Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys

A Comparison of Rapid Rule-Learning Strategies in Humans and Monkeys

Interspecies comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of the strategies of female macaque monkeys to male and female humans on a variant of the Wisconsin Card Sorting Test (WCST), a widely studied and applied task that provides a multiattribute measure of cognitive function and depends on the frontal lobe. WCST performance requires the inference of a rule change given ambiguous feedback. We found that well-trained monkeys infer new rules three times more slowly than minimally instructed humans. Input-dependent hidden Markov model-generalized linear models were fit to their choices, revealing hidden states akin to feature-based attention in both species. Decision processes resembled a win-stay, lose-shift strategy with interspecies similarities as well as key differences. Monkeys and humans both test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidate choice options. We quantitatively show that perseveration, random exploration, and poor sensitivity to negative feedback account for the slower task-switching performance in monkeys.

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.

Heart rate variability: A primer for alcohol researchers

Problem alcohol drinking remains a major cost and burden for society. Also, rates of problem drinking in women have dramatically increased in recent decades, and women are at risk for more alcohol problems and comorbidities. The purpose of this commentary is to discuss the potential utility of cardiac measures, including heart rate (HR) and HR variability (HRV), as markers of individual and sex differences in the drive to drink alcohol. We recently used cardiac telemetry in female and male adult rats to determine whether different cardiac markers, including HR and HRV, would differently predict alcohol and anxiety-like behavior across the sexes. Indeed, female behaviors related to HRV measures that indicate more parasympathetic (PNS) influence (the "rest and digest" system). In contrast, male behaviors are associated more with sympathetic (SNS) indicators (the activation system). Remarkably, similar sex differences in PNS versus SNS engagement under challenge are seen in human studies, suggesting strong cross-species convergence in differential autonomic regulation in females and males. Here, we describe the larger challenges that alcohol addiction presents, and how HRV measures may provide new biomarkers to help enhance development of more individualized and sex-specific treatments. We briefly explain the physiological systems underlying cardiac PNS and SNS states, and how specific HRV metrics are defined and validated, especially why particular HRV measures are considered to reflect more PNS versus SNS influence. Finally, we describe hormonal influences and sex differences in brain circuits related to cardiac autonomic regulation. Together, these findings show that HR and HRV have potential for uncovering key underlying mechanisms of sex and individual differences in autonomic drivers, which could guide more personalized treatment.

Human ventromedial prefrontal cortex is necessary for prosocial motivation

Ventromedial prefrontal cortex (vmPFC) is vital for decision-making. Functional neuroimaging links vmPFC to processing rewards and effort, while parallel work suggests vmPFC involvement in prosocial behaviour. However, the necessity of vmPFC for these functions is unknown. Patients with rare focal vmPFC lesions (n = 25), patients with lesions elsewhere (n = 15) and healthy controls (n = 40) chose between rest and exerting effort to earn rewards for themselves or another person. vmPFC damage decreased prosociality across behavioural and computational measures. vmPFC patients earned less, discounted rewards by effort more, and exerted less force when another person benefited, compared to both control groups. Voxel-based lesion mapping revealed dissociations between vmPFC subregions. While medial damage led to antisocial behaviour, lateral damage increased prosocial behaviour relative to patients with damage elsewhere. vmPFC patients also showed reduced effort sensitivity overall, but reward sensitivity was limited to specific subregions. These results reveal multiple causal contributions of vmPFC to prosocial behaviour, effort and reward.

Timing along the cardiac cycle modulates neural signals of reward-based learning

Natural fluctuations in cardiac activity modulate brain activity associated with sensory stimuli, as well as perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the relationship between fluctuations in heart activity and other internal representations. Here we investigate whether the cardiac cycle relates to learning-related internal representations - absolute and signed prediction errors. We combined machine learning techniques with electroencephalography with both simple, direct indices of task performance and computational model-derived indices of learning. Our results demonstrate that just as people are more sensitive to low magnitude, near-threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. However, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger differences in their prediction error representations between cardiac cycles exhibited higher learning rates and greater task accuracy.

