Selective and graded coding of reward uncertainty by neurons in the primate anterodorsal septal region

Dorsal raphe neurons integrate the values of reward amount, delay, and uncertainty in multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when confronting reward uncertainty. However, it has been unclear whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider these attributes to make a choice. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes, and this population tended to integrate the attributes in a manner that reflected monkeys' preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how the DRN participates in value computations, guiding theories about the role of the DRN in decision-making and psychiatric disease.

Curiosity: primate neural circuits for novelty and information seeking

For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.

An opponent striatal circuit for distributional reinforcement learning

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.

Modeling fashion as an emergent collective behavior of bored individuals

Boredom is an aversive mental state that is typically evoked by monotony and drives individuals to seek novel information. Despite this effect on individual behavior, the consequences of boredom for collective behavior remain elusive. Here, we introduce an agent-based model of collective fashion behavior in which simplified agents interact randomly and repeatedly choose alternatives from a circular space of color variants. Agents are endowed with a memory of past experiences and a boredom parameter, promoting avoidance of monotony. Simulating collective color trends with this model captures aspects of real trends observed in fashion magazines. We manipulate the two parameters and observe that the boredom parameter is essential for perpetuating fashion dynamics in our model. Furthermore, highly bored agents lead future population trends, when acting coherently or being highly popular. Taken together, our study illustrates that highly bored individuals can guide collective dynamics of a population to continuously explore different variants of behavior.

Studying the neural representations of uncertainty

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.

Rewarding-unrewarding prediction signals under a bivalent context in the primate lateral hypothalamus

Animals can expect rewards under equivocal situations. The lateral hypothalamus (LH) is thought to process motivational information by producing valence signals of reward and punishment. Despite rich studies using rodents and non-human primates, these signals have been assessed separately in appetitive and aversive contexts; therefore, it remains unclear what information the LH encodes in equivocal situations. To address this issue, macaque monkeys were conditioned under a bivalent context in which reward and punishment were probabilistically delivered, in addition to appetitive and aversive contexts. The monkeys increased approaching behavior similarly in the bivalent and appetitive contexts as the reward probability increased. They increased avoiding behavior under the bivalent and aversive contexts as the punishment probability increased, but the mean frequency was lower under the bivalent context than under the aversive context. The population activity correlated with these mean behaviors. Moreover, the LH produced fine prediction signals of reward expectation, uncertainty, and predictability consistently in the bivalent and appetitive contexts by recruiting context-independent and context-dependent subpopulations of neurons, while it less produced punishment signals in the aversive and bivalent contexts. Further, neural ensembles encoded context information and "rewarding-unrewarding" and "reward-punishment" valence. These signals may motivate individuals robustly in equivocal environments.

Weaker situations: Uncertainty reveals individual differences in learning: Implications for PTSD

Few individuals who experience trauma develop posttraumatic stress disorder (PTSD). Therefore, the identification of individual differences that signal increased risk for PTSD is important. Lissek et al. (2006) proposed using a weak rather than a strong situation to identify individual differences. A weak situation involves less-salient cues as well as some degree of uncertainty, which reveal individual differences. A strong situation involves salient cues with little uncertainty, which produce consistently strong responses. Results from fear conditioning studies that support this hypothesis are discussed briefly. This review focuses on recent findings from three learning tasks: classical eyeblink conditioning, avoidance learning, and a computer-based task. These tasks are interpreted as weaker learning situations in that they involve some degree of uncertainty. Individual differences in learning based on behavioral inhibition, which is a risk factor for PTSD, are explored. Specifically, behaviorally inhibited individuals and rodents (i.e., Wistar Kyoto rats), as well as individuals expressing PTSD symptoms, exhibit enhanced eyeblink conditioning. Behaviorally inhibited rodents also demonstrate enhanced avoidance responding (i.e., lever pressing). Both enhanced eyeblink conditioning and avoidance are most evident with schedules of partial reinforcement. Behaviorally inhibited individuals also performed better on reward and punishment trials than noninhibited controls in a probabilistic category learning task. Overall, the use of weaker situations with uncertain relationships may be more ecologically valid than learning tasks in which the aversive event occurs on every trial and may provide more sensitivity for identifying individual differences in learning for those at risk for, or expressing, PTSD symptoms.

Uncertainty modulates visual maps during noninstrumental information demand

Animals are intrinsically motivated to obtain information independently of instrumental incentives. This motivation depends on two factors: a desire to resolve uncertainty by gathering accurate information and a desire to obtain positively-valenced observations, which predict favorable rather than unfavorable outcomes. To understand the neural mechanisms, we recorded parietal cortical activity implicated in prioritizing stimuli for spatial attention and gaze, in a task in which monkeys were free (but not trained) to obtain information about probabilistic non-contingent rewards. We show that valence and uncertainty independently modulated parietal neuronal activity, and uncertainty but not reward-related enhancement consistently correlated with behavioral sensitivity. The findings suggest uncertainty-driven and valence-driven information demand depend on partially distinct pathways, with the former being consistently related to parietal responses and the latter depending on additional mechanisms implemented in downstream structures.

Curiosity is motivated by uncertainty and valence, but how uncertainty and valence are encoded in the brain remains poorly understood. Here, the authors show that parietal neurons are enhanced by both factors, but that they specifically predict visual information seeking based on uncertainty.

