A tripartite view of the posterior cingulate cortex.

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

Abstract Value Encoding in Neural Populations But Not Single Neurons.

An important open question in neuroeconomics is how the brain represents the value of offers in a way that is both abstract (allowing for comparison) and concrete (preserving the details of the factors that influence value). Here, we examine neuronal responses to risky and safe options in five brain regions that putatively encode value in male macaques. Surprisingly, we find no detectable overlap in the neural codes used for risky and safe options, even when the options have identical subjective values (as revealed by preference) in any of the regions. Indeed, responses are weakly correlated and occupy distinct (semi-orthogonal) encoding subspaces. Notably, however, these subspaces are linked through a linear transform of their constituent encodings, a property that allows for comparison of dissimilar option types. This encoding scheme allows these regions to multiplex decision related processes: they can encode the detailed factors that influence offer value (here, risky and safety) but also directly compare dissimilar offer types. Together these results suggest a neuronal basis for the qualitatively different psychological properties of risky and safe options and highlight the power of population geometry to resolve outstanding problems in neural coding.SIGNIFICANCE STATEMENT To make economic choices, we must have some mechanism for comparing dissimilar offers. We propose that the brain uses distinct neural codes for risky and safe offers, but that these codes are linearly transformable. This encoding scheme has the dual advantage of allowing for comparison across offer types while preserving information about offer type, which in turn allows for flexibility in changing circumstances. We show that responses to risky and safe offers exhibit these predicted properties in five different reward-sensitive regions. Together, these results highlight the power of population coding principles for solving representation problems in economic choice.

Dorsal Anterior Cingulate Cortex: A Bottom-Up View.

The dorsal anterior cingulate cortex (dACC) has attracted great interest from neuroscientists because it is associated with so many important cognitive functions. Despite, or perhaps because of, its rich functional repertoire, we lack a single comprehensive view of its function. Most research has approached this puzzle from the top down, using aggregate measures such as neuroimaging. We provide a view from the bottom up, with a focus on single-unit responses and anatomy. We summarize the strengths and weaknesses of the three major approaches to characterizing the dACC: as a monitor, as a controller, and as an economic structure. We argue that neurons in the dACC are specialized for representing contexts, or task-state variables relevant for behavior, and strategies, or aspects of future plans. We propose that dACC neurons link contexts with strategies by integrating diverse task-relevant information to create a rich representation of task space and exert high-level and abstract control over decision and action.

The Dangers of Cortical Brain Maps.

The parcellation of the primate cerebral cortex into numbered regions, based on cytoarchitecture, has greatly helped neuroscientists in our quest to understand how the brain implements cognition. Nonetheless, these maps provide an unnecessarily constraining view of how we should do functional neuroanatomy. It is time to think more broadly. Doing so will help advance the goal of incorporating ideas about emergentist organization and interactional complexity into neuroscience.

Rules warp feature encoding in decision-making circuits.

We have the capacity to follow arbitrary stimulus-response rules, meaning simple policies that guide our behavior. Rule identity is broadly encoded across decision-making circuits, but there are less data on how rules shape the computations that lead to choices. One idea is that rules could simplify these computations. When we follow a rule, there is no need to encode or compute information that is irrelevant to the current rule, which could reduce the metabolic or energetic demands of decision-making. However, it is not clear if the brain can actually take advantage of this computational simplicity. To test this idea, we recorded from neurons in 3 regions linked to decision-making, the orbitofrontal cortex (OFC), ventral striatum (VS), and dorsal striatum (DS), while macaques performed a rule-based decision-making task. Rule-based decisions were identified via modeling rules as the latent causes of decisions. This left us with a set of physically identical choices that maximized reward and information, but could not be explained by simple stimulus-response rules. Contrasting rule-based choices with these residual choices revealed that following rules (1) decreased the energetic cost of decision-making; and (2) expanded rule-relevant coding dimensions and compressed rule-irrelevant ones. Together, these results suggest that we use rules, in part, because they reduce the costs of decision-making through a distributed representational warping in decision-making circuits.

OpenMonkeyChallenge: Dataset and Benchmark Challenges for Pose Estimation of Non-human Primates.

