identified in primates, except that the neuronal representation in mice is spatial (in monkeys it is good-based). Our results lay the foundations for a circuit-level analysis of economic decisions.

Activity in ventromedial prefrontal cortex during self-related processing: positive subjective value or personal significance?

Well-being and subjective experience of a coherent world depend on our sense of 'self' and relations between the self and the environment (e.g. people, objects and ideas). The ventromedial prefrontal cortex (vMPFC) is involved in self-related processing, and disrupted vMPFC activity is associated with disruptions of emotional/social functioning (e.g. depression and autism). Clarifying precise function(s) of vMPFC in self-related processing is an area of active investigation. In this study, we sought to more specifically characterize the function of vMPFC in self-related processing, focusing on two alternative accounts: (i) assignment of positive subjective value to self-related information and (ii) assignment of personal significance to self-related information. During functional magnetic resonance imaging (fMRI), participants imagined owning objects associated with either their perceived ingroup or outgroup. We found that for ingroup-associated objects, vMPFC showed greater activity for objects with increased than decreased post-ownership preference. In contrast, for outgroup-associated objects, vMPFC showed greater activity for objects with decreased than increased post-ownership preference. Our findings support the idea that the function of vMPFC in self-related processing may not be to represent/evaluate the 'positivity' or absolute preference of self-related information but to assign personal significance to it based on its meaning/function for the self.

Contributions of the Medial Prefrontal Cortex to Social Influence in Economic Decision-Making

Economic decisions are guided by highly subjective reward valuations (SVs). Often these SVs are over-ridden when individuals conform to social norms. Yet, the neural mechanisms that underpin the distinct processing of such normative reward valuations (NVs) are poorly understood. The dorsomedial and ventromedial portions of the prefrontal cortex (dmPFC/vmPFC) are putatively key regions for processing social and economic information respectively. However, the contribution of these regions to economic decisions guided by social norms is unclear. Using functional magnetic resonance imaging and computational modeling we examine the neural mechanisms underlying the processing of SVs and NVs. Subjects (n = 15) indicated either their own economic preferences or made similar choices based on a social norm-learnt during a training session. We found that that the vmPFC and dmPFC make dissociable contributions to the processing of SV and NV. Regions of the dmPFC processed "only" the value of rewards when making normative choices. In contrast, we identify a novel mechanism in the vmPFC for the coding of value. This region signaled both subjective and normative valuations, but activity was scaled positively for SV and negatively for NV. These results highlight some of the key mechanisms that underpin conformity and social influence in economic decision-making.

The Presence of Real Food Usurps Hypothetical Health Value Judgment in Overweight People

To develop more ecologically valid models of the neurobiology of obesity, it is critical to determine how the neural processes involved in food-related decision-making translate into real-world eating behaviors. We examined the relationship between goal-directed valuations of food images in the MRI scanner and food consumption at a subsequent ad libitum buffet meal. We observed that 23 lean and 40 overweight human participants showed similar patterns of value-based neural responses to health and taste attributes of foods. In both groups, these value-based responses in the ventromedial PFC were predictive of subsequent consumption at the buffet. However, overweight participants consumed a greater proportion of unhealthy foods. This was not predicted by in-scanner choices or neural response. Moreover, in overweight participants alone, impulsivity scores predicted greater consumption of unhealthy foods. Overall, our findings suggest that, while the hypothetical valuation of the health of foods is predictive of eating behavior in both lean and overweight people, it is only the real-world food choices that clearly distinguish them.