Neural population clocks: Encoding time in dynamic patterns of neural activity

The ability to predict and prepare for near- and far-future events is among the most fundamental computations the brain performs. Because of the importance of time for prediction and sensorimotor processing, the brain has evolved multiple mechanisms to tell and encode time across scales ranging from microseconds to days and beyond. Converging experimental and computational data indicate that, on the scale of seconds, timing relies on diverse neural mechanisms distributed across different brain areas. Among the different encoding mechanisms on the scale of seconds, we distinguish between neural population clocks and ramping activity as distinct strategies to encode time. One instance of neural population clocks, neural sequences, represents in some ways an optimal and flexible dynamic regime for the encoding of time. Specifically, neural sequences comprise a high-dimensional representation that can be used by downstream areas to flexibly generate arbitrarily simple and complex output patterns using biologically plausible learning rules. We propose that high-level integration areas may use high-dimensional dynamics such as neural sequences to encode time, providing downstream areas information to build low-dimensional ramp-like activity that can drive movements and temporal expectation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Seeking motivation and reward: roles of dopamine, hippocampus and supramammillo-septal pathway

Reinforcement learning and goal-seeking behavior are thought to be mediated by midbrain dopamine neurons. However, little is known about neural substrates of curiosity and exploratory behavior, which occur in the absence of clear goal or reward. This is despite behavioral scientists having long suggested that curiosity and exploratory behaviors are regulated by an innate drive. We refer to such behavior as information-seeking behavior and propose 1) key neural substrates and 2) the concept of environment prediction error as a framework to understand information-seeking processes. The cognitive aspect of information-seeking behavior, including the perception of salience and uncertainty, involves, in part, the pathways from the posterior hypothalamic supramammillary region to the hippocampal formation. The vigor of such behavior is modulated by the following: supramammillary glutamatergic neurons; their projections to medial septal glutamatergic neurons; and the projections of medial septal glutamatergic neurons to ventral tegmental dopaminergic neurons. Phasic responses of dopaminergic neurons are characterized as signaling potentially important stimuli rather than rewards. This paper describes how novel stimuli and uncertainty trigger seeking motivation and how these neural substrates modulate information-seeking behavior.

Child witness expressions of certainty are informative

Children are frequently witnesses of crime. In the witness literature and legal systems, children are often deemed to have unreliable memories. Yet, in the basic developmental literature, young children can monitor their memory. To address these contradictory conclusions, we reanalyzed the confidence-accuracy relationship in basic and applied research. Confidence provided considerable information about memory accuracy, from at least age 8, but possibly younger. We also conducted an experiment where children in young (4-6 years), middle (7-9 years), and late (10-17 years) childhood (N = 2,205) watched a person in a video and then identified that person from a police lineup. Children provided a confidence rating (an explicit judgment) and used an interactive lineup-in which the lineup faces can be rotated-and we analyzed children's viewing behavior (an implicit measure of metacognition). A strong confidence-accuracy relationship was observed from age 10 and an emerging relationship from age 7. A constant likelihood ratio signal-detection model can be used to understand these findings. Moreover, in all ages, interactive viewing behavior differed in children who made correct versus incorrect suspect identifications. Our research reconciles the apparent divide between applied and basic research findings and suggests that the fundamental architecture of metacognition that has previously been evidenced in basic list-learning paradigms also underlies performance on complex applied tasks. Contrary to what is believed by legal practitioners, but similar to what has been found in the basic literature, identifications made by children can be reliable when appropriate metacognitive measures are used to estimate accuracy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A prefrontal network integrates preferences for advance information about uncertain rewards and punishments

Humans and animals can be strongly motivated to seek information to resolve uncertainty about rewards and punishments. In particular, despite its clinical and societal relevance, very little is known about information seeking about punishments. We show that attitudes toward information about punishments and rewards are distinct and separable at both behavioral and neuronal levels. We demonstrate the existence of prefrontal neuronal populations that anticipate opportunities to gain information in a relatively valence-specific manner, separately anticipating information about either punishments or rewards. These neurons are located in anatomically interconnected subregions of anterior cingulate cortex (ACC) and ventrolateral prefrontal cortex (vlPFC) in area 12o/47. Unlike ACC, vlPFC also contains a population of neurons that integrate attitudes toward both reward and punishment information, to encode the overall preference for information in a bivalent manner. This cortical network is well suited to mediate information seeking by integrating the desire to resolve uncertainty about multiple, distinct motivational outcomes.

Common coding of expected value and value uncertainty memories in the prefrontal cortex and basal ganglia output

Recent evidence implicates both basal ganglia and ventrolateral prefrontal cortex (vlPFC) in encoding value memories. However, comparative roles of cortical and basal nodes in value memory are not well understood. Here, single-unit recordings in vlPFC and substantia nigra reticulata (SNr), within macaque monkeys, revealed a larger value signal in SNr that was nevertheless correlated with and had a comparable onset to the vlPFC value signal. The value signal was maintained for many objects (>90) many weeks after reward learning and was resistant to extinction in both regions and to repetition suppression in vlPFC. Both regions showed comparable granularity in encoding expected value and value uncertainty, which was paralleled by enhanced gaze bias during free viewing. The value signal dynamics in SNr could be predicted by combining responses of vlPFC neurons according to their value preferences consistent with a scheme in which cortical neurons reached SNr via direct and indirect pathways.