The ability to automatically estimate the pose of non-human primates as they move through the world is important for several subfields in biology and biomedicine. Inspired by the recent success of computer vision models enabled by benchmark challenges (e.g., object detection), we propose a new benchmark challenge called OpenMonkeyChallenge that facilitates collective community efforts through an annual competition to build generalizable non-human primate pose estimation models. To host the benchmark challenge, we provide a new public dataset consisting of 111,529 annotated (17 body landmarks) photographs of non-human primates in naturalistic contexts obtained from various sources including the Internet, three National Primate Research Centers, and the Minnesota Zoo. Such annotated datasets will be used for the training and testing datasets to develop generalizable models with standardized evaluation metrics. We demonstrate the effectiveness of our dataset quantitatively by comparing it with existing datasets based on seven state-of-the-art pose estimation models.

Heuristics and optimal solutions to the breadth-depth dilemma.

In multialternative risky choice, we are often faced with the opportunity to allocate our limited information-gathering capacity between several options before receiving feedback. In such cases, we face a natural trade-off between breadth-spreading our capacity across many options-and depth-gaining more information about a smaller number of options. Despite its broad relevance to daily life, including in many naturalistic foraging situations, the optimal strategy in the breadth-depth trade-off has not been delineated. Here, we formalize the breadth-depth dilemma through a finite-sample capacity model. We find that, if capacity is small (∼10 samples), it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, which roughly decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, is a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multialternative decisions with bounded capacity using close-to-optimal heuristics.

The PRO model accounts for the anterior cingulate cortex role in risky decision-making and monitoring.

The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and monkey ACC data involving anatomy, neurophysiology, EEG, fMRI, and behavior. We explored the computational nature of ACC with the PRO model, extending it to account specifically for both human and macaque monkey decision-making under risk, including both behavioral and neural data. We show that the PRO model can account for a number of additional effects related to outcome prediction, decision-making under risk, gambling behavior. In particular, we show that the ACC represents the variance of uncertain outcomes, suggesting a link between ACC function and mean-variance theories of decision making. The PRO model provides a unified account of a large set of data regarding the ACC.

A distributed, hierarchical and recurrent framework for reward-based choice.

Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex. This account also suggests that certain correlates of value are emergent rather than represented explicitly in the brain.

Macaques preferentially attend to intermediately surprising information.

Normative learning theories dictate that we should preferentially attend to informative sources, but only up to the point that our limited learning systems can process their content. Humans, including infants, show this predicted strategic deployment of attention. Here, we demonstrate that rhesus monkeys, much like humans, attend to events of moderate surprisingness over both more and less surprising events. They do this in the absence of any specific goal or contingent reward, indicating that the behavioural pattern is spontaneous. We suggest this U-shaped attentional preference represents an evolutionarily preserved strategy for guiding intelligent organisms toward material that is maximally useful for learning.

Automated pose estimation in primates.

Understanding the behavior of primates is important for primatology, for psychology, and for biology more broadly. It is also important for biomedicine, where primates are an important model organism, and whose behavior is often an important variable of interest. Our ability to rigorously quantify behavior has, however, long been limited. On one hand, we can rigorously quantify low-information measures like preference, looking time, and reaction time; on the other, we can use more gestalt measures like behavioral categories tracked via ethogram, but at high cost and with high variability. Recent technological advances have led to a major revolution in behavioral measurement that offers affordable and scalable rigor. Specifically, digital video cameras and automated pose tracking software can provide measures of full-body position (i.e., pose) of primates over time (i.e., behavior) with high spatial and temporal resolution. Pose-tracking technology in turn can be used to infer behavioral states, such as eating, sleeping, and mating. We call this technological approach behavioral imaging. In this review, we situate the behavioral imaging revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that primate centers and zoos will take on a more central role in relevant fields of research than they have in the past.

Ventromedial Prefrontal Cortex Tracks Multiple Environmental Variables during Search.