Neural Activity in the Anterior Insula at Drinking Onset and Licking Relates to Compulsion-Like Alcohol Consumption

Much remains unknown about the etiology of compulsion-like alcohol drinking, where consumption persists despite adverse consequences. The role of the anterior insula (AIC) in emotion, motivation, and interoception makes this brain region a likely candidate to drive challenge-resistant behavior, including compulsive drinking. Indeed, subcortical projections from the AIC promote compulsion-like intake in rats and are recruited in heavy-drinking humans during compulsion for alcohol, highlighting the importance of and need for more information about AIC activity patterns that support aversion-resistant responding. Single-unit activity was recorded in the AIC from 15 male rats during alcohol-only and compulsion-like consumption. We found three sustained firing phenotypes, sustained-increase, sustained-decrease, and drinking-onset cells, as well as several firing patterns synchronized with licking. While many AIC neurons had session-long activity changes, only neurons with firing increases at drinking onset had greater activity under compulsion-like conditions. Further, only cells with persistent firing increases maintained activity during pauses in licking, suggesting roles in maintaining drive for alcohol during breaks. AIC firing was not elevated during saccharin drinking, similar to lack of effect of AIC inhibition on sweet fluid intake in many studies. In addition, we observed subsecond changes in AIC neural activity tightly entrained to licking. One lick-synched firing pattern (determined for all licks in a session) predicted compulsion-like drinking, while a separate lick-associated pattern correlated with greater consumption across alcohol intake conditions. Collectively, these data provide a more integrated model for the role of AIC firing in compulsion-like drinking, with important relevance for how the AIC promotes sustained motivated responding more generally.

Comparable roles for serotonin in rats and humans for computations underlying flexible decision-making

Serotonin is critical for adapting behavior flexibly to meet changing environmental demands. Cognitive flexibility is important for successful attainment of goals, as well as for social interactions, and is frequently impaired in neuropsychiatric disorders, including obsessive-compulsive disorder. However, a unifying mechanistic framework accounting for the role of serotonin in behavioral flexibility has remained elusive. Here, we demonstrate common effects of manipulating serotonin function across two species (rats and humans) on latent processes supporting choice behavior during probabilistic reversal learning, using computational modelling. The findings support a role of serotonin in behavioral flexibility and plasticity, indicated, respectively, by increases or decreases in choice repetition ('stickiness') or reinforcement learning rates following manipulations intended to increase or decrease serotonin function. More specifically, the rate at which expected value increased following reward and decreased following punishment (reward and punishment 'learning rates') was greatest after sub-chronic administration of the selective serotonin reuptake inhibitor (SSRI) citalopram (5 mg/kg for 7 days followed by 10 mg/kg twice a day for 5 days) in rats. Conversely, humans given a single dose of an SSRI (20 mg escitalopram), which can decrease post-synaptic serotonin signalling, and rats that received the neurotoxin 5,7-dihydroxytryptamine (5,7-DHT), which destroys forebrain serotonergic neurons, exhibited decreased reward learning rates. A basic perseverative tendency ('stickiness'), or choice repetition irrespective of the outcome produced, was likewise increased in rats after the 12-day SSRI regimen and decreased after single dose SSRI in humans and 5,7-DHT in rats. These common effects of serotonergic manipulations on rats and humans-identified via computational modelling-suggest an evolutionarily conserved role for serotonin in plasticity and behavioral flexibility and have clinical relevance transdiagnostically for neuropsychiatric disorders.

Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex

The signed value and unsigned salience of reward prediction errors (RPEs) are critical to understanding reinforcement learning (RL) and cognitive control. Dorsomedial prefrontal cortex (dMPFC) and insula (INS) are key regions for integrating reward and surprise information, but conflicting evidence for both signed and unsigned activity has led to multiple proposals for the nature of RPE representations in these brain areas. Recently developed RL models allow neurons to respond differently to positive and negative RPEs. Here, we use intracranially recorded high frequency activity (HFA) to test whether this flexible asymmetric coding strategy captures RPE coding diversity in human INS and dMPFC. At the region level, we found a bias towards positive RPEs in both areas which paralleled behavioral adaptation. At the local level, we found spatially interleaved neural populations responding to unsigned RPE salience and valence-specific positive and negative RPEs. Furthermore, directional connectivity estimates revealed a leading role of INS in communicating positive and unsigned RPEs to dMPFC. These findings support asymmetric coding across distinct but intermingled neural populations as a core principle of RPE processing and inform theories of the role of dMPFC and INS in RL and cognitive control.

Beta and theta oscillations track effort and previous reward in human basal ganglia and prefrontal cortex during decision making

Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we investigate this with chronic intracranial recordings from prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinson's disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12-20 Hz) oscillations tracking subjective effort on a single trial basis and PFC theta (4-7 Hz) signaling previous trial reward. Stimulation of PFC increased overall acceptance of offers in addition to increasing the impact of reward on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, as well as supporting a causal role of PFC for such choices.

Understanding patch foraging strategies across development

Patch foraging is a near-ubiquitous behaviour across the animal kingdom and characterises many decision-making domains encountered by humans. We review how a disposition to explore in adolescence may reflect the evolutionary conditions under which hunter-gatherers foraged for resources. We propose that neurocomputational mechanisms responsible for reward processing, learning, and cognitive control facilitate the transition from exploratory strategies in adolescence to exploitative strategies in adulthood - where individuals capitalise on known resources. This developmental transition may be disrupted by psychopathology, as there is emerging evidence of biases in explore/exploit choices in mental health problems. Explore/exploit choices may be an informative marker for mental health across development and future research should consider this feature of decision-making as a target for clinical intervention.

Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable

Theoretical and empirical accounts suggest that adolescence is associated with heightened reward learning and impulsivity. Experimental tasks and computational models that can dissociate reward learning from the tendency to initiate actions impulsively (action initiation bias) are thus critical to characterise the mechanisms that drive developmental differences. However, existing work has rarely quantified both learning ability and action initiation, or it has relied on small samples. Here, using computational modelling of a learning task collected from a large sample (N = 742, 9-18 years, 11 countries), we test differences in reward and punishment learning and action initiation from childhood to adolescence. Computational modelling reveals that whilst punishment learning rates increase with age, reward learning remains stable. In parallel, action initiation biases decrease with age. Results are similar when considering pubertal stage instead of chronological age. We conclude that heightened reward responsivity in adolescence can reflect differences in action initiation rather than enhanced reward learning.

Using lickometry to infer differential contributions of salience network regions during compulsion-like alcohol drinking

Alcohol use disorder extracts substantial personal, social and clinical costs, and continued intake despite negative consequences (compulsion-like consumption) can contribute strongly. Here we discuss lickometry, a simple method where lick times are determined across a session, while analysis across many aspects of licking can offer important insights into underlying psychological and action strategies, including their brain mechanisms. We first describe studies implicating anterior insula (AIC) and dorsal medial prefrontal cortex (dMPF) in compulsion-like responding for alcohol, then review work suggesting that AIC/ventral frontal cortex versus dMPF regulate different aspects of behavior (oral control and overall response strategy, versus moment-to-moment action organization). We then detail our lickometer work comparing alcohol-only drinking (AOD) and compulsion-like drinking under moderate- or higher-challenge (ModChD or HiChD, using quinine-alcohol). Many studies have suggested utilization of one of two main strategies, with higher motivation indicated by more bouts, and greater palatability suggested by longer, faster bouts. Instead, ModChD shows decreased variability in many lick measures, which is unexpected but consistent with the suggested importance of automaticity for addiction. Also surprising is that HiChD retains several behavior changes seen with ModChD, reduced tongue variability and earlier bout start, even though intake is otherwise disrupted. Since AIC-related measures are retained under both moderate- and higher-challenge, we propose a novel hypothesis that AIC sustains overall commitment regardless of challenge level, while disordered licking during HiChD mirrors the effects of dMPF inhibition. Thus, while AIC provides overall drive despite challenge, the ability to act is ultimately determined within the dMPF.

Dorsal raphe neurons signal integrated value during 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 grappling with reward uncertainty. However, whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider all these attributes to make a choice, is unclear. 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. Remarkably, these neurons commonly integrated offer attributes in a manner that reflected monkeys' overall 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 DRN participates in integrated value computations, guiding theories of DRN in decision-making and psychiatric disease.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

l-DOPA and oxytocin influence the neurocomputational mechanisms of self-benefitting and prosocial reinforcement learning

Humans learn through reinforcement, particularly when outcomes are unexpected. Recent research suggests similar mechanisms drive how we learn to benefit other people, that is, how we learn to be prosocial. Yet the neurochemical mechanisms underlying such prosocial computations remain poorly understood. Here, we investigated whether pharmacological manipulation of oxytocin and dopamine influence the neurocomputational mechanisms underlying self-benefitting and prosocial reinforcement learning. Using a double-blind placebo-controlled cross-over design, we administered intranasal oxytocin (24 IU), dopamine precursor l-DOPA (100 mg + 25 mg carbidopa), or placebo over three sessions. Participants performed a probabilistic reinforcement learning task with potential rewards for themselves, another participant, or no one, during functional magnetic resonance imaging. Computational models of reinforcement learning were used to calculate prediction errors (PEs) and learning rates. Participants behavior was best explained by a model with different learning rates for each recipient, but these were unaffected by either drug. On the neural level, however, both drugs blunted PE signaling in the ventral striatum and led to negative signaling of PEs in the anterior mid-cingulate cortex, dorsolateral prefrontal cortex, inferior parietal gyrus, and precentral gyrus, compared to placebo, and regardless of recipient. Oxytocin (versus placebo) administration was additionally associated with opposing tracking of self-benefitting versus prosocial PEs in dorsal anterior cingulate cortex, insula and superior temporal gyrus. These findings suggest that both l-DOPA and oxytocin induce a context-independent shift from positive towards negative tracking of PEs during learning. Moreover, oxytocin may have opposing effects on PE signaling when learning to benefit oneself versus another.

