Specific frontal neural dynamics contribute to decisions to check

Dissociable Representations of Decision Variables within Subdivisions of the Macaque Orbital and Ventrolateral Frontal Cortex

The ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subdivisions of VFC in male macaque monkeys performing a two-choice probabilistic task for different fruit juice outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral Area 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within Area 12o, in contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal Area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subdivisions, while the properties of the reward, such as its flavor, were more strongly represented in Areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Preferences reveal dissociable encoding across prefrontal-limbic circuits

Individual preferences for the flavor of different foods and fluids exert a strong influence on behavior. Most current theories posit that preferences are integrated with other state variables in the orbitofrontal cortex (OFC), which is thought to derive the relative subjective value of available options to guide choice behavior. Here, we report that instead of a single integrated valuation system in the OFC, another complementary one is centered in the ventrolateral prefrontal cortex (vlPFC) in macaques. Specifically, we found that the OFC and vlPFC preferentially represent outcome flavor and outcome probability, respectively, and that preferences are separately integrated into value representations in these areas. In addition, the vlPFC, but not the OFC, represented the probability of receiving the available outcome flavors separately, with the difference between these representations reflecting the degree of preference for each flavor. Thus, both the vlPFC and OFC exhibit dissociable but complementary representations of subjective value, both of which are necessary for decision-making.

Goal commitment is supported by vmPFC through selective attention

When striking a balance between commitment to a goal and flexibility in the face of better options, people often demonstrate strong goal perseveration. Here, using functional MRI (n = 30) and lesion patient (n = 26) studies, we argue that the ventromedial prefrontal cortex (vmPFC) drives goal commitment linked to changes in goal-directed selective attention. Participants performed an incremental goal pursuit task involving sequential decisions between persisting with a goal versus abandoning progress for better alternative options. Individuals with stronger goal perseveration showed higher goal-directed attention in an interleaved attention task. Increasing goal-directed attention also affected abandonment decisions: while pursuing a goal, people lost their sensitivity to valuable alternative goals while remaining more sensitive to changes in the current goal. In a healthy population, individual differences in both commitment biases and goal-oriented attention were predicted by baseline goal-related activity in the vmPFC. Among lesion patients, vmPFC damage reduced goal commitment, leading to a performance benefit.

General mechanisms of task engagement in the primate frontal cortex

Staying engaged is necessary to maintain goal-directed behaviors. Despite this, engagement exhibits continuous, intrinsic fluctuations. Even in experimental settings, animals, unlike most humans, repeatedly and spontaneously move between periods of complete task engagement and disengagement. We, therefore, looked at behavior in male macaques (macaca mulatta) in four tasks while recording fMRI signals. We identified consistent autocorrelation in task disengagement. This made it possible to build models capturing task-independent engagement. We identified task general patterns of neural activity linked to impending sudden task disengagement in mid-cingulate gyrus. By contrast, activity centered in perigenual anterior cingulate cortex (pgACC) was associated with maintenance of performance across tasks. Importantly, we carefully controlled for task-specific factors such as the reward history and other motivational effects, such as response vigor, in our analyses. Moreover, we showed pgACC activity had a causal link to task engagement: transcranial ultrasound stimulation of pgACC changed task engagement patterns.

Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subregions of VFC in macaque monkeys performing a two-choice probabilistic task for different fruit juices outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral subdivision 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within 12o, by contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subregions, while the properties of the reward, such as its flavor, were more strongly represented in areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

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.

Contribution of the subthalamic nucleus to motor, cognitive and limbic processes: an electrophysiological and stimulation study in monkeys

Deep brain stimulation of the subthalamic nucleus (STN) has become the gold standard surgical treatment for Parkinson's disease and is being investigated for obsessive compulsive disorders. Even if the role of the STN in the behavior is well documented, its organization and especially its division into several functional territories is still debated. A better characterization of these territories and a better knowledge of the impact of stimulation would address this issue. We aimed to find specific electrophysiological markers of motor, cognitive and limbic functions within the STN and to specifically modulate these components. Two healthy non-human primates (Macaca fascicularis) performed a behavioral task allowing the assessment of motor, cognitive and limbic reward-related behavioral components. During the task, four contacts in the STN allowed recordings and stimulations, using low frequency stimulation (LFS) and high frequency stimulation (HFS). Specific electrophysiological functional markers were found in the STN with beta band activity for the motor component of behavior, theta band activity for the cognitive component, and, gamma and theta activity bands for the limbic component. For both monkeys, dorsolateral HFS and LFS of the STN significantly modulated motor performances, whereas only ventromedial HFS modulated cognitive performances. Our results validated the functional overlap of dorsal motor and ventral cognitive subthalamic territories, and, provide information that tends toward a diffuse limbic territory sensitive to the reward within the STN.

Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research

The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) is a brain area subject to many theories and debates over its function(s). Even its precise anatomical borders are subject to much controversy. In the past decades, the dmPFC/dACC has been associated with more than 15 different cognitive processes, which sometimes appear quite unrelated (e.g. body perception, cognitive conflict). As a result, understanding what the dmPFC/dACC does has become a real challenge for many neuroscientists. Several theories of this brain area's function(s) have been developed, leading to successive and competitive publications bearing different models, which sometimes contradict each other. During the last two decades, the lively scientific exchanges around the dmPFC/dACC have promoted fruitful research in cognitive neuroscience. In this review, we provide an overview of the anatomy of the dmPFC/dACC, summarize the state of the art of functions that have been associated with this brain area and present the main theories aiming at explaining the dmPFC/dACC function(s). We explore the commonalities and the arguments between the different theories. Finally, we explain what can be learned from these debates for future investigations of the dmPFC/dACC and other brain regions' functions.