To make efficient foraging decisions, we must combine information about the values of available options with nonvalue information. Some accounts of ventromedial PFC (vmPFC) suggest that it has a narrow role limited to evaluating immediately available options. We examined responses of neurons in area 14 (a putative macaque homolog of human vmPFC) as 2 male macaques performed a novel foraging search task. Although many neurons encoded the values of immediately available offers, they also independently encoded several other variables that influence choice, but that are conceptually distinct from offer value. These variables include average reward rate, number of offers viewed per trial, previous offer values, previous outcome sizes, and the locations of the currently attended offer. We conclude that, rather than serving as specialized economic value center, vmPFC plays a broad role in integrating relevant environmental information to drive foraging decisions.SIGNIFICANCE STATEMENT Decision makers must often choose whether to take an immediately available option or continue to search for a better one. We hypothesized that this process, which is integral to foraging theory, leaves neural signatures in the brain region ventromedial PFC. Subjects performed a novel foraging task in which they searched through differently valued options and attempted to balance their reward threshold with various time costs. We found that neurons not only encode the values of immediately available offers, but multiplexed these with environmental variables, including reward rate, number of offers viewed, previous offer values, and spatial information. We conclude that vmPFC plays a rich role in encoding and integrating multiple foraging-related variables during economic decisions.

Ultra-high field (10.5 T) resting state fMRI in the macaque.

Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

Activity in orbitofrontal neuronal ensembles reflects inhibitory control.

Stopping, or inhibition, is a form of self-control that is a core element of flexible and adaptive behavior. Its neural origins remain unclear. Some views hold that inhibition decisions reflect the aggregation of widespread and diverse pieces of information, including information arising in ostensible core reward regions (i.e., outside the canonical executive system). We recorded activity of single neurons in the orbitofrontal cortex (OFC) of macaques, a region associated with economic decisions, and whose role in inhibition is debated. Subjects performed a classic inhibition task known as the stop signal task. Ensemble decoding analyses reveal a clear firing rate pattern that distinguishes successful from failed inhibition and that begins after the stop signal and before the stop signal reaction time (SSRT). We also found a different and orthogonal ensemble pattern that distinguishes successful from failed stopping before the beginning of the trial. These signals were distinct from, and orthogonal to, value encoding, which was also observed in these neurons. The timing of the early and late signals was, respectively, consistent with the idea that neuronal activity in OFC encodes inhibition both proactively and reactively.

Correlates of decisional dynamics in the dorsal anterior cingulate cortex.

We hypothesized that during binary economic choice, decision makers use the first option they attend as a default to which they compare the second. To test this idea, we recorded activity of neurons in the dorsal anterior cingulate cortex (dACC) of macaques choosing between gambles presented asynchronously. We find that ensemble encoding of the value of the first offer includes both choice-dependent and choice-independent aspects, as if reflecting a partial decision. That is, its responses are neither entirely pre- nor post-decisional. In contrast, coding of the value of the second offer is entirely decision dependent (i.e., post-decisional). This result holds even when offer-value encodings are compared within the same time period. Additionally, we see no evidence for 2 pools of neurons linked to the 2 offers; instead, all comparison appears to occur within a single functionally homogenous pool of task-selective neurons. These observations suggest that economic choices reflect a context-dependent evaluation of attended options. Moreover, they raise the possibility that value representations reflect, to some extent, a tentative commitment to a choice.

Tonic exploration governs both flexibility and lapses.

In many cognitive tasks, lapses (spontaneous errors) are tacitly dismissed as the result of nuisance processes like sensorimotor noise, fatigue, or disengagement. However, some lapses could also be caused by exploratory noise: randomness in behavior that facilitates learning in changing environments. If so, then strategic processes would need only up-regulate (rather than generate) exploration to adapt to a changing environment. This view predicts that more frequent lapses should be associated with greater flexibility because these behaviors share a common cause. Here, we report that when rhesus macaques performed a set-shifting task, lapse rates were negatively correlated with perseverative error frequency across sessions, consistent with a common basis in exploration. The results could not be explained by local failures to learn. Furthermore, chronic exposure to cocaine, which is known to impair cognitive flexibility, did increase perseverative errors, but, surprisingly, also improved overall set-shifting task performance by reducing lapse rates. We reconcile these results with a state-switching model in which cocaine decreases exploration by deepening attractor basins corresponding to rule states. These results support the idea that exploratory noise contributes to lapses, affecting rule-based decision-making even when it has no strategic value, and suggest that one key mechanism for regulating exploration may be the depth of rule states.

Behavioural variability contributes to over-staying in patchy foraging.