Nutrient-Sensitive Reinforcement Learning in Monkeys

In reinforcement learning (RL), animals choose by assigning values to options and learn by updating these values from reward outcomes. This framework has been instrumental in identifying fundamental learning variables and their neuronal implementations. However, canonical RL models do not explain how reward values are constructed from biologically critical intrinsic reward components, such as nutrients. From an ecological perspective, animals should adapt their foraging choices in dynamic environments to acquire nutrients that are essential for survival. Here, to advance the biological and ecological validity of RL models, we investigated how (male) monkeys adapt their choices to obtain preferred nutrient rewards under varying reward probabilities. We found that the nutrient composition of rewards strongly influenced learning and choices. Preferences of the animals for specific nutrients (sugar, fat) affected how they adapted to changing reward probabilities; the history of recent rewards influenced choices of the monkeys more strongly if these rewards contained the their preferred nutrients (nutrient-specific reward history). The monkeys also chose preferred nutrients even when they were associated with lower reward probability. A nutrient-sensitive RL model captured these processes; it updated the values of individual sugar and fat components of expected rewards based on experience and integrated them into subjective values that explained the choices of the monkeys. Nutrient-specific reward prediction errors guided this value-updating process. Our results identify nutrients as important reward components that guide learning and choice by influencing the subjective value of choice options. Extending RL models with nutrient-value functions may enhance their biological validity and uncover nutrient-specific learning and decision variables.SIGNIFICANCE STATEMENT RL is an influential framework that formalizes how animals learn from experienced rewards. Although reward is a foundational concept in RL theory, canonical RL models cannot explain how learning depends on specific reward properties, such as nutrients. Intuitively, learning should be sensitive to the nutrient components of the reward to benefit health and survival. Here, we show that the nutrient (fat, sugar) composition of rewards affects how the monkeys choose and learn in an RL paradigm and that key learning variables including reward history and reward prediction error should be modified with nutrient-specific components to account for the choice behavior observed in the monkeys. By incorporating biologically critical nutrient rewards into the RL framework, our findings help advance the ecological validity of RL models.

Local and global reward learning in the lateral frontal cortex show differential development during human adolescence

Reward-guided choice is fundamental for adaptive behaviour and depends on several component processes supported by prefrontal cortex. Here, across three studies, we show that two such component processes, linking reward to specific choices and estimating the global reward state, develop during human adolescence and are linked to the lateral portions of the prefrontal cortex. These processes reflect the assignment of rewards contingently to local choices, or noncontingently, to choices that make up the global reward history. Using matched experimental tasks and analysis platforms, we show the influence of both mechanisms increase during adolescence (study 1) and that lesions to lateral frontal cortex (that included and/or disconnected both orbitofrontal and insula cortex) in human adult patients (study 2) and macaque monkeys (study 3) impair both local and global reward learning. Developmental effects were distinguishable from the influence of a decision bias on choice behaviour, known to depend on medial prefrontal cortex. Differences in local and global assignments of reward to choices across adolescence, in the context of delayed grey matter maturation of the lateral orbitofrontal and anterior insula cortex, may underlie changes in adaptive behaviour.

Neural activity tracking identity and confidence in social information

Humans learn about the environment either directly by interacting with it or indirectly by seeking information about it from social sources such as conspecifics. The degree of confidence in the information obtained through either route should determine the impact that it has on adapting and changing behaviour. We examined whether and how behavioural and neural computations differ during non-social learning as opposed to learning from social sources. Trial-wise confidence judgements about non-social and social information sources offered a window into this learning process. Despite matching exactly the statistical features of social and non-social conditions, confidence judgements were more accurate and less changeable when they were made about social as opposed to non-social information sources. In addition to subjective reports of confidence, differences were also apparent in the Bayesian estimates of participants' subjective beliefs. Univariate activity in dorsomedial prefrontal cortex and posterior temporoparietal junction more closely tracked confidence about social as opposed to non-social information sources. In addition, the multivariate patterns of activity in the same areas encoded identities of social information sources compared to non-social information sources.

Relationship between nuclei-specific amygdala connectivity and mental health dimensions in humans

There has been increasing interest in using neuroimaging measures to predict psychiatric disorders. However, predictions usually rely on large brain networks and large disorder heterogeneity. Thus, they lack both anatomical and behavioural specificity, preventing the advancement of targeted interventions. Here we address both challenges. First, using resting-state functional magnetic resonance imaging, we parcellated the amygdala, a region implicated in mood disorders, into seven nuclei. Next, a questionnaire factor analysis provided subclinical mental health dimensions frequently altered in anxious-depressive individuals, such as negative emotions and sleep problems. Finally, for each behavioural dimension, we identified the most predictive resting-state functional connectivity between individual amygdala nuclei and highly specific regions of interest, such as the dorsal raphe nucleus in the brainstem or medial frontal cortical regions. Connectivity in circumscribed amygdala networks predicted behaviours in an independent dataset. Our results reveal specific relations between mental health dimensions and connectivity in precise subcortical networks.