Environment-based object values learned by local network in the striatum tail

Choosing good objects is a fundamental behavior for all animals, to which the basal ganglia (BG) contribute extensively. However, the object choice needs to be changed in different environments. The mechanism of object choice is based on the neuronal circuits originating from output neurons (MSNs) in the striatum. We found that the environment information is provided by fast-spiking interneurons (FSIs) connecting to the MSN circuit. More critically, the experimental reduction of the FSI-input to MSNs disabled the monkey to learn the environment-based object choice. This proved that the object choice controlled by the downstream BG circuit is modulated by the environmental context controlled by the internal circuits in the top of BG circuit. This is important for our flexible decision.

Basal ganglia contribute to object-value learning, which is critical for survival. The underlying neuronal mechanism is the association of each object with its rewarding outcome. However, object values may change in different environments and we then need to choose different objects accordingly. The mechanism of this environment-based value learning is unknown. To address this question, we created an environment-based value task in which the value of each object was reversed depending on the two scene-environments (X and Y). After experiencing this task repeatedly, the monkeys became able to switch the choice of object when the scene-environment changed unexpectedly. When we blocked the inhibitory input from fast-spiking interneurons (FSIs) to medium spiny projection neurons (MSNs) in the striatum tail by locally injecting IEM-1460, the monkeys became unable to learn scene-selective object values. We then studied the mechanism of the FSI-MSN connection. Before and during this learning, FSIs responded to the scenes selectively, but were insensitive to object values. In contrast, MSNs became able to discriminate the objects (i.e., stronger response to good objects), but this occurred clearly in one of the two scenes (X or Y). This was caused by the scene-selective inhibition by FSI. As a whole, MSNs were divided into two groups that were sensitive to object values in scene X or in scene Y. These data indicate that the local network of striatum tail controls the learning of object values that are selective to the scene-environment. This mechanism may support our flexible switching behavior in various environments.

Non-human primates use combined rules when deciding under ambiguity

Decision outcomes in unpredictable environments may not have exact known probabilities. Yet the predictability level of outcomes matters in decisions, and animals, including humans, generally avoid ambiguous options. Managing ambiguity may be more challenging and requires stronger cognitive skills than decision-making under risk, where decisions involve known probabilities. Here we compare decision-making in capuchins, macaques, orangutans, gorillas, chimpanzees and bonobos in risky and ambiguous contexts. Subjects were shown lotteries (a tray of potential rewards, some large, some small) and could gamble a medium-sized food item to obtain one of the displayed rewards. The odds of winning and losing varied and were accessible in the risky context (all rewards were visible) or partially available in the ambiguous context (some rewards were covered). In the latter case, the level of information varied from fully ambiguous (individuals could not guess what was under the covers) to predictable (individuals could guess). None of the species avoided gambling in ambiguous lotteries and gambling rates were high if at least two large rewards were visible. Capuchins and bonobos ignored the covered items and gorillas and macaques took the presence of potential rewards into account, but only chimpanzees and orangutans could consistently build correct expectations about the size of the covered rewards. Chimpanzees and orangutans combined decision rules according to the number of large visible rewards and the level of predictability, a process resembling conditional probabilities assessment in humans. Despite a low sample size, this is the first evidence in non-human primates that a combination of several rules can underlie choices made in an unpredictable environment. Our finding that non-human primates can deal with the uncertainty of an outcome when exchanging one food item for another is a key element to the understanding of the evolutionary origins of economic behaviour. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.

Reward uncertainty asymmetrically affects information transmission within the monkey fronto-parietal network

A central hypothesis in research on executive function is that controlled information processing is costly and is allocated according to the behavioral benefits it brings. However, while computational theories predict that the benefits of new information depend on prior uncertainty, the cellular effects of uncertainty on the executive network are incompletely understood. Using simultaneous recordings in monkeys, we describe several mechanisms by which the fronto-parietal network reacts to uncertainty. We show that the variance of expected rewards, independently of the value of the rewards, was encoded in single neuron and population spiking activity and local field potential (LFP) oscillations, and, importantly, asymmetrically affected fronto-parietal information transmission (measured through the coherence between spikes and LFPs). Higher uncertainty selectively enhanced information transmission from the parietal to the frontal lobe and suppressed it in the opposite direction, consistent with Bayesian principles that prioritize sensory information according to a decision maker’s prior uncertainty.

Bahareh Taghizadeh and Nicholas Foley et al. show that individual neuronal responses, population spiking activity, and local field potential oscillations encode the variance of expected rewards independent of their value. They also demonstrate that reward uncertainty asymmetrically affects neuronal transmission within the monkey fronto-parietal network.

How Outcome Uncertainty Mediates Attention, Learning, and Decision-Making

Animals and humans evolved sophisticated nervous systems that endowed them with the ability to form internal-models or beliefs and make predictions about the future to survive and flourish in a world in which future outcomes are often uncertain. Crucial to this capacity is the ability to adjust behavioral and learning policies in response to the level of uncertainty. Until recently, the neuronal mechanisms that could underlie such uncertainty-guided control have been largely unknown. In this review, I discuss newly discovered neuronal circuits in primates that represent uncertainty about future rewards and propose how they guide information-seeking, attention, decision-making, and learning to help us survive in an uncertain world. Lastly, I discuss the possible relevance of these findings to learning in artificial systems.