Foragers often systematically deviate from rate-maximizing choices in two ways: accuracy and precision. That is, they use suboptimal threshold values and also show variability in their application of those thresholds. We hypothesized that these biases are related and, more specifically, that foragers' widely known accuracy bias--over-staying--could be explained, at least in part, by their imprecision. To test this hypothesis, we analysed choices made by three rhesus macaques in a computerized patch foraging task. Confirming previously observed findings, we found high levels of variability. We then showed, through simulations, that this variability changed optimal thresholds, meaning that a forager aware of its own variability should increase its leaving threshold (i.e. over-stay) to increase performance. All subjects showed thresholds that were biased in the predicted direction. These results indicate that over-staying in patches may reflect, in part, an adaptation to behavioural variability.

On the Flexibility of Basic Risk Attitudes in Monkeys.

Monkeys and other animals appear to share with humans two risk attitudes predicted by prospect theory: an inverse-S-shaped probability-weighting (PW) function and a steeper utility curve for losses than for gains. These findings suggest that such preferences are stable traits with common neural substrates. We hypothesized instead that animals tailor their preferences to subtle changes in task contexts, making risk attitudes flexible. Previous studies used a limited number of outcomes, trial types, and contexts. To gain a broader perspective, we examined two large datasets of male macaques' risky choices: one from a task with real (juice) gains and another from a token task with gains and losses. In contrast to previous findings, monkeys were risk seeking for both gains and losses (i.e., lacked a reflection effect) and showed steeper gain than loss curves (loss seeking). Utility curves for gains were substantially different in the two tasks. Monkeys showed nearly linear PWs in one task and S-shaped ones in the other; neither task produced a consistent inverse-S-shaped curve. To account for these observations, we developed and tested various computational models of the processes involved in the construction of reward value. We found that adaptive differential weighting of prospective gamble outcomes could partially account for the observed differences in the utility functions across the two experiments and thus provide a plausible mechanism underlying flexible risk attitudes. Together, our results support the idea that risky choices are constructed flexibly at the time of elicitation and place important constraints on neural models of economic choice.SIGNIFICANCE STATEMENT We respond in reliable ways to risk, but are our risk preferences stable traits or ephemeral states? Using various computational models, we examined two large datasets of macaque risky choices in two different tasks. We observed several deviations from "classic" risk preferences seen in humans and monkeys: no reflection effect, loss seeking as opposed to loss aversion, and linear and S-shaped, as opposed to inverse-S-shaped, probability distortion. These results challenge the idea that our risk attitudes are evolved traits shared with the last common ancestor of macaques and humans, suggesting instead that behavioral flexibility is the hallmark of risky choice in primates. We show how this flexibility can emerge partly as a result of interactions between attentional and reward systems.

Beyond "incentive hope": Information sampling and learning under reward uncertainty.

Information seeking, especially when motivated by strategic learning and intrinsic curiosity, could render the new mechanism "incentive hope" proposed by Anselme & Güntürkün sufficient, but not necessary to explain how reward uncertainty promotes reward seeking and consumption. Naturalistic and foraging-like tasks can help parse motivational processes that bridge learning and foraging behaviors and identify their neural underpinnings.

Robust Encoding of Spatial Information in Orbitofrontal Cortex and Striatum.

Knowing whether core reward regions carry information about the positions of relevant objects is crucial for adjudicating between choice models. One limitation of previous studies, including our own, is that spatial positions can be consistently differentially associated with rewards, and thus position can be confounded with attention, motor plans, or target identity. We circumvented these problems by using a task in which value-and thus choices-was determined solely by a frequently changing rule, which was randomized relative to spatial position on each trial. We presented offers asynchronously, which allowed us to control for reward expectation, spatial attention, and motor plans in our analyses. We find robust encoding of the spatial position of both offers and choices in two core reward regions, orbitofrontal Area 13 and ventral striatum, as well as in dorsal striatum of macaques. The trial-by-trial correlation in noise in encoding of position was associated with variation in choice, an effect known as choice probability correlation, suggesting that the spatial encoding is associated with choice and is not incidental to it. Spatial information and reward information are not carried by separate sets of neurons, although the two forms of information are temporally dissociable. These results highlight the ubiquity of multiplexed information in association cortex and argue against the idea that these ostensible reward regions serve as part of a pure value domain.