Medial and orbital frontal cortex in decision-making and flexible behavior

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.

Flexible auditory training, psychophysics, and enrichment of common marmosets with an automated, touchscreen-based system

Devising new and more efficient protocols to analyze the phenotypes of non-human primates, as well as their complex nervous systems, is rapidly becoming of paramount importance. This is because with genome-editing techniques, recently adopted to non-human primates, new animal models for fundamental and translational research have been established. One aspect in particular, namely cognitive hearing, has been difficult to assess compared to visual cognition. To address this, we devised autonomous, standardized, and unsupervised training and testing of auditory capabilities of common marmosets with a cage-based standalone, wireless system. All marmosets tested voluntarily operated the device on a daily basis and went from naïve to experienced at their own pace and with ease. Through a series of experiments, here we show, that animals autonomously learn to associate sounds with images; to flexibly discriminate sounds, and to detect sounds of varying loudness. The developed platform and training principles combine in-cage training of common marmosets for cognitive and psychoacoustic assessment with an enriched environment that does not rely on dietary restriction or social separation, in compliance with the 3Rs principle.

The authors present a cage-based stand-alone platform for autonomous, standardized, and unsupervised training and testing of visuo-auditory-cued behaviours of common marmosets. The experiments do not require dietary restriction or social separation.

Complementary roles of serotonergic and cholinergic systems in decisions about when to act

Decision-making not only involves deciding about which action to choose but when and whether to initiate an action in the first place. Macaque monkeys tracked number of dots on a screen and could choose when to make a response. The longer the animals waited before responding, the more dots appeared on the screen and the higher the probability of reward. Monkeys waited longer before making a response when a trial’s value was less than the environment’s average value. Recordings of brain activity with fMRI revealed that activity in dorsal raphe nucleus (DRN)—a key source of serotonin (5-HT)—tracked average value of the environment. By contrast, activity in the basal forebrain (BF)—an important source of acetylcholine (ACh)—was related to decision time to act as a function of immediate and recent past context. Interactions between DRN and BF and the anterior cingulate cortex (ACC), another region with action initiation-related activity, occurred as a function of the decision time to act. Next, we performed two psychopharmacological studies. Manipulating systemic 5-HT by citalopram prolonged the time macaques waited to respond for a given opportunity. This effect was more evident during blocks with long inter-trial intervals (ITIs) where good opportunities were sparse. Manipulating systemic acetylcholine (ACh) by rivastigmine reduced the time macaques waited to respond given the immediate and recent past context, a pattern opposite to the effect observed with 5-HT. These findings suggest complementary roles for serotonin/DRN and acetylcholine/BF in decisions about when to initiate an action.

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Both immediate context and wider environment influence decisions about when to act

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DRN and 5-HT mediate the influence of wider environment

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BF and ACh mediate the influence of immediate context

Serotonin neurons modulate learning rate through uncertainty

Regulating how fast to learn is critical for flexible behavior. Learning about the consequences of actions should be slow in stable environments, but accelerate when that environment changes. Recognizing stability and detecting change are difficult in environments with noisy relationships between actions and outcomes. Under these conditions, theories propose that uncertainty can be used to modulate learning rates ("meta-learning"). We show that mice behaving in a dynamic foraging task exhibit choice behavior that varied as a function of two forms of uncertainty estimated from a meta-learning model. The activity of dorsal raphe serotonin neurons tracked both types of uncertainty in the foraging task as well as in a dynamic Pavlovian task. Reversible inhibition of serotonin neurons in the foraging task reproduced changes in learning predicted by a simulated lesion of meta-learning in the model. We thus provide a quantitative link between serotonin neuron activity, learning, and decision making.

Neuromodulation of prefrontal cortex cognitive function in primates: the powerful roles of monoamines and acetylcholine

The primate prefrontal cortex (PFC) subserves our highest order cognitive operations, and yet is tremendously dependent on a precise neurochemical environment for proper functioning. Depletion of noradrenaline and dopamine, or of acetylcholine from the dorsolateral PFC (dlPFC), is as devastating as removing the cortex itself, and serotonergic influences are also critical to proper functioning of the orbital and medial PFC. Most neuromodulators have a narrow inverted U dose response, which coordinates arousal state with cognitive state, and contributes to cognitive deficits with fatigue or uncontrollable stress. Studies in monkeys have revealed the molecular signaling mechanisms that govern the generation and modulation of mental representations by the dlPFC, allowing dynamic regulation of network strength, a process that requires tight regulation to prevent toxic actions, e.g., as occurs with advanced age. Brain imaging studies in humans have observed drug and genotype influences on a range of cognitive tasks and on PFC circuit functional connectivity, e.g., showing that catecholamines stabilize representations in a baseline-dependent manner. Research in monkeys has already led to new treatments for cognitive disorders in humans, encouraging future research in this important field.