Dissecting the neurocomputational bases of patch-switching

The survival and well-being of humans require solving the patch-switching problem: we must decide when to stop collecting rewards in a current patch and travel somewhere else where gains may be higher. Previous studies suggested that frontal regions are underpinned by several processes in the context of foraging decisions such as tracking task difficulty, and/or the value of exploring the environment. To dissociate between these processes, participants completed an fMRI patch-switching learning task inspired by behavioral ecology. By analyzing >11,000 trials collected across 21 participants, we found that the activation in the cingulate cortex was closely related to several patch-switching-related variables including the decision to leave the current patch, the encounter of a new patch, the harvest value, and the relative forage value. Learning-induced changes in the patch-switching threshold were tracked by activity within frontoparietal regions including the superior frontal gyrus and angular gyrus. Our findings suggest that frontoparietal regions shape patch-switching learning apart from encoding classical non-learning foraging processes. These findings provide a novel neurobiological understanding of how learning emerges neurocomputationally shaping patch-switching behavior with implications in real-life choices such as job selection and pave the way for future studies to probe the causal role of these neurobiological mechanisms.

Preferences reveal separable valuation systems in prefrontal-limbic circuits

Individual preferences for the flavor of different foods and fluids exert a strong influence on behavior. Most current theories posit that preferences are integrated with other state variables in orbitofrontal cortex (OFC), which is thought to derive the relative subjective value of available options to drive choice behavior. Here we report that instead of a single integrated valuation system in OFC, another separate one is centered in ventrolateral prefrontal cortex (vlPFC) in macaque monkeys. Specifically, we found that OFC and vlPFC preferentially represent outcome flavor and outcome probability, respectively, and that preferences are separately integrated into these two aspects of subjective valuation. In addition, vlPFC, but not OFC, represented the outcome probability for the two options separately, with the difference between these representations reflecting the degree of preference. Thus, there are at least two separable valuation systems that work in concert to guide choices and that both are biased by preferences.

Neuronal properties of pyramidal cells in lateral prefrontal cortex of the aging rhesus monkey brain are associated with performance deficits on spatial working memory but not executive function

Age-related declines in cognitive abilities occur as early as middle-age in humans and rhesus monkeys. Specifically, performance by aged individuals on tasks of executive function (EF) and working memory (WM) is characterized by greater frequency of errors, shorter memory spans, increased frequency of perseverative responses, impaired use of feedback and reduced speed of processing. However, how aging precisely differentially impacts specific aspects of these cognitive functions and the distinct brain areas mediating cognition are not well understood. The prefrontal cortex (PFC) is known to mediate EF and WM and is an area that shows a vulnerability to age-related alterations in neuronal morphology. In the current study, we show that performance on EF and WM tasks exhibited significant changes with age, and these impairments correlate with changes in biophysical properties of layer 3 (L3) pyramidal neurons in lateral LPFC (LPFC). Specifically, there was a significant age-related increase in excitability of L3 LPFC pyramidal neurons, consistent with previous studies. Further, this age-related hyperexcitability of LPFC neurons was significantly correlated with age-related decline on a task of WM, but not an EF task. The current study characterizes age-related performance on tasks of WM and EF and provides insight into the neural substrates that may underlie changes in both WM and EF with age.

Dissociation and integration of outcome and state uncertainty signals in cognitive control

Signals related to uncertainty are frequently observed in regions of the cognitive control network, including anterior cingulate/medial prefrontal cortex (ACC/mPFC), dorsolateral prefrontal cortex (dlPFC), and anterior insular cortex. Uncertainty generally refers to conditions in which decision variables may assume multiple possible values and can arise at multiple points in the perception-action cycle, including sensory input, inferred states of the environment, and the consequences of actions. These sources of uncertainty are frequently correlated: noisy input can lead to unreliable estimates of the state of the environment, with consequential influences on action selection. Given this correlation amongst various sources of uncertainty, dissociating the neural structures underlying their estimation presents an ongoing issue: a region associated with uncertainty related to outcomes may estimate outcome uncertainty itself, or it may reflect a cascade effect of state uncertainty on outcome estimates. In this study, we derive signals of state and outcome uncertainty from mathematical models of risk and observe regions in the cognitive control network whose activity is best explained by signals related to state uncertainty (anterior insula), outcome uncertainty (dlPFC), as well as regions that appear to integrate the two (ACC/mPFC).

Neuronal properties of pyramidal cells in lateral prefrontal cortex of the aging rhesus monkey brain are associated with performance deficits on spatial working memory but not executive function

Age-related declines in cognitive abilities occur as early as middle-age in humans and rhesus monkeys. Specifically, performance by aged individuals on tasks of executive function (EF) and working memory (WM) is characterized by greater frequency of errors, shorter memory spans, increased frequency of perseverative responses, impaired use of feedback and reduced speed of processing. However, how aging precisely differentially impacts specific aspects of these cognitive functions and the distinct brain areas mediating cognition are not well understood. The prefrontal cortex (PFC) is known to mediate EF and WM and is an area that shows a vulnerability to age-related alterations in neuronal morphology. In the current study, we show that performance on EF and WM tasks exhibited significant changes with age and these impairments correlate with changes in biophysical properties of L3 pyramidal neurons in lateral LPFC (LPFC). Specifically, there was a significant age-related increase in excitability of Layer 3 LPFC pyramidal neurons, consistent with previous studies. Further, this age-related hyperexcitability of LPFC neurons was significantly correlated with age-related decline on a task of WM, but not an EF task. The current study characterizes age-related performance on tasks of WM and EF and provides insight into the neural substrates that may underlie changes in both WM and EF with age.