Unpredictability as a modulator of drug self-administration: Relevance for substance-use disorders

Drug self-administration has been regarded as a gold-standard preclinical model of addiction and substance-use disorder (SUD). However, investigators are becoming increasingly aware, that certain aspects of addiction or SUDs experienced by humans are not accurately captured in our preclinical self-administration models. The current review will focus on two such aspects of current preclinical drug self-administration models: 1) Predictable vs. unpredictable drug access in terms of the time and effort put into obtaining drugs (i.e., response requirement) and drug quality (i.e., amount) and 2) rich vs. lean access to drugs. Some behavioral and neurobiological mechanisms that could contribute to excessive allocation of behavior toward drug-seeking and drug-taking at the expense of engaging in nondrug-related activities are discussed, and some directions for future research are identified. Based on the experiments reviewed, lean and unpredictable drug access could worsen drug-seeking and drug-taking behavior in individuals with SUDs. Once more fully explored, this area of research will help determine whether and how unpredictable and lean cost requirements affect drug self-administration in preclinical laboratory studies with nonhuman subjects and will help determine whether incorporating these conditions in current self-administration models will increase their predictive validity.

Human decisions about when to act originate within a basal forebrain-nigral circuit

Decision-making studies often focus on brain mechanisms for selecting between goals and actions; however, another important, and often neglected, aspect of decision-making in humans concerns whether, at any given point in time, it is worth making any action at all. We showed that a considerable portion of the variance in when voluntary actions are emitted can be explained by a simple model that that takes into account key features of the current environment. By using ultrahigh-field MRI we identified a multilayered circuit in the human brain originating far beyond the medial frontal areas typically linked to human voluntary action starting in the basal forebrain and brain stem, converging in the dopaminergic midbrain, and only then projecting to striatum and cortex.

Decisions about when to act are critical for survival in humans as in animals, but how a desire is translated into the decision that an action is worth taking at any particular point in time is incompletely understood. Here we show that a simple model developed to explain when animals decide it is worth taking an action also explains a significant portion of the variance in timing observed when humans take voluntary actions. The model focuses on the current environment’s potential for reward, the timing of the individual’s own recent actions, and the outcomes of those actions. We show, by using ultrahigh-field MRI scanning, that in addition to anterior cingulate cortex within medial frontal cortex, a group of subcortical structures including striatum, substantia nigra, basal forebrain (BF), pedunculopontine nucleus (PPN), and habenula (HB) encode trial-by-trial variation in action time. Further analysis of the activity patterns found in each area together with psychophysiological interaction analysis and structural equation modeling suggested a model in which BF integrates contextual information that will influence the decision about when to act and communicates this information, in parallel with PPN and HB influences, to nigrostriatal circuits. It is then in the nigrostriatal circuit that action initiation per se begins.

Adaptive learning under expected and unexpected uncertainty

The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.

Primate prefrontal neurons signal economic risk derived from the statistics of recent reward experience

Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects ('object risk') or actions ('action risk'). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.

Parietal neurons encode information sampling based on decision uncertainty

In natural behavior animals actively gather information that is relevant for learning or actions, but the mechanisms of active sampling are rarely investigated. We tested parietal neurons involved in oculomotor control in a task in which monkeys made saccades to gather visual information before reporting a decision based on the information. We show that the neurons encode, before the saccade, the information gains (reduction in decision uncertainty) that the saccade was expected to bring, correlating with the monkeys’ efficiency in processing the information in the post-saccadic fixation. Informational sensitivity is independent of the neurons’ reward sensitivity, which is unreliable across task contexts, inconsistent with the view that the cells encode economic utility. Instead, we suggest that parietal cells are involved in implementing active sampling policies, showing uncertainty-dependent boosts of neural gain that facilitate the selection of relevant cues and the efficient use of the information delivered by these cues.

Diverse motives for human curiosity

Curiosity-our desire to know-is a fundamental drive in human behaviour, but its mechanisms are poorly understood. A classical question concerns the curiosity motives. What drives individuals to become curious about some but not other sources of information?¹ Here we show that curiosity about probabilistic events depends on multiple aspects of the distribution of these events. Participants (n = 257) performed a task in which they could demand advance information about only one of two randomly selected monetary prizes that contributed to their income. Individuals differed markedly in the extent to which they requested information as a function of the ex ante uncertainty or ex ante value of an individual prize. This heterogeneity was not captured by theoretical models describing curiosity as a desire to learn about the total rewards of a situation^2,3. Instead, it could be explained by an extended model that allowed for attribute-specific anticipatory utility-the savouring of individual components of the eventual reward-and postulates that this utility increased nonlinearly with the certainty of receiving the reward. Parameter values fitting individual choices were consistent for information about gains or losses, suggesting that attribute-specific anticipatory utility captures fundamental heterogeneity in the determinants of curiosity.