Interactions between ventrolateral prefrontal and anterior cingulate cortex during learning and behavioural change

Hypotheses and beliefs guide credit assignment - the process of determining which previous events or actions caused an outcome. Adaptive hypothesis formation and testing are crucial in uncertain and changing environments in which associations and meanings are volatile. Despite primates' abilities to form and test hypotheses, establishing what is causally responsible for the occurrence of particular outcomes remains a fundamental challenge for credit assignment and learning. Hypotheses about what surprises are due to stochasticity inherent in an environment as opposed to real, systematic changes are necessary for identifying the environment's predictive features, but are often hard to test. We review evidence that two highly interconnected frontal cortical regions, anterior cingulate cortex and ventrolateral prefrontal area 47/12o, provide a biological substrate for linking two crucial components of hypothesis-formation and testing: the control of information seeking and credit assignment. Neuroimaging, targeted disruptions, and neurophysiological studies link an anterior cingulate - 47/12o circuit to generation of exploratory behaviour, non-instrumental information seeking, and interpretation of subsequent feedback in the service of credit assignment. Our observations support the idea that information seeking and credit assignment are linked at the level of neural circuits and explain why this circuit is important for ensuring behaviour is flexible and adaptive.

Long term study of motivational and cognitive effects of low-intensity focused ultrasound neuromodulation in the dorsal striatum of nonhuman primates

Noninvasive brain stimulation using transcranial focused ultrasound (FUS) has many potential applications as a research and clinical tool, including incorporation into neural prosthetics for cognitive rehabilitation. To develop this technology, it is necessary to evaluate the safety and efficacy of FUS neuromodulation for specific brain targets and cognitive functions. It is also important to test whether repeated long-term application of FUS to deep brain targets improves or degrades behavioral and cognitive function. To this end, we investigated the effects of FUS in the dorsal striatum of nonhuman primates (NHP) performing a visual-motor decision-making task for small or large rewards. Over the course of 2 years, we performed 129 and 147 FUS applications, respectively, in two NHP. FUS (0.5 MHz @ 0.2-0.8 MPa) was applied to the putamen and caudate in both hemispheres to evaluate the effects on movement accuracy, motivation, decision accuracy, and response time. Sonicating the caudate or the putamen unilaterally resulted in modest but statistically significant improvements in motivation and decision accuracy, but at the cost of slower reaction times. The effects were dose (i.e., FUS pressure) and reward dependent. There was no effect on reaching accuracy, nor was there long-term behavioral impairment or neurological trauma evident on T1-weighted, T2-weighted, or susceptibility-weighted MRI scans. Sonication also resulted in significant changes in resting state functional connectivity between the caudate and multiple cortical regions. The results indicate that applying FUS to the dorsal striatum can positively impact the motivational and cognitive aspects of decision making. The capability of FUS to improve motivation and cognition in NHPs points to its therapeutic potential in treating a wide variety of human neural diseases, and warrants further development as a novel technique for non-invasive deep brain stimulation.

Computational models of adaptive behavior and prefrontal cortex

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

Ultrasound modulation of macaque prefrontal cortex selectively alters credit assignment-related activity and behavior

Credit assignment is the association of specific instances of reward to the specific events, such as a particular choice, that caused them. Without credit assignment, choice values reflect an approximate estimate of how good the environment was when the choice was made—the global reward state—rather than exactly which outcome the choice caused. Combined transcranial ultrasound stimulation (TUS) and functional magnetic resonance imaging in macaques demonstrate credit assignment–related activity in prefrontal area 47/12o, and when this signal was disrupted with TUS, choice value representations across the brain were impaired. As a consequence, behavior was no longer guided by choice value, and decision-making was poorer. By contrast, global reward state–related activity in the adjacent anterior insula remained intact and determined decision-making after prefrontal disruption.