A shared brain system forming confidence judgment across cognitive domains

Confidence is typically defined as a subjective judgment about whether a decision is right. Decisions are based on sources of information that come from various cognitive domains and are processed in different brain systems. An unsettled question is whether the brain computes confidence in a similar manner whatever the domain or in a manner that would be idiosyncratic to each domain. To address this issue, human participants performed two tasks probing confidence in decisions made about the same material (history and geography statements), but based on different cognitive processes: semantic memory for deciding whether the statement was true or false, and duration perception for deciding whether the statement display was long or short. At the behavioral level, we found that the same factors (difficulty, accuracy, response time, and confidence in the preceding decision) predicted confidence judgments in both tasks. At the neural level, we observed using functional magnetic resonance imaging that confidence judgments in both tasks were associated to activity in the same brain regions: positively in the ventromedial prefrontal cortex and negatively in a prefronto-parietal network. Together, these findings suggest the existence of a shared brain system that generates confidence judgments in a similar manner across cognitive domains.

Neural responses in macaque prefrontal cortex are linked to strategic exploration

Humans have been shown to strategically explore. They can identify situations in which gathering information about distant and uncertain options is beneficial for the future. Because primates rely on scarce resources when they forage, they are also thought to strategically explore, but whether they use the same strategies as humans and the neural bases of strategic exploration in monkeys are largely unknown. We designed a sequential choice task to investigate whether monkeys mobilize strategic exploration based on whether information can improve subsequent choice, but also to ask the novel question about whether monkeys adjust their exploratory choices based on the contingency between choice and information, by sometimes providing the counterfactual feedback about the unchosen option. We show that monkeys decreased their reliance on expected value when exploration could be beneficial, but this was not mediated by changes in the effect of uncertainty on choices. We found strategic exploratory signals in anterior and mid-cingulate cortex (ACC/MCC) and dorsolateral prefrontal cortex (dlPFC). This network was most active when a low value option was chosen, which suggests a role in counteracting expected value signals, when exploration away from value should to be considered. Such strategic exploration was abolished when the counterfactual feedback was available. Learning from counterfactual outcome was associated with the recruitment of a different circuit centered on the medial orbitofrontal cortex (OFC), where we showed that monkeys represent chosen and unchosen reward prediction errors. Overall, our study shows how ACC/MCC-dlPFC and OFC circuits together could support exploitation of available information to the fullest and drive behavior towards finding more information through exploration when it is beneficial.

Check or Go? Impact of Doubt on the Hierarchical Organization of the Mediofrontal Area

BACKGROUND

Based on numerous imaging and electrophysiological studies, the presupplementary motor area (pre-SMA) and the rostral cingulate motor area are cortical regions considered to be essential to voluntary movement initiation and behavioral control. However, their respective roles and functional interactions remain a long-standing and still debated question.

METHODS

Here, we trained 2 rhesus monkeys (Macaca mulatta) in a complex cognitive task to compare the neuronal activity of these 2 regions on the medial wall during both perceptual and internally guided decisions.

RESULTS

We confirmed the implication of both areas throughout the decision process. Critically, we demonstrate that instead of a stable invariant role, the pre-SMA and rostral cingulate motor area manifested a versatile hierarchical relationship depending on the mode of movement initiation. Whereas pre-SMA neurons were primarily engaged in decisions based on perceptual information, rostral cingulate motor area neurons preempted the decision process in case of an internally doubt-driven checking behavior, withholding pre-SMA recruitment during the time spent inhibiting the habitual action.

CONCLUSIONS

We identified a versatile hierarchical organization of the mediofrontal area that may substantially affect normal and pathological decision processes because adaptive behaviors, such as doubt-checking and its compulsive counterpart, rely on this subtle equilibrium in controlling action initiation.

BioSimia, France CNRS network for nonhuman primate biomedical research in infectiology, immunology, and neuroscience

Research and developments based on nonhuman primate models have a specific place in biomedical sciences, and nonhuman primate species also have a specific place in the public opinion on the use of animal in research. While nonhuman primates are used in very limited number compared to other animal models, they are rightly the focus of deep ethical concerns. The importance of nonhuman primates in neuroscientific fundamental and preclinical discoveries together with the targeting of such research by activist groups well illustrate this fact. Nonhuman primates also highly contribute to other biomedical fields including immunology, virology, or metabolic and respiratory physiology. In all these fields, researchers, engineers and technicians face similar matters and share the same needs for optimal animal welfare, handling, and veterinary care, the same quest for first-rate research infrastructure and funding, and the same yearning for more public understanding and support. In this article, we give an overview of the evolution of human-animal relationships and public attitudes to animal research in France, and we recount the creation of BioSimia, France network for nonhuman primate biomedical research which now links all academic laboratories nationwide in all the domains for which nonhuman primates remain essential. We explain the principles as well as the outcomes of networking across disciplines. As a perspective, we outline the potential benefits of extending such network to a European scale.

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.

Inhibitory control of frontal metastability sets the temporal signature of cognition

Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.

Integrating Reward Information for Prospective Behavior

Value-based decision-making is often studied in a static context, where participants decide which option to select from those currently available. However, everyday life often involves an additional dimension: deciding when to select to maximize reward. Recent evidence suggests that agents track the latent reward of an option, updating changes in their latent reward estimate, to achieve appropriate selection timing (latent reward tracking). However, this strategy can be difficult to distinguish from one in which the optimal selection time is estimated in advance, allowing an agent to wait a predetermined amount of time before selecting, without needing to monitor an option's latent reward (distance-to-goal tracking). Here, we show that these strategies can in principle be dissociated. Human brain activity was recorded using electroencephalography (EEG), while female and male participants performed a novel decision task. Participants were shown an option and decided when to select it, as its latent reward changed from trial-to-trial. While the latent reward was uncued, it could be estimated using cued information about the option's starting value and value growth rate. We then used representational similarity analysis (RSA) to assess whether EEG signals more closely resembled latent reward tracking or distance-to-goal tracking. This approach successfully dissociated the strategies in this task. Starting value and growth rate were translated into a distance-to-goal signal, far in advance of selecting the option. Latent reward could not be independently decoded. These results demonstrate the feasibility of using high temporal resolution neural recordings to identify internally computed decision variables in the human brain.SIGNIFICANCE STATEMENT Reward-seeking behavior involves acting at the right time. However, the external world does not always tell us when an action is most rewarding, necessitating internal representations that guide action timing. Specifying this internal neural representation is challenging because it might stem from a variety of strategies, many of which make similar predictions about brain activity. This study used a novel approach to test whether alternative strategies could be dissociated in principle. Using representational similarity analysis (RSA), we were able to distinguish between candidate internal representations for selection timing. This shows how pattern analysis methods can be used to measure latent decision information in noninvasive neural data.