Encoding prediction signals during appetitive and aversive Pavlovian conditioning in the primate lateral hypothalamus

The lateral hypothalamus (LH), which plays a role in homeostatic functions such as appetite regulation, is also linked to arousal and motivational behavior. However, little is known about how these components are encoded in the LH. Thus cynomolgus monkeys were conditioned with two distinct contexts, i.e., an appetitive context with available rewards and an aversive context with predicted air puffs. Different LH neuron groups encoded different degrees of expectation, predictability, and risks of rewards in a specific manner. A nearly equal number of one-third of the recorded LH neurons showed a positive or negative correlation between their response to visual conditioned stimuli (CS) that predicted the probabilistic delivery of rewards (0%, 50%, and 100%) and the associative values. For another one-third of recorded neurons, a nearly equal number showed a positive or negative correlation between their responses to rewards [appetitive unconditioned stimulus (US)] and reward predictability. Some neurons exhibited their highest or lowest trace-period responses in the 50% reward trials. These response modulations were represented independently and overlaid on a consistent excitatory or inhibitory response across the conditioning events. LH neurons also showed consistent responses in the aversive context. However, the responses to aversive conditioning events depending on the air puff value and predictability were less common. The multifaceted modulation of consistent activity related to outcome predictions may reflect motivational and arousal signals. Furthermore, it may underlie the role the LH plays in the integration and relay of signals to cortices for adaptive and goal-directed physiological and behavioral responses to environmental changes.

NEW & NOTEWORTHY The lateral hypothalamus (LH) is implicated in motivational and arousal behavior; however, the detailed information carried by single LH neurons remains unclear. We demonstrate that primate LH neurons encode multiple combinations of signals concerning different degrees of expectation, appreciation, and uncertainty of rewards in consistent responses across conditioning events and between different contexts. This multifaceted modulation of activity may underlie the role of the LH as a critical node integrating motivational signals with arousal signals.

Novelty, Salience, and Surprise Timing Are Signaled by Neurons in the Basal Forebrain

The basal forebrain (BF) is a principal source of modulation of the neocortex [1-6] and is thought to regulate cognitive functions such as attention, motivation, and learning by broadcasting information about salience [2, 3, 5, 7-19]. However, events can be salient for multiple reasons-such as novelty, surprise, or reward prediction errors [20-24]-and to date, precisely which salience-related information the BF broadcasts is unclear. Here, we report that the primate BF contains at least two types of neurons that often process salient events in distinct manners: one with phasic burst responses to cues predicting salient events and one with ramping activity anticipating such events. Bursting neurons respond to cues that convey predictions about the magnitude, probability, and timing of primary reinforcements. They also burst to the reinforcement itself, particularly when it is unexpected. However, they do not have a selective response to reinforcement omission (the unexpected absence of an event). Thus, bursting neurons do not convey value-prediction errors but do signal surprise associated with external events. Indeed, they are not limited to processing primary reinforcement: they discriminate fully expected novel visual objects from familiar objects and respond to object-sequence violations. In contrast, ramping neurons predict the timing of many salient, novel, and surprising events. Their ramping activity is highly sensitive to the subjects' confidence in event timing and on average encodes the subjects' surprise after unexpected events occur. These data suggest that the primate BF contains mechanisms to anticipate the timing of a diverse set of important external events (via ramping activity) and to rapidly deploy cognitive resources when these events occur (via short latency bursting).

Predictive coding of the statistical parameters of uncertain rewards by orbitofrontal neurons

Uncertain reward outcomes are characterised by statistical parameters that capture the numerical values of the underlying probability distributions of reward values, including the expected value, risk (variance) and probability. Here we show coding of an integrated expected value signal by single orbitofrontal neurons in response to visual cues predicting uncertain rewards. Separate subpopulations of orbitofrontal neurons predominantly code the prediction of one statistical parameter with few neurons showing combined coding. These signals are likely combined with subjective value signals to inform learning and decision making under conditions of uncertainty.

Neural responses to familiar conspecifics are modulated by a nonapeptide receptor in a winter flocking sparrow

The social behavior network, a collection of reciprocally connected areas within the basal forebrain and midbrain, plays a conserved role in the regulation of vertebrate social behavior. Specific behaviors are associated with patterns of activity across the network, and these activity profiles vary with species and context. We investigated how the social behavior network responds to familiar social stimuli in a seasonally flocking songbird. Further, we explored how socially-induced neural responses are modulated by endogenous nonapeptide receptor blockade. Winter flocking dark-eyed juncos were exposed to either familiar conspecifics or a familiar empty aviary following a peripheral injection of either saline or [desGly-NH₂,d(CH₂)₅, Tyr(Me)²,Thr⁴]-ornithine vasotocin, an VT3 receptor antagonist. Socially-exposed animals exhibited greater Fos induction across the social behavior network. Sex and drug effects were site-specific, with females tending to exhibit greater Fos responses to social stimuli and a greater sensitivity to VT3 antagonism. We suggest that in flocking animals, VT3 activation during social interaction may shift the pattern of neural activity towards the dorsocaudal lateral septum and rostral arcopallium and away from the extended amygdala, anterior and ventromedial hypothalamus, and the caudal ventral/ventrolateral lateral septum.