Entropy-based metrics for predicting choice behavior based on local response to reward

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

Better living through understanding the insula: Why subregions can make all the difference

Insula function is considered critical for many motivated behaviors, with proposed functions ranging from attention, behavioral control, emotional regulation, goal-directed and aversion-resistant responding. Further, the insula is implicated in many neuropsychiatric conditions including substance abuse. More recently, multiple insula subregions have been distinguished based on anatomy, connectivity, and functional contributions. Generally, posterior insula is thought to encode more somatosensory inputs, which integrate with limbic/emotional information in middle insula, that in turn integrate with cognitive processes in anterior insula. Together, these regions provide rapid interoceptive information about the current or predicted situation, facilitating autonomic recruitment and quick, flexible action. Here, we seek to create a robust foundation from which to understand potential subregion differences, and provide direction for future studies. We address subregion differences across humans and rodents, so that the latter's mechanistic interventions can best mesh with clinical relevance of human conditions. We first consider the insula's suggested roles in humans, then compare subregional studies, and finally describe rodent work. One primary goal is to encourage precision in describing insula subregions, since imprecision (e.g. including both posterior and anterior studies when describing insula work) does a disservice to a larger understanding of insula contributions. Additionally, we note that specific task details can greatly impact recruitment of various subregions, requiring care and nuance in design and interpretation of studies. Nonetheless, the central ethological importance of the insula makes continued research to uncover mechanistic, mood, and behavioral contributions of paramount importance and interest. This article is part of the special Issue on 'Neurocircuitry Modulating Drug and Alcohol Abuse'.

Choice history effects in mice and humans improve reward harvesting efficiency

Choice history effects describe how future choices depend on the history of past choices. In experimental tasks this is typically framed as a bias because it often diminishes the experienced reward rates. However, in natural habitats, choices made in the past constrain choices that can be made in the future. For foraging animals, the probability of earning a reward in a given patch depends on the degree to which the animals have exploited the patch in the past. One problem with many experimental tasks that show choice history effects is that such tasks artificially decouple choice history from its consequences on reward availability over time. To circumvent this, we use a variable interval (VI) reward schedule that reinstates a more natural contingency between past choices and future reward availability. By examining the behavior of optimal agents in the VI task we discover that choice history effects observed in animals serve to maximize reward harvesting efficiency. We further distil the function of choice history effects by manipulating first- and second-order statistics of the environment. We find that choice history effects primarily reflect the growth rate of the reward probability of the unchosen option, whereas reward history effects primarily reflect environmental volatility. Based on observed choice history effects in animals, we develop a reinforcement learning model that explicitly incorporates choice history over multiple time scales into the decision process, and we assess its predictive adequacy in accounting for the associated behavior. We show that this new variant, known as the double trace model, has a higher performance in predicting choice data, and shows near optimal reward harvesting efficiency in simulated environments. These results suggests that choice history effects may be adaptive for natural contingencies between consumption and reward availability. This concept lends credence to a normative account of choice history effects that extends beyond its description as a bias.

Animals foraging for food in natural habitats compete to obtain better quality food patches. To achieve this goal, animals can rely on memory and choose the same patches that have provided higher quality of food in the past. However, in natural habitats simply identifying better food patches may not be sufficient to successfully compete with their conspecifics, as food resources can grow over time. Therefore, it makes sense to visit from time to time those patches that were associated with lower food quality in the past. This demands optimal foraging animals to keep in memory not only which food patches provided the best food quality, but also which food patches they visited recently. To see if animals track their history of visits and use it to maximize the food harvesting efficiency, we subjected them to experimental conditions that mimicked natural foraging behavior. In our behavioral tasks, we replaced food foraging behavior with a two choice task that provided rewards to mice and humans. By developing a new computational model and subjecting animals to various behavioral manipulations, we demonstrate that keeping a memory of past visits helps the animals to optimize the efficiency with which they can harvest rewards.

Neuronal Circuits for Social Decision-Making and Their Clinical Implications

Social living facilitates individual access to rewards, cognitive resources, and objects that would not be otherwise accessible. There are, however, some drawbacks to social living, particularly when competing for scarce resources. Furthermore, variability in our ability to make social decisions can be associated with neuropsychiatric disorders. The neuronal mechanisms underlying social decision-making are beginning to be understood. The momentum to study this phenomenon has been partially carried over by the study of economic decision-making. Yet, because of the similarities between these different types of decision-making, it is unclear what is a social decision. Here, we propose a definition of social decision-making as choices taken in a context where one or more conspecifics are involved in the decision or the consequences of it. Social decisions can be conceptualized as complex economic decisions since they are based on the subjective preferences between different goods. During social decisions, individuals choose based on their internal value estimate of the different alternatives. These are complex decisions given that conspecifics beliefs or actions could modify the subject's internal valuations at every choice. Here, we first review recent developments in our collective understanding of the neuronal mechanisms and circuits of social decision-making in primates. We then review literature characterizing populations with neuropsychiatric disorders showing deficits in social decision-making and the underlying neuronal circuitries associated with these deficits.