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.

Frontal Cortical Functional Connectivity Is Impacted by Anaesthesia in Macaques

A critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional magnetic resonance imaging (rs-fMRI) in anaesthetized nonhuman primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity organization of a well-characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques show that rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas, while more caudal seeds displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrated a similar functional connectivity (FC) organization of the cingulo-frontal cortical network in awake macaque to that previously uncovered in the human brain pointing toward a preserved FC organization from macaque to human. However, it can only be observed in awake state suggesting that this network is sensitive to anaesthesia and warranting significant caution when comparing FC patterns across species under different states.

Medial Frontal Cortex Activity Predicts Information Sampling in Economic Choice

Decision-making not only requires agents to decide what to choose but also how much information to sample before committing to a choice. Previously established frameworks for economic choice argue for a deliberative process of evidence accumulation across time. These tacitly acknowledge a role of information sampling in that decisions are only made once sufficient evidence is acquired, yet few experiments have explicitly placed information sampling under the participant's control. Here, we use fMRI to investigate the neural basis of information sampling in economic choice by allowing participants (n = 30, sex not recorded) to actively sample information in a multistep decision task. We show that medial frontal cortex (MFC) activity is predictive of further information sampling before choice. Choice difficulty (inverse value difference, keeping sensory difficulty constant) was also encoded in MFC, but this effect was explained away by the inclusion of information sampling as a coregressor in the general linear model. A distributed network of regions across the prefrontal cortex encoded key features of the sampled information at the time it was presented. We propose that MFC is an important controller of the extent to which information is gathered before committing to an economic choice. This role may explain why MFC activity has been associated with evidence accumulation in previous studies in which information sampling was an implicit rather than explicit feature of the decision.SIGNIFICANCE STATEMENT The decisions we make are determined by the information we have sampled before committing to a choice. Accumulator frameworks of decision-making tacitly acknowledge the need to sample further information during the evidence accumulation process until a decision boundary is reached. However, relatively few studies explicitly place this decision to sample further information under the participant's control. In this fMRI study, we find that MFC activity is related to information sampling decisions in a multistep economic choice task. This suggests that an important role of evidence representations within MFC may be to guide adaptive sequential decisions to sample further information before committing to a final decision.

Joint contributions of metacognition and self-beliefs to uncertainty-guided checking behavior

Checking behavior is a natural and adaptive strategy for resolving uncertainty in everyday situations. Here, we aimed at investigating the psychological drivers of checking and its regulation by uncertainty, in non-clinical participants and controlled experimental settings. We found that the sensitivity of participants' explicit confidence judgments to actual performance (explicit metacognition) predicted the extent to which their checking strategy was regulated by uncertainty. Yet, a more implicit measure of metacognition (derived from asking participants to opt between trials) did not contribute to the regulation of checking behavior. Meanwhile, how participants scaled on questionnaires eliciting self-beliefs such as self-confidence and self-reported obsessive-compulsive symptoms also predicted participants' uncertainty-guided checking tendencies. Altogether, these findings demonstrate that checking behavior is likely the outcome of a core explicit metacognitive process operating at the scale of single decisions, while remaining influenced by general self-beliefs. Our findings are thus consistent with two mechanisms (micro vs. macro) through which this otherwise adaptive behavior could go awry in certain psychiatric disorders such as obsessive-compulsive disorder.

Formalizing planning and information search in naturalistic decision-making

Decisions made by mammals and birds are often temporally extended. They require planning and sampling of decision-relevant information. Our understanding of such decision-making remains in its infancy compared with simpler, forced-choice paradigms. However, recent advances in algorithms supporting planning and information search provide a lens through which we can explain neural and behavioral data in these tasks. We review these advances to obtain a clearer understanding for why planning and curiosity originated in certain species but not others; how activity in the medial temporal lobe, prefrontal and cingulate cortices may support these behaviors; and how planning and information search may complement each other as means to improve future action selection.

Interactions of Medial and Lateral Prefrontal Cortex in Hierarchical Predictive Coding

Cognitive control and decision-making rely on the interplay of medial and lateral prefrontal cortex (mPFC/lPFC), particularly for circumstances in which correct behavior requires integrating and selecting among multiple sources of interrelated information. While the interaction between mPFC and lPFC is generally acknowledged as a crucial circuit in adaptive behavior, the nature of this interaction remains open to debate, with various proposals suggesting complementary roles in (i) signaling the need for and implementing control, (ii) identifying and selecting appropriate behavioral policies from a candidate set, and (iii) constructing behavioral schemata for performance of structured tasks. Although these proposed roles capture salient aspects of conjoint mPFC/lPFC function, none are sufficiently well-specified to provide a detailed account of the continuous interaction of the two regions during ongoing behavior. A recent computational model of mPFC and lPFC, the Hierarchical Error Representation (HER) model, places the regions within the framework of hierarchical predictive coding, and suggests how they interact during behavioral periods preceding and following salient events. In this manuscript, we extend the HER model to incorporate real-time temporal dynamics and demonstrate how the extended model is able to capture single-unit neurophysiological, behavioral, and network effects previously reported in the literature. Our results add to the wide range of results that can be accounted for by the HER model, and provide further evidence for predictive coding as a unifying framework for understanding PFC function and organization.