Post-decision wagering after perceptual judgments reveals bi-directional certainty readouts

Humans and other animals constantly evaluate their decisions in order to learn and behave adaptively. Experimentally, such evaluation processes are accessed using metacognitive reports made after decisions, typically using verbally formulated confidence scales. When subjects report high confidence, it reflects a high certainty of being correct, but a low confidence might signify either low certainty about the outcome, or a high certainty of being incorrect. Hence, metacognitive reports might reflect not only different levels of decision certainty, but also two certainty directions (certainty of being correct and certainty of being incorrect). It is important to test if such bi-directional processing can be measured because, for decision-making under uncertainty, information about being incorrect is as important as information about being correct for guidance of subsequent behavior. We were able to capture implicit bi-directional certainty readouts by asking subjects to bet money on their perceptual decision accuracy using a six-grade wager scale (post-decision wagering, PDW). To isolate trial-specific aspects of metacognitive judgments, we used pre-decision wagering (wagering before the perceptual decision) to subtract, from PDW trials, influences resulting from non-trial-specific assessment of expected difficulty and psychological biases. This novel design allowed independent quantification of certainty of being correct and certainty of being incorrect, showing that subjects were able to read out certainty in a bi-directional manner. Certainty readouts about being incorrect were particularly associated with metacognitive sensitivity exceeding perceptual sensitivity (i.e. meta-d' > d'), suggesting that such enhanced metacognitive efficiency is driven by information about incorrect decisions. Readouts of certainty in both directions increased on easier trials, and both certainty directions were also associated with faster metacognitive reaction times, indicating that certainty of being incorrect was not confounded with low certainty. Finally, both readouts influenced the amount of money subjects earned through PDW, suggesting that bi-directional readouts are important for planning future actions when feedback about previous decisions is unavailable.

Anterior cingulate is a source of valence-specific information about value and uncertainty

Anterior cingulate cortex (ACC) is thought to control a wide range of reward, punishment, and uncertainty-related behaviors. However, how it does so is unclear. Here, in a Pavlovian procedure in which monkeys displayed a diverse repertoire of reward-related, punishment-related, and uncertainty-related behaviors, we show that many ACC-neurons represent expected value and uncertainty in a valence-specific manner, signaling value or uncertainty predictions about either rewards or punishments. Other ACC-neurons signal prediction information about rewards and punishments by displaying excitation to both (rather than excitation to one and inhibition to the other). This diversity in valence representations may support the role of ACC in many behavioral states that are either enhanced by reward and punishment (e.g., vigilance) or specific to either reward or punishment (e.g., approach and avoidance). Also, this first demonstration of punishment-uncertainty signals in the brain suggests that ACC could be a target for the treatment of uncertainty-related disorders of mood.

Rewards or punishments elicit diverse behavioral responses; however, the neural circuits underlying such flexibility are unclear. Here Monosov shows that this diversity could be supported by neurons in the anterior cingulate that represent expected value and uncertainty in a valence-specific manner.

Multiple Mechanisms for Processing Reward Uncertainty in the Primate Basal Forebrain

The ability to use information about the uncertainty of future outcomes is critical for adaptive behavior in an uncertain world. We show that the basal forebrain (BF) contains at least two distinct neural-coding strategies to support this capacity. The dorsal-lateral BF, including the ventral pallidum (VP), contains reward-sensitive neurons, some of which are selectively suppressed by uncertain-reward predictions (U(-)). In contrast, the medial BF (mBF) contains reward-sensitive neurons, some of which are selectively enhanced (U(+)) by uncertain-reward predictions. In a two-alternative choice-task, U(-) neurons were selectively suppressed while monkeys chose uncertain options over certain options. During the same choice-epoch, U(+) neurons signaled the subjective reward value of the choice options. Additionally, after the choice was reported, U(+) neurons signaled reward uncertainty until the choice outcome. We suggest that uncertainty-related suppression of VP may participate in the mediation of uncertainty-seeking actions, whereas uncertainty-related enhancement of the mBF may direct cognitive resources to monitor and learn from uncertain-outcomes.

SIGNIFICANCE STATEMENT

To survive in an uncertain world, we must approach uncertainty and learn from it. Here we provide evidence for two mostly distinct mechanisms for processing uncertainty about rewards within different subregions of the primate basal forebrain (BF). We found that uncertainty suppressed the representation of certain (or safe) reward values by some neurons in the dorsal-lateral BF, in regions occupied by the ventral pallidum. This uncertainty-related suppression was evident as monkeys made risky choices. We also found that uncertainty-enhanced the activity of many medial BF neurons, most prominently after the monkeys' choices were completed (as they awaited uncertain outcomes). Based on these findings, we propose that different subregions of the BF could support action and learning under uncertainty in distinct but complimentary manners.

The Input-Output Relationship of the Cholinergic Basal Forebrain

Basal forebrain cholinergic neurons influence cortical state, plasticity, learning, and attention. They collectively innervate the entire cerebral cortex, differentially controlling acetylcholine efflux across different cortical areas and timescales. Such control might be achieved by differential inputs driving separable cholinergic outputs, although no input-output relationship on a brain-wide level has ever been demonstrated. Here, we identify input neurons to cholinergic cells projecting to specific cortical regions by infecting cholinergic axon terminals with a monosynaptically restricted viral tracer. This approach revealed several circuit motifs, such as central amygdala neurons synapsing onto basolateral amygdala-projecting cholinergic neurons or strong somatosensory cortical input to motor cortex-projecting cholinergic neurons. The presence of input cells in the parasympathetic midbrain nuclei contacting frontally projecting cholinergic neurons suggest that the network regulating the inner eye muscles are additionally regulating cortical state via acetylcholine efflux. This dataset enables future circuit-level experiments to identify drivers of known cortical cholinergic functions.