Common functional localizers to enhance NHP & cross-species neuroscience imaging research

Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.

Ageing is associated with disrupted reinforcement learning whilst learning to help others is preserved

Reinforcement learning is a fundamental mechanism displayed by many species. However, adaptive behaviour depends not only on learning about actions and outcomes that affect ourselves, but also those that affect others. Using computational reinforcement learning models, we tested whether young (age 18-36) and older (age 60-80, total n = 152) adults learn to gain rewards for themselves, another person (prosocial), or neither individual (control). Detailed model comparison showed that a model with separate learning rates for each recipient best explained behaviour. Young adults learned faster when their actions benefitted themselves, compared to others. Compared to young adults, older adults showed reduced self-relevant learning rates but preserved prosocial learning. Moreover, levels of subclinical self-reported psychopathic traits (including lack of concern for others) were lower in older adults and the core affective-interpersonal component of this measure negatively correlated with prosocial learning. These findings suggest learning to benefit others is preserved across the lifespan with implications for reinforcement learning and theories of healthy ageing.

Causal manipulation of self-other mergence in the dorsomedial prefrontal cortex

To navigate social environments, people must simultaneously hold representations about their own and others’ abilities. During self-other mergence, people estimate others’ abilities not only on the basis of the others’ past performance, but the estimates are also influenced by their own performance. For example, if we perform well, we overestimate the abilities of those with whom we are co-operating and underestimate competitors. Self-other mergence is associated with specific activity patterns in the dorsomedial prefrontal cortex (dmPFC). Using a combination of non-invasive brain stimulation, functional magnetic resonance imaging, and computational modeling, we show that dmPFC neurostimulation silences these neural signatures of self-other mergence in relation to estimation of others’ abilities. In consequence, self-other mergence behavior increases, and our assessments of our own performance are projected increasingly onto other people. This suggests an inherent tendency to form interdependent social representations and a causal role of the dmPFC in separating self and other representations.

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During self-other mergence (SOM), people confuse one’s own with another’s performance

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Brain stimulation over dorsomedial prefrontal cortex (dmPFC) alters neural SOM

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Brain stimulation over dmPFC simultaneously alters behavioral SOM

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This suggests a causal role of dmPFC in separating self and other representations

Prior Cocaine Exposure Increases Firing to Immediate Reward While Attenuating Cue and Context Signals Related to Reward Value in the Insula

The insula contributes to behavioral control and is disrupted by substance abuse, yet we know little about the neural signals underlying these functions or how they are disrupted after chronic drug self-administration. Here, male and female rats self-administered either cocaine (experimental group) or sucrose (control) for 12 consecutive days. After a 1 month withdrawal period, we recorded from insula while rats performed a previously learned reward-guided decision-making task. Cocaine-exposed rats were more sensitive to value manipulations and were faster to respond. These behavioral changes were accompanied by elevated counts of neurons in the insula that increased firing to reward. These neurons also fired more strongly at the start of long-delay trials, when a more immediate reward would be expected, and fired less strongly in anticipation of the actual delivery of delayed rewards. Although reward-related firing to immediate reward was enhanced after cocaine self-administration, reward-predicting cue and context signals were attenuated. In addition to revealing novel firing patterns unique to insula, our data suggest changes in such neural activity likely contribute to impaired decision making observed after drug use.SIGNIFICANCE STATEMENT The insula plays a clear role in drug addiction and drug-induced impairments of decision making, yet there is little understanding of its underlying neural signals. We found that chronic cocaine self-administration reduces cue and context encoding in insula while enhancing signals related to immediate reward. These changes in neural activity likely contribute to impaired decision making and impulsivity observed after drug use.

Defining an orbitofrontal compass: Functional and anatomical heterogeneity across anterior-posterior and medial-lateral axes

The orbitofrontal cortex (OFC) plays a critical role in the flexible control of behaviors and has been the focus of increasing research interest. However, there have been a number of controversies around the exact theoretical role of the OFC. One potential source of these issues is the comparison of evidence from different studies, particularly across species, which focus on different specific sub-regions within the OFC. Furthermore, there is emerging evidence that there may be functional diversity across the OFC which may account for these theoretical differences. Therefore, in this review we consider evidence supporting functional heterogeneity within the OFC and how it relates to underlying anatomical heterogeneity. We highlight the importance of anatomical and functional distinctions within the traditionally defined OFC subregions across the medial-lateral axis, which are often not differentiated for practical and historical reasons. We then consider emerging evidence of even finer-grained distinctions within these defined subregions along the anterior-posterior axis. These fine-grained anatomical considerations reveal a pattern of dissociable, but often complementary functions within the OFC. (PsycInfo Database Record (c) 2021 APA, all rights reserved).