Partial Adaptation to the Value Range in the Macaque Orbitofrontal Cortex

Values available for choice in different behavioral contexts can vary immensely. To compensate for this variability, neuronal circuits underlying economic decisions undergo adaptation. In orbitofrontal cortex (OFC), neurons encode the subjective value of offered and chosen goods in a quasilinear way. Previous experiments found that the gain of the encoding is lower when the value range is wider. However, the parameters OFC neurons adapted to remained unclear. Furthermore, previous studies did not examine additive changes in neuronal responses. Computational considerations indicate that these factors can directly impact choice behavior. Here we investigated how OFC neurons adapt to changes in the value range. We recorded from two male rhesus monkeys during a juice choice task. Each session was divided into two blocks of trials. In each block, juices were offered within a set range of values, and ranges changed between blocks. Across blocks, neuronal responses adapted to both the maximum and the minimum value, but only partially. As a result, the minimum neural activity was elevated in some value ranges relative to others. Through simulation of a linear decision model, we showed that increasing the minimum response increases choice variability, lowering the expected payoff. This effect is modulated by the balance between cells with positive and negative encoding. The presence of these two populations induces a non-monotonic relationship between the value range and choice efficacy, such that the expected payoff is highest for decisions in an intermediate value range.SIGNIFICANCE STATEMENT Economic decisions are thought to rely on the orbitofrontal cortex (OFC). The values available for choice vary enormously in different contexts. Previous work showed that neurons in OFC encode values in a linear way, and that the gain of encoding is inversely related to the range of available values. However, the specific parameters driving adaptation remained unclear. Here we show that OFC neurons adapt to both the maximum and minimum value in the current context. However, adaptation is partial, leading to contextual changes in the response offset. Interestingly, increasing the activity offset negatively affects choices in a simulated network. Partial adaptation may allow the circuit to maintain information about context value at the cost of slightly reduced payoff.

Neuronal variability in orbitofrontal cortex during economic decisions

Neuroeconomic models assume that economic decisions are based on the activity of offer value cells in the orbitofrontal cortex (OFC), but testing this assertion has proven difficult. In principle, the decision made on a given trial should correlate with the stochastic fluctuations of these cells. However, this correlation, measured as a choice probability (CP), is small. Importantly, a neuron's CP reflects not only its individual contribution to the decision (termed readout weight), but also the intensity and the structure of correlated variability across the neuronal population (termed noise correlation). A precise mathematical relation between CPs, noise correlations, and readout weights was recently derived by Haefner and colleagues (Haefner RM, Gerwinn S, Macke JH, Bethge M. Nat Neurosci 16: 235-242, 2013) for a linear decision model. In this framework, concurrent measurements of noise correlations and CPs can provide quantitative information on how a population of cells contributes to a decision. Here we examined neuronal variability in the OFC of rhesus monkeys during economic decisions. Noise correlations had similar structure but considerably lower strength compared with those typically measured in sensory areas during perceptual decisions. In contrast, variability in the activity of individual cells was high and comparable to that recorded in other cortical regions. Simulation analyses based on Haefner's equation showed that noise correlations measured in the OFC combined with a plausible readout of offer value cells reproduced the experimental measures of CPs. In other words, the results obtained for noise correlations and those obtained for CPs taken together support the hypothesis that economic decisions are primarily based on the activity of offer value cells.

Trading mental effort for confidence in the metacognitive control of value-based decision-making

Why do we sometimes opt for actions or items that we do not value the most? Under current neurocomputational theories, such preference reversals are typically interpreted in terms of errors that arise from the unreliable signaling of value to brain decision systems. But, an alternative explanation is that people may change their mind because they are reassessing the value of alternative options while pondering the decision. So, why do we carefully ponder some decisions, but not others? In this work, we derive a computational model of the metacognitive control of decisions or MCD. In brief, we assume that fast and automatic processes first provide initial (and largely uncertain) representations of options' values, yielding prior estimates of decision difficulty. These uncertain value representations are then refined by deploying cognitive (e.g., attentional, mnesic) resources, the allocation of which is controlled by an effort-confidence tradeoff. Importantly, the anticipated benefit of allocating resources varies in a decision-by-decision manner according to the prior estimate of decision difficulty. The ensuing MCD model predicts response time, subjective feeling of effort, choice confidence, changes of mind, as well as choice-induced preference change and certainty gain. We test these predictions in a systematic manner, using a dedicated behavioral paradigm. Our results provide a quantitative link between mental effort, choice confidence, and preference reversals, which could inform interpretations of related neuroimaging findings.