Neurons in the primate dorsal striatum signal the uncertainty of object-reward associations

To learn, obtain reward and survive, humans and other animals must monitor, approach and act on objects that are associated with variable or unknown rewards. However, the neuronal mechanisms that mediate behaviours aimed at uncertain objects are poorly understood. Here we demonstrate that a set of neurons in an internal-capsule bordering regions of the primate dorsal striatum, within the putamen and caudate nucleus, signal the uncertainty of object–reward associations. Their uncertainty responses depend on the presence of objects associated with reward uncertainty and evolve rapidly as monkeys learn novel object–reward associations. Therefore, beyond its established role in mediating actions aimed at known or certain rewards, the dorsal striatum also participates in behaviours aimed at reward-uncertain objects.

The dorsal striatum (DS) is a brain region that is thought to aim actions at certain or known rewards. Here, the authors show that an internal-capsule bordering region of the primate DS signals the uncertainty of object-reward associations, suggesting a novel role for the DS in behavior under uncertainty.

Ecological Origins of Object Salience: Reward, Uncertainty, Aversiveness, and Novelty

Among many objects around us, some are more salient than others (i.e., attract our attention automatically). Some objects may be inherently salient (e.g., brighter), while others may become salient by virtue of their ecological relevance through experience. However, the role of ecological experience in automatic attention has not been studied systematically. To address this question, we let subjects (macaque monkeys) view a large number of complex objects (>300), each experienced repeatedly (>5 days) with rewarding, aversive or no outcome association (mere-perceptual exposure). Test of salience was done on separate days using free viewing with no outcome. We found that gaze was biased among the objects from the outset, affecting saccades to objects or fixations within objects. When the outcome was rewarding, gaze preference was stronger (i.e., positive) for objects with larger or equal but uncertain rewards. The effects of aversive outcomes were variable. Gaze preference was positive for some outcome associations (e.g., airpuff), but negative for others (e.g., time-out), possibly due to differences in threat levels. Finally, novel objects attracted gaze, but mere perceptual exposure of objects reduced their salience (learned negative salience). Our results show that, in primates, object salience is strongly influenced by previous ecological experience and is supported by a large memory capacity. Owing to such high capacity for learned salience, the ability to rapidly choose important objects can grow during the entire life to promote biological fitness.

The modulation of savouring by prediction error and its effects on choice

When people anticipate uncertain future outcomes, they often prefer to know their fate in advance. Inspired by an idea in behavioral economics that the anticipation of rewards is itself attractive, we hypothesized that this preference of advance information arises because reward prediction errors carried by such information can boost the level of anticipation. We designed new empirical behavioral studies to test this proposal, and confirmed that subjects preferred advance reward information more strongly when they had to wait for rewards for a longer time. We formulated our proposal in a reinforcement-learning model, and we showed that our model could account for a wide range of existing neuronal and behavioral data, without appealing to ambiguous notions such as an explicit value for information. We suggest that such boosted anticipation significantly drives risk-seeking behaviors, most pertinently in gambling.

DOI:http://dx.doi.org/10.7554/eLife.13747.001

People, pigeons and monkeys often want to know in advance whether they will receive a reward in the future. This behaviour is irrational when individuals pay for costly information that makes no difference to an eventual outcome. One explanation is that individuals seek information because anticipating reward has hedonic value (it produces a feeling of pleasure). Consistent with this, pigeons are more likely to seek information when they have to wait longer for the potential reward. However, existing models cannot account for why this anticipation of rewards leads to irrational information-seeking.

In many situations, animals are uncertain about what is going to happen. Providing new information can produce a “prediction error” that indexes a discrepancy between what is expected and what actually happens. Iigaya et al. have now developed a mathematical model of information-seeking in which anticipation is boosted by this prediction error.

The model accounts for a wide range of previously unexplained data from monkeys and pigeons. It also successfully explains the behaviour of a group of human volunteers from whom Iigaya et al. elicited informational and actual decisions concerning uncertain and delayed rewards. The longer that the participants had to wait for possible rewards, the more avidly they wanted to find out about them. Further research is now needed to investigate the neural underpinnings of anticipation and its boosting by prediction errors.

DOI:http://dx.doi.org/10.7554/eLife.13747.002

Differential coding of uncertain reward in rat insular and orbitofrontal cortex

Anterior insular and orbitofrontal cortex (AIC and OFC, respectively) are known to play important roles in decision making under risk. However, risk-related AIC neural activity has not been investigated and it is controversial whether the rodent OFC conveys genuine risk signals. To address these issues, we examined AIC and OFC neuronal activity in rats responding to five distinct auditory cues predicting water reward with different probabilities. Both structures conveyed significant neural signals for reward, value and risk, with value and risk signals conjunctively coded. However, value signals were stronger and appeared earlier in the OFC, and many risk-coding OFC neurons responded only to the cue predicting certain (100%) reward. Also, AIC neurons tended to increase their activity for a prolonged time following a negative outcome and according to previously expected value. These results show that both the AIC and OFC convey neural signals related to reward uncertainty, but in different ways. The OFC might play an important role in encoding certain reward-biased, risk-modulated subjective value, whereas the AIC might convey prolonged negative outcome and disappointment signals.

