Complementary roles for hippocampus and anterior cingulate in composing continuous choice

Naturalistic, goal directed behavior often requires continuous actions directed at dynamically changing goals. In this context, the closest analogue to choice is a strategic reweighting of multiple goal-specific control policies in response to shifting environmental pressures. To understand the algorithmic and neural bases of choice in continuous contexts, we examined behavior and brain activity in humans performing a continuous prey-pursuit task. Using a newly developed control-theoretic decomposition of behavior, we find pursuit strategies are well described by a meta-controller dictating a mixture of lower-level controllers, each linked to specific pursuit goals. Examining hippocampus and anterior cingulate cortex (ACC) population dynamics during goal switches revealed distinct roles for the two regions in parameterizing continuous controller mixing and meta-control. Hippocampal ensemble dynamics encoded the controller blending dynamics, suggesting it implements a mixing of goal-specific control policies. In contrast, ACC ensemble activity exhibited value-dependent ramping activity before goal switches, linking it to a meta-control process that accumulates evidence for switching goals. Our results suggest that hippocampus and ACC play complementary roles corresponding to a generalizable mixture controller and meta-controller that dictates value dependent changes in controller mixing.

Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory

Working memory (WM) is constructive in nature. Instead of passively retaining information, WM reorganizes complex sequences into hierarchically embedded chunks to overcome capacity limits and facilitate flexible behaviour. Here, to investigate the neural mechanisms underlying hierarchical reorganization in WM, we performed two electroencephalography and one magnetoencephalography experiments, wherein humans retain in WM a temporal sequence of items, that is, syllables, which are organized into chunks, that is, multisyllabic words. We demonstrate that the one-dimensional sequence is represented by two-dimensional neural representational geometry in WM arising from left prefrontal and temporoparietal regions, with separate dimensions encoding item position within a chunk and chunk position in the sequence. Critically, this two-dimensional geometry is observed consistently in different experimental settings, even during tasks not encouraging hierarchical reorganization in WM and correlates with WM behaviour. Overall, these findings strongly support that complex sequences are reorganized into factorized multidimensional neural representational geometry in WM, which also speaks to general structure-based organizational principles given WM's involvement in many cognitive functions.

TR(Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting-State Functional MRI Fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

Prefrontal transthalamic uncertainty processing drives flexible switching

Making adaptive decisions in complex environments requires appropriately identifying sources of error1,2. The frontal cortex is critical for adaptive decisions, but its neurons show mixed selectivity to task features3 and their uncertainty estimates4, raising the question of how errors are attributed to their most likely causes. Here, by recording neural responses from tree shrews (Tupaia belangeri) performing a hierarchical decision task with rule reversals, we find that the mediodorsal thalamus independently represents cueing and rule uncertainty. This enables the relevant thalamic population to drive prefrontal reconfiguration following a reversal by appropriately attributing errors to an environmental change. Mechanistic dissection of behavioural switching revealed a transthalamic pathway for cingulate cortical error monitoring5,6 to reconfigure prefrontal executive control7. Overall, our work highlights a potential role for the thalamus in demixing cortical signals while providing a low-dimensional pathway for cortico-cortical communication.

Chicks make stochastic decisions based on gain rates of different time constants

The marginal value theorem (MVT) predicts that optimal foragers leave a patch when the instantaneous gain rate decreases to the average long-term gain rate. However, various animals systematically deviate from this optimum by staying too long or overharvesting relative to this optimum. We hypothesised that animals do not represent their optimal stay time but instead determine their departure point probabilistically. To test this hypothesis, we conducted behavioural experiments and modelling using chicks. The chicks ran on a treadmill with feeders on both sides, and their travel time to the feeder was experimentally controlled. As predicted by the MVT, the chicks stayed longer at the feeder when forced to run more. However, they stayed even longer than predicted by the MVT. Therefore, we constructed and compared stochastic decision-making models with the MVT-based model. The stochastic models explained the chicks' behaviour better than the MVT-based model. These results suggest that chicks leave probabilistically based on their immediate foraging history rather than representing an optimal stay time.