The midcingulate cortex and temporal integration

The ability to integrate information across time at multiple timescales is a vital element of adaptive behavior, because it provides the capacity to link events separated in time, extract useful information from previous events and actions, and to construct plans for behavior over time. Here we make the argument that this information integration capacity is a central function of the midcingulate cortex (MCC), by reviewing the anatomical, intrinsic network, neurophysiological, and behavioral properties of MCC. The MCC is the region of the medial wall situated dorsal to the corpus callosum and sometimes referred to as dACC. It is positioned within the densely connected core network of the primate brain, with a rich diversity of cognitive, somatomotor and autonomic connections. Furthermore, the MCC shows strong local network inhibition which appears to control the metastability of the region-an established feature of many cortical networks in which the neural dynamics move through a series of quasi-stationary states. We propose that the strong local inhibition in MCC leads to particularly long dynamic state durations, and so less frequent transitions. Apparently as a result of these anatomical features and synaptic and ionic determinants, the MCC cells display the longest neuronal timescales among a range of recorded cortical areas. We conclude that the anatomical position, intrinsic properties, and local network interactions of MCC make it a uniquely positioned cortical area to perform the integration of diverse information over time that is necessary for behavioral adaptation.

Anterior Cingulate Cortex and the Control of Dynamic Behavior in Primates

The brain mechanism for controlling continuous behavior in dynamic contexts must mediate action selection and learning across many timescales, responding differentially to the level of environmental uncertainty and volatility. In this review, we argue that a part of the frontal cortex known as the anterior cingulate cortex (ACC) is particularly well suited for this function. First, the ACC is interconnected with prefrontal, parietal, and subcortical regions involved in valuation and action selection. Second, the ACC integrates diverse, behaviorally relevant information across multiple timescales, producing output signals that temporally encapsulate decision and learning processes and encode high-dimensional information about the value and uncertainty of future outcomes and subsequent behaviors. Third, the ACC signals behaviorally relevant information flexibly, displaying the capacity to represent information about current and future states in a valence-, context-, task- and action-specific manner. Fourth, the ACC dynamically controls instrumental- and non-instrumental information seeking behaviors to resolve uncertainty about future outcomes. We review electrophysiological and circuit disruption studies in primates to develop this point, discuss its relationship to novel therapeutics for neuropsychiatric disorders in humans, and conclude by relating ongoing research in primates to studies of medial frontal cortical regions in rodents.

Topographic Distribution of Amyloid-β, Tau, and Atrophy in Patients With Behavioral/Dysexecutive Alzheimer Disease

Objective

To determine the associations between amyloid-PET, tau-PET, and atrophy with the behavioral/dysexecutive presentation of Alzheimer disease (AD), how these differ from amnestic AD, and how they correlate to clinical symptoms.

Methods

We assessed 15 patients with behavioral/dysexecutive AD recruited from a tertiary care memory clinic, all of whom had biologically defined AD. They were compared with 25 patients with disease severity– and age-matched amnestic AD and a group of 131 cognitively unimpaired (CU) elderly individuals. All participants were evaluated with amyloid-PET with [¹⁸F]AZD4694, tau-PET with [¹⁸F]MK6240, MRI, and neuropsychological testing.

Results

Voxelwise contrasts identified patterns of frontal cortical tau aggregation in behavioral/dysexecutive AD, with peaks in medial prefrontal, anterior cingulate, and frontal insular cortices in contrast to amnestic AD. No differences were observed in the distribution of amyloid-PET or atrophy as determined by voxel-based morphometry. Voxelwise area under the receiver operating characteristic curve analyses revealed that tau-PET uptake in the medial prefrontal, anterior cingulate, and frontal insular cortices were best able to differentiate between behavioral/dysexecutive and amnestic AD (area under the curve 0.87). Voxelwise regressions demonstrated relationships between frontal cortical tau load and degree of executive dysfunction.

Conclusions

Our results provide evidence of frontal cortical involvement of tau pathology in behavioral/dysexecutive AD and highlight the need for consensus clinical criteria in this syndrome.

Polarity of uncertainty representation during exploration and exploitation in ventromedial prefrontal cortex

Environments furnish multiple information sources for making predictions about future events. Here we use behavioural modelling and functional magnetic resonance imaging to describe how humans select predictors that might be most relevant. First, during early encounters with potential predictors, participants' selections were explorative and directed towards subjectively uncertain predictors (positive uncertainty effect). This was particularly the case when many future opportunities remained to exploit knowledge gained. Then, preferences for accurate predictors increased over time, while uncertain predictors were avoided (negative uncertainty effect). The behavioural transition from positive to negative uncertainty-driven selections was accompanied by changes in the representations of belief uncertainty in ventromedial prefrontal cortex (vmPFC). The polarity of uncertainty representations (positive or negative encoding of uncertainty) changed between exploration and exploitation periods. Moreover, the two periods were separated by a third transitional period in which beliefs about predictors' accuracy predominated. The vmPFC signals a multiplicity of decision variables, the strength and polarity of which vary with behavioural context.