The neural dynamics of reward value and risk coding in the human orbitofrontal cortex

The orbitofrontal cortex is known to carry information regarding expected reward, risk and experienced outcome. Yet, due to inherent limitations in lesion and neuroimaging methods, the neural dynamics of these computations has remained elusive in humans. Here, taking advantage of the high temporal definition of intracranial recordings, we characterize the neurophysiological signatures of the intact orbitofrontal cortex in processing information relevant for risky decisions. Local field potentials were recorded from the intact orbitofrontal cortex of patients suffering from drug-refractory partial epilepsy with implanted depth electrodes as they performed a probabilistic reward learning task that required them to associate visual cues with distinct reward probabilities. We observed three successive signals: (i) around 400 ms after cue presentation, the amplitudes of the local field potentials increased with reward probability; (ii) a risk signal emerged during the late phase of reward anticipation and during the outcome phase; and (iii) an experienced value signal appeared at the time of reward delivery. Both the medial and lateral orbitofrontal cortex encoded risk and reward probability while the lateral orbitofrontal cortex played a dominant role in coding experienced value. The present study provides the first evidence from intracranial recordings that the human orbitofrontal cortex codes reward risk both during late reward anticipation and during the outcome phase at a time scale of milliseconds. Our findings offer insights into the rapid mechanisms underlying the ability to learn structural relationships from the environment.

Neurons in the Primate Medial Basal Forebrain Signal Combined Information about Reward Uncertainty, Value, and Punishment Anticipation

It has been suggested that the basal forebrain (BF) exerts strong influences on the formation of memory and behavior. However, what information is used for the memory-behavior formation is unclear. We found that a population of neurons in the medial BF (medial septum and diagonal band of Broca) of macaque monkeys encodes a unique combination of information: reward uncertainty, expected reward value, anticipation of punishment, and unexpected reward and punishment. The results were obtained while the monkeys were expecting (often with uncertainty) a rewarding or punishing outcome during a Pavlovian procedure, or unexpectedly received an outcome outside the procedure. In vivo anterograde tracing using manganese-enhanced MRI suggested that the major recipient of these signals is the intermediate hippocampal formation. Based on these findings, we hypothesize that the medial BF identifies various contexts and outcomes that are critical for memory processing in the hippocampal formation.

Neuronal Reward and Decision Signals: From Theories to Data

Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.

Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans

The ability to evaluate variability in the environment is vital for making optimal behavioral decisions. Here we show that Caenorhabditis elegans evaluates variability in its food environment and modifies its future behavior accordingly. We derive a behavioral model that reveals a critical period over which information about the food environment is acquired and predicts future search behavior. We also identify a pair of high-threshold sensory neurons that encode variability in food concentration and the downstream dopamine-dependent circuit that generates appropriate search behavior upon removal from food. Further, we show that CREB is required in a subset of interneurons and determines the timescale over which the variability is integrated. Interestingly, the variability circuit is a subset of a larger circuit driving search behavior, showing that learning directly modifies the very same neurons driving behavior. Our study reveals how a neural circuit decodes environmental variability to generate contextually appropriate decisions.

Economic risk coding by single neurons in the orbitofrontal cortex

Risk is a ubiquitous feature of the environment for all organisms. Very few things in life are achieved with absolute certainty. Therefore, it is essential that organisms process risky information efficiently to promote adaptive behaviour and enhance survival. Here we outline a clear definition of economic risk derived from economic theory and focus on two experiments in which we have shown subpopulations of single neurons in the orbitofrontal cortex of rhesus macaques that code either economic risk per se or an error-related risk signal, namely a risk prediction error. These biological risk signals are essential for processing and updating risky information in the environment to contribute to efficient decision making and adaptive behaviour.

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.

Dopamine reward prediction error responses reflect marginal utility

BACKGROUND

Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity.

RESULTS

In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles.

CONCLUSIONS

These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility).

Social functions of individual vasopressin-oxytocin cell groups in vertebrates: what do we really know?

Vasopressin-oxytocin (VP-OT) nonapeptides modulate numerous social and stress-related behaviors, yet these peptides are made in multiple nuclei and brain regions (e.g., >20 in some mammals), and VP-OT cells in these areas often exhibit overlapping axonal projections. Furthermore, the magnocellular cell groups release peptide volumetrically from dendrites and soma, which gives rise to paracrine modulation in distal brain areas. Nonapeptide receptors also tend to be promiscuous. Hence, behavioral effects that are mediated by any given receptor type (e.g., the OT receptor) in a target brain region cannot be conclusively attributed to either VP or OT, nor to a specific cell group. We here review what is actually known about the social behavior functions of nonapeptide cell groups, with a focus on aggression, affiliation, bonding, social stress, and parental behavior, and discuss recent studies that demonstrate a diversity of sex-specific contributions of VP-OT cell groups to gregariousness and pair bonding.

Bayesian modeling of flexible cognitive control

"Cognitive control" describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation.

Reward value comparison via mutual inhibition in ventromedial prefrontal cortex

Recent theories suggest that reward-based choice reflects competition between value signals in the ventromedial prefrontal cortex (vmPFC). We tested this idea by recording vmPFC neurons while macaques performed a gambling task with asynchronous offer presentation. We found that neuronal activity shows four patterns consistent with selection via mutual inhibition: (1) correlated tuning for probability and reward size, suggesting that vmPFC carries an integrated value signal; (2) anti-correlated tuning curves for the two options, suggesting mutual inhibition; (3) neurons rapidly come to signal the value of the chosen offer, suggesting the circuit serves to produce a choice; and (4) after regressing out the effects of option values, firing rates still could predict choice-a choice probability signal. In addition, neurons signaled gamble outcomes, suggesting that vmPFC contributes to both monitoring and choice processes. These data suggest a possible mechanism for reward-based choice and endorse the centrality of vmPFC in that process.