A Reduction in Delay Discounting by Using Episodic Future Imagination and the Association with Episodic Memory Capacity

Delay discounting (DD) refers to the phenomenon that individuals discount future consequences. Previous studies showed that future imagination reduces DD, which was mediated by functional connectivity between medial prefrontal valuation areas and a key region for episodic memory (hippocampus). Future imagination involves an initial period of construction and a later period of elaboration, with the more elaborative latter period recruiting more cortical regions. This study examined whether elaborative future imagination modulated DD, and if so, what are the underlying neural substrates. It was assumed that cortical areas contribute to the modulation effect during the later period of imagination. Since future imagination is supported by episodic memory capacity, we additionally hypothesize that the neural network underlying the modulation effect is related to individual episodic memory capacity. Twenty-two subjects received an extensive interview on personal future events, followed by an fMRI DD experiment with and without the need to perform elaborative future imagination simultaneously. Subjects' episodic memory capacity was also assessed. Behavioral results replicate previous findings of a reduced discount rate in the DD plus imagination condition compared to the DD only condition. The behavioral effect positively correlated with: (i) subjective value signal changes in midline brain structures during the initial imagination period; and (ii) signal changes in left prefrontoparietal areas during the later imagination period. Generalized psychophysiological interaction (gPPI) analyses reveal positive correlations between the behavioral effect and functional connectivity among the following areas: right anterior cingulate cortex (ACC) and left hippocampus; left inferior parietal cortex (IPC) and left hippocampus; and left IPC and bilateral occipital cortices. These changes in functional connectivity are also associated with episodic memory capacity. A hierarchical multiple regression indicates that the model with both the valuation related signal changes in the right ACC and the imagination related signal changes in the left IPC best predicts the reduction in DD. This study illustrates interactions between the left hippocampus and multiple cortical regions underlying the modulation effect of elaborative episodic future imagination, demonstrating, for the first time, empirical support for a relation to individual episodic memory capacity.

Components and characteristics of the dopamine reward utility signal

Rewards are defined by their behavioral functions in learning (positive reinforcement), approach behavior, economic choices, and emotions. Dopamine neurons respond to rewards with two components, similar to higher order sensory and cognitive neurons. The initial, rapid, unselective dopamine detection component reports all salient environmental events irrespective of their reward association. It is highly sensitive to factors related to reward and thus detects a maximal number of potential rewards. It also senses aversive stimuli but reports their physical impact rather than their aversiveness. The second response component processes reward value accurately and starts early enough to prevent confusion with unrewarded stimuli and objects. It codes reward value as a numeric, quantitative utility prediction error, consistent with formal concepts of economic decision theory. Thus, the dopamine reward signal is fast, highly sensitive and appropriate for driving and updating economic decisions.

Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

Corticostriatal Circuits Encode the Subjective Value of Perceived Control

The ability to perceive and exercise control over an outcome is both desirable and beneficial to our well-being. It has been shown that animals and humans alike exhibit behavioral bias towards seeking control and that such bias recruits the ventromedial prefrontal cortex (vmPFC) and striatum. Yet, this bias remains to be quantitatively captured and studied neurally. Here, we employed a behavioral task to measure the preference for control and characterize its neural underpinnings. Participants made a series of binary choices between having control and no-control over a game for monetary reward. The mere presence of the control option evoked activity in the ventral striatum. Importantly, we manipulated the expected value (EV) of each choice pair to extract the pairing where participants were equally likely to choose either option. The difference in EV between the options at this point of equivalence was inferred as the subjective value of control. Strikingly, perceiving control inflated the reward value of the associated option by 30% and this value inflation was tracked by the vmPFC. Altogether, these results capture the subjective value of perceived control inherent in decision making and highlight the role of corticostriatal circuitry in the perception of control.