Dissociable Neural Systems Support the Learning and Transfer of Hierarchical Control Structure

Humans can draw insight from previous experiences to quickly adapt to novel environments that share a common underlying structure. Here we combine functional imaging and computational modeling to identify the neural systems that support the discovery and transfer of hierarchical task structure. Human subjects (male and female) completed multiple blocks of a reinforcement learning task that contained a global hierarchical structure governing stimulus-response action mapping. First, behavioral and computational evidence showed that humans successfully discover and transfer the hierarchical rule structure embedded within the task. Next, analysis of fMRI BOLD data revealed activity across a frontoparietal network that was specifically associated with the discovery of this embedded structure. Finally, activity throughout a cingulo-opercular network supported the transfer and implementation of this discovered structure. Together, these results reveal a division of labor in which dissociable neural systems support the learning and transfer of abstract control structures.SIGNIFICANCE STATEMENT A fundamental and defining feature of human behavior is the ability to generalize knowledge from the past to support future action. Although the neural circuits underlying more direct forms of learning have been well established over the last century, we still lack a solid framework from which to investigate more abstract, higher-order human learning and knowledge generalization. We designed a novel behavioral paradigm to specifically isolate a learning process in which previous knowledge, rather than directly indicating the correct action, instead guides the search for the correct action. Moreover, we identify that this learning process is achieved via the coordinated and temporally specific activity of two prominent cognitive control brain networks.

Stable and dynamic representations of value in the prefrontal cortex

Optimal decision-making requires that stimulus-value associations are kept up to date by constantly comparing the expected value of a stimulus with its experienced outcome. To do this, value information must be held in mind when a stimulus and outcome are separated in time. However, little is known about the neural mechanisms of working memory (WM) for value. Contradicting theories have suggested WM requires either persistent or transient neuronal activity, with stable or dynamic representations, respectively. To test these hypotheses, we recorded neuronal activity in the orbitofrontal and anterior cingulate cortex of two monkeys performing a valuation task. We found that features of all hypotheses were simultaneously present in prefrontal activity, and no single hypothesis was exclusively supported. Instead, mixed dynamics supported robust, time invariant value representations while also encoding the information in a temporally specific manner. We suggest that this hybrid coding is a critical mechanism supporting flexible cognitive abilities.

Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques

One of the most influential accounts of central orbitofrontal cortex-that it mediates behavioral flexibility-has been challenged by the finding that discrimination reversal in macaques, the classic test of behavioral flexibility, is unaffected when lesions are made by excitotoxin injection rather than aspiration. This suggests that the critical brain circuit mediating behavioral flexibility in reversal tasks lies beyond the central orbitofrontal cortex. To determine its identity, a group of nine macaques were taught discrimination reversal learning tasks, and its impact on gray matter was measured. Magnetic resonance imaging scans were taken before and after learning and compared with scans from two control groups, each comprising 10 animals. One control group learned discrimination tasks that were similar but lacked any reversal component, and the other control group engaged in no learning. Gray matter changes were prominent in posterior orbitofrontal cortex/anterior insula but were also found in three other frontal cortical regions: lateral orbitofrontal cortex (orbital part of area 12 [12o]), cingulate cortex, and lateral prefrontal cortex. In a second analysis, neural activity in posterior orbitofrontal cortex/anterior insula was measured at rest, and its pattern of coupling with the other frontal cortical regions was assessed. Activity coupling increased significantly in the reversal learning group in comparison with controls. In a final set of experiments, we used similar structural imaging procedures and analyses to demonstrate that aspiration lesion of central orbitofrontal cortex, of the type known to affect discrimination learning, affected structure and activity in the same frontal cortical circuit. The results identify a distributed frontal cortical circuit associated with behavioral flexibility.

The Value of Persistent Value

In this issue of Neuron, Bari et al. (2019) show that neurons in medial frontal cortex, but not a nearby premotor area, encode the relative value of available options with long-lasting persistent activity states during naturalistic foraging. These long-lasting activity states serve to preferentially guide choices to more likely rewarded options.

A Basal Forebrain-Cingulate Circuit in Macaques Decides It Is Time to Act

The medial frontal cortex has been linked to voluntary action, but an explanation of why decisions to act emerge at particular points in time has been lacking. We show that, in macaques, decisions about whether and when to act are predicted by a set of features defining the animal’s current and past context; for example, respectively, cues indicating the current average rate of reward and recent previous voluntary action decisions. We show that activity in two brain areas—the anterior cingulate cortex and basal forebrain—tracks these contextual factors and mediates their effects on behavior in distinct ways. We use focused transcranial ultrasound to selectively and effectively stimulate deep in the brain, even as deep as the basal forebrain, and demonstrate that alteration of activity in the two areas changes decisions about when to act.

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Likelihood and timing of voluntary action in macaques can be partially predicted

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Recent experience and present context influence when voluntary action occurs

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A basal forebrain-cingulate circuit mediated effects of these factors on behavior

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Stimulation of this circuit by ultrasound changed decisions about when to act

Autistic traits influence the strategic diversity of information sampling: Insights from two-stage decision models

Information sampling can reduce uncertainty in future decisions but is often costly. To maximize reward, people need to balance sampling cost and information gain. Here we aimed to understand how autistic traits influence the optimality of information sampling and to identify the particularly affected cognitive processes. Healthy human adults with different levels of autistic traits performed a probabilistic inference task, where they could sequentially sample information to increase their likelihood of correct inference and may choose to stop at any moment. We manipulated the cost and evidence associated with each sample and compared participants’ performance to strategies that maximize expected gain. We found that participants were overall close to optimal but also showed autistic-trait-related differences. Participants with higher autistic traits had a higher efficiency of winning rewards when the sampling cost was zero but a lower efficiency when the cost was high and the evidence was more ambiguous. Computational modeling of participants’ sampling choices and decision times revealed a two-stage decision process, with the second stage being an optional second thought. Participants may consider cost in the first stage and evidence in the second stage, or in the reverse order. The probability of choosing to stop sampling at a specific stage increases with increasing cost or increasing evidence. Surprisingly, autistic traits did not influence the decision in either stage. However, participants with higher autistic traits inclined to consider cost first, while those with lower autistic traits considered cost or evidence first in a more balanced way. This would lead to the observed autistic-trait-related advantages or disadvantages in sampling optimality, depending on whether the optimal sampling strategy is determined only by cost or jointly by cost and evidence.

Children with autism can spend hours practicing lining up toys or learning all about cars or lighthouses. This kind of behaviors, we think, may reflect suboptimal information sampling strategies, that is, a failure to balance the gain of information with the cost (time, energy, or money) of information sampling. We hypothesized that suboptimal information sampling is a general characteristic of people with autism or high level of autistic traits. In our experiment, we tested how participants may adjust their sampling strategies with the change of sampling cost and information gain in the environment. Though all participants were healthy young adults who had similar IQs, higher autistic traits were associated with higher or lower efficiency of winning rewards under different conditions. Counterintuitively, participants with different levels of autistic traits did not differ in the general tendency of oversampling or undersampling, or in the decision they would reach when a specific set of sampling cost or information gain was considered. Instead, participants with higher autistic traits consistently considered sampling cost first and only weighed information gain during a second thought, while those with lower autistic traits had more diverse sampling strategies that consequently better balanced sampling cost and information gain.

Planning Your Way: How Humans Strategically Evaluate Prospective Decisions

We are capable of planning ahead by incorporating dynamic factors influencing future choices. In this issue of Neuron, Kolling et al. (2018) present fMRI results of a novel task that demonstrates how humans evaluate alternative environments by prospectively incorporating their characteristics over time and account for their own decision tendencies.

Sources of suboptimality in a minimalistic explore-exploit task

People often choose between sticking with an available good option (exploitation) and trying out a new option that is uncertain but potentially more rewarding (exploration)^1,2. Laboratory studies on explore-exploit decisions often contain real-world complexities such as non-stationary environments, stochasticity under exploitation and unknown reward distributions^3-7. However, such factors might limit the researcher's ability to understand the essence of people's explore-exploit decisions. For this reason, we introduce a minimalistic task in which the optimal policy is to start off exploring and to switch to exploitation at most once in each sequence of decisions. The behaviour of 49 laboratory and 143 online participants deviated both qualitatively and quantitatively from the optimal policy, even when allowing for bias and decision noise. Instead, people seem to follow a suboptimal rule in which they switch from exploration to exploitation when the highest reward so far exceeds a certain threshold. Moreover, we show that this threshold decreases approximately linearly with the proportion of the sequence that remains, suggesting a temporal ratio law. Finally, we find evidence for 'sequence-level' variability that is shared across all decisions in the same sequence. Our results emphasize the importance of examining sequence-level strategies and their variability when studying sequential decision-making.

Control of entropy in neural models of environmental state

Humans and animals construct internal models of their environment in order to select appropriate courses of action. The representation of uncertainty about the current state of the environment is a key feature of these models that controls the rate of learning as well as directly affecting choice behaviour. To maintain flexibility, given that uncertainty naturally decreases over time, most theoretical inference models include a dedicated mechanism to drive up model uncertainty. Here we probe the long-standing hypothesis that noradrenaline is involved in determining the uncertainty, or entropy, and thus flexibility, of neural models. Pupil diameter, which indexes neuromodulatory state including noradrenaline release, predicted increases (but not decreases) in entropy in a neural state model encoded in human medial orbitofrontal cortex, as measured using multivariate functional MRI. Activity in anterior cingulate cortex predicted pupil diameter. These results provide evidence for top-down, neuromodulatory control of entropy in neural state models.

Midcingulate somatomotor and autonomic functions

The midcingulate cortex (MCC) is viewed as a central node within a large-scale system devoted to adjusting behavior in the face of changing environments. Whereas the role of the MCC in interfacing action and cognition is well established, its role in regulating the autonomic nervous system is poorly understood. Yet, adaptive reactions to novel or threatening situations induce coordinated changes in the sympathetic and the parasympathetic systems. The somatomotor maps in the MCC are organized dorsoventrally. A meta-analysis of the literature reveals that the dorsoventral organization might also concern connections with the autonomic nervous system. Activation of the dorsal and ventral parts of the MCC correlate with recruitments of the sympathetic and the parasympathetic systems, respectively. Data also suggest that, in the MCC, projections toward the sympathetic system are mapped along the sensory-motor system following the same cervico-sacral organization as projections on the spinal cord for skeletal motor control.

Triple dissociation of attention and decision computations across prefrontal cortex

Naturalistic decision-making typically involves sequential deployment of attention to choice alternatives to gather information before a decision is made. Attention filters how information enters decision circuits, thus implying that attentional control may shape how decision computations unfold. We recorded neuronal activity from three subregions of the prefrontal cortex (PFC) while monkeys performed an attention-guided decision-making task. From the first saccade to decision-relevant information, a triple dissociation of decision- and attention-related computations emerged in parallel across PFC subregions. During subsequent saccades, orbitofrontal cortex activity reflected the value comparison between currently and previously attended information. In contrast, the anterior cingulate cortex carried several signals reflecting belief updating in light of newly attended information, the integration of evidence to a decision bound and an emerging plan for what action to choose. Our findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.

Prospection, Perseverance, and Insight in Sequential Behavior

Real-world decisions have benefits occurring only later and dependent on additional decisions taken in the interim. We investigated this in a novel decision-making task in humans (n = 76) while measuring brain activity with fMRI (n = 24). Modeling revealed that participants computed the prospective value of decisions: they planned their future behavior taking into account how their decisions might affect which states they would encounter and how they themselves might respond in these states. They considered their own likely future behavioral biases (e.g., failure to adapt to changes in prospective value) and avoided situations in which they might be prone to such biases. Three neural networks in adjacent medial frontal regions were linked to distinct components of prospective decision making: activity in dorsal anterior cingulate cortex, area 8 m/9, and perigenual anterior cingulate cortex reflected prospective value, anticipated changes in prospective value, and the degree to which prospective value influenced decisions.

Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner

Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have remained largely elusive. Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex (dACC) and the brainstem neuromodulatory nuclei: ventral tegmental area (VTA) and locus coeruleus (LC). From this perspective, the dACC optimizes decisions about stimuli and actions, and using the same computational machinery, it also modulates cortical functions (meta-learning), via neuromodulatory control (VTA and LC). We implemented this theory in a novel neuro-computational model–the Reinforcement Meta Learner (RML). We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains, and parsimoniously integrates various previous proposals on dACC functioning.

A major challenge for all organisms is selecting optimal behaviour to obtain resources while minimizing energetic and other expenses. Evolution provided mammals with exceptional decision-making capabilities to face this challenge. Even though neuroscientists have identified a heterogeneous and distributed set of brain structures to be involved, a comprehensive theory about the biological and computational basis of such decision-making is yet to be formulated. We propose that the interaction between the medial prefrontal cortex (a part of the frontal lobes) and the subcortical nuclei releasing catecholaminergic neuromodulators will be key to such a theory. We argue that this interaction allows both the selection of optimal behaviour and, more importantly, the optimal modulation of the very brain circuits that drive such behavioral selection (i.e., meta-learning). We implemented this theory in a novel neuro-computational model, the Reinforcement Meta-Learner (RML). By means of computer simulations we showed that the RML provides a biological and computational account for a set of neuroscientific data with unprecedented scope, thereby suggesting a critical mechanism of decision-making in the mammalian brain.

State-change decisions and dorsomedial prefrontal cortex: the importance of time

Different kinds of decision making can be categorized by their differential effect on the agent’s current and future states as well as the computational challenges they pose. Here, we draw a distinction between within-state and state-change decision-making, and propose that a dedicated decision mechanism exists in dorsomedial prefrontal cortex (dmPFC) that is specialized for state-change decisions. We set out a formal framework in which state change decisions may be made on the basis of the integrated momentary reward rate, over the intended time to be spent in a state. A key feature of this framework is that reward rate is expressed as a function of continuous time. We argue that dmPFC is suited for this type of decision making partly due to its ability to track the passage of time. This proposed function of dmPFC is placed in contrast to other evaluative systems such as the orbitofrontal cortex, which is important for careful deliberation within a specific model-space or option-space and within a decision strategy.

A novel approach to probabilistic characterisation of neural firing patterns

BACKGROUND

The advances in extracellular neural recording techniques result in big data volumes that necessitate fast, reliable, and automatic identification of statistically similar units. This study proposes a single framework yielding a compact set of probabilistic descriptors that characterise the firing patterns of a single unit.

NEW METHOD

Probabilistic features are estimated from an inter-spike-interval time series, without assumptions about the firing distribution or the stationarity. The first level of proposed firing patterns decomposition divides the inter-spike intervals into bursting, moderate and idle firing modes, yielding a coarse feature set. The second level identifies the successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yielding a refined feature set. The features are estimated from simulated data and from experimental recordings from the lateral prefrontal cortex in awake, behaving rhesus monkeys.

RESULTS

An efficient and stable partitioning of neural units is provided by the ensemble evidence accumulation clustering. The possibility of selecting the number of clusters and choosing among coarse and refined feature sets provides an opportunity to explore and compare different data partitions.

CONCLUSIONS

The estimation of features, if applied to a single unit, can serve as a tool for the firing analysis, observing either overall spiking activity or the periods of interest in trial-to-trial recordings. If applied to massively parallel recordings, it additionally serves as an input to the clustering procedure, with the potential to compare the functional properties of various brain structures and to link the types of neural cells to the particular behavioural states.

Heuristic and optimal policy computations in the human brain during sequential decision-making

Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.

Alhough humans often make a series of related decisions, it is unknown whether this is done by relying on optimal or heuristic strategies. Here, the authors show that humans rely on both the best heuristic and the optimal policy, and that these strategies are controlled by parts of the medial prefrontal cortex.

Rostro-Caudal Organization of Connectivity between Cingulate Motor Areas and Lateral Frontal Regions

According to contemporary views, the lateral frontal cortex is organized along a rostro-caudal functional axis with increasingly complex cognitive/behavioral control implemented rostrally, and increasingly detailed motor control implemented caudally. Whether the medial frontal cortex follows the same organization remains to be elucidated. To address this issue, the functional connectivity of the 3 cingulate motor areas (CMAs) in the human brain with the lateral frontal cortex was investigated. First, the CMAs and their representations of hand, tongue, and eye movements were mapped via task-related functional magnetic resonance imaging (fMRI). Second, using resting-state fMRI, their functional connectivity with lateral prefrontal and lateral motor cortical regions of interest (ROIs) were examined. Importantly, the above analyses were conducted at the single-subject level to account for variability in individual cingulate morphology. The results demonstrated a rostro-caudal functional organization of the CMAs in the human brain that parallels that in the lateral frontal cortex: the rostral CMA has stronger functional connectivity with prefrontal regions and weaker connectivity with motor regions; conversely, the more caudal CMAs have weaker prefrontal and stronger motor connectivity. Connectivity patterns of the hand, tongue and eye representations within the CMAs are consistent with that of their parent CMAs. The parallel rostral-to-caudal functional organization observed in the medial and lateral frontal cortex could likely contribute to different hierarchies of cognitive-motor control.

Simultaneous representation of a spectrum of dynamically changing value estimates during decision making

Decisions are based on value expectations derived from experience. We show that dorsal anterior cingulate cortex and three other brain regions hold multiple representations of choice value based on different timescales of experience organized in terms of systematic gradients across the cortex. Some parts of each area represent value estimates based on recent reward experience while others represent value estimates based on experience over the longer term. The value estimates within these areas interact with one another according to their temporal scaling. Some aspects of the representations change dynamically as the environment changes. The spectrum of value estimates may act as a flexible selection mechanism for combining experience-derived value information with other aspects of value to allow flexible and adaptive decisions in changing environments.

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.