Dynamic tuning of neural stability for cognitive control

The brain is thought to execute cognitive control by actively maintaining and flexibly updating patterns of neural activity that represent goals and rules. However, while actively maintaining patterns of activity requires robustness against noise and distractors, updating the activity requires sensitivity to task-relevant inputs. How these conflicting demands can be reconciled in a single neural system remains unclear. Here, we study the prefrontal cortex of monkeys maintaining a covert rule and integrating sensory inputs toward a choice. Following the onset of neural responses, sensory integration evolves with a 70 ms delay. Using a stability analysis and a recurrent neural network model trained to perform the task, we show that this delay enables a transient, system-level destabilization, opening a temporal window to selectively incorporate new information. This mechanism allows robustness and sensitivity to coexist in a neural system and hierarchically updates patterns of neural activity, providing a general framework for cognitive control. Furthermore, it reveals a learned, explicit rule representation, suggesting a reconciliation between the symbolic and connectionist approaches for building intelligent machines.

Alcohol, flexible behavior, and the prefrontal cortex: Functional changes underlying impaired cognitive flexibility

Cognitive flexibility enables individuals to alter their behavior in response to changing environmental demands, facilitating optimal behavior in a dynamic world. The inability to do this, called behavioral inflexibility, is a pervasive behavioral phenotype in alcohol use disorder (AUD), driven by disruptions in cognitive flexibility. Research has repeatedly shown that behavioral inflexibility not only results from alcohol exposure across species but can itself be predictive of future drinking. Like many high-level executive functions, flexible behavior requires healthy functioning of the prefrontal cortex (PFC). The scope of this review addresses two primary themes: first, we outline tasks that have been used to investigate flexibility in the context of AUD or AUD models. We characterize these based on the task features and underlying cognitive processes that differentiate them from one another. We highlight the neural basis of flexibility measures, focusing on the PFC, and how acute or chronic alcohol in humans and non-human animal models impacts flexibility. Second, we consolidate findings on the molecular, physiological and functional changes in the PFC elicited by alcohol, that may contribute to cognitive flexibility deficits seen in AUD. Collectively, this approach identifies several key avenues for future research that will facilitate effective treatments to promote flexible behavior in the context of AUD, to reduce the risk of alcohol related harm, and to improve outcomes following AUD. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

Sensory History Drives Adaptive Neural Geometry in LP/Pulvinar-Prefrontal Cortex Circuits

Prior expectations guide attention and support perceptual filtering for efficient processing during decision-making. Here we show that during a visual discrimination task, mice adaptively use prior stimulus history to guide ongoing choices by estimating differences in evidence between consecutive trials (| Δ Dir |). The thalamic lateral posterior (LP)/pulvinar nucleus provides robust inputs to the Anterior Cingulate Cortex (ACC), which has been implicated in selective attention and predictive processing, but the function of the LP-ACC projection is unknown. We found that optogenetic manipulations of LP-ACC axons disrupted animals' ability to effectively estimate and use information across stimulus history, leading to | Δ Dir |-dependent ipsilateral biases. Two-photon calcium imaging of LP-ACC axons revealed an engagement-dependent low-dimensional organization of stimuli along a curved manifold. This representation was scaled by | Δ Dir | in a manner that emphasized greater deviations from prior evidence. Thus, our work identifies the LP-ACC pathway as essential for selecting and evaluating stimuli relative to prior evidence to guide decisions.

Neural basis of concurrent deliberation toward a choice and degree of confidence

Decision confidence plays a key role in flexible behavior and (meta)cognition, but its underlying neural mechanisms remain elusive. To uncover the latent dynamics of confidence formation at the level of population activity, we designed a decision task for nonhuman primates that measures choice, reaction time, and confidence with a single eye movement on every trial. Monkey behavior was well fit by a bounded accumulator model instantiating parallel processing of evidence, rejecting a serial model in which the choice is resolved first followed by post-decision accumulation for confidence. Neurons in area LIP reflected concurrent accumulation, exhibiting covariation of choice and confidence signals across the population, and within-trial dynamics consistent with parallel updating at near-zero time lag. The results demonstrate that monkeys can process a single stream of evidence in service of two computational goals simultaneously-a categorical decision and associated level of confidence-and illuminate a candidate neural substrate for this ability.

Prediction-error signals in anterior cingulate cortex drive task-switching

Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

The Projection-Specific Noradrenergic Modulation of Perseverative Spatial Behavior in Adult Male Rats

Adaptive behavior relies on efficient cognitive control. The anterior cingulate cortex (ACC) is a key node within the executive prefrontal network. The reciprocal connectivity between the locus ceruleus (LC) and ACC is thought to support behavioral reorganization triggered by the detection of an unexpected change. We transduced LC neurons with either excitatory or inhibitory chemogenetic receptors in adult male rats and trained rats on a spatial task. Subsequently, we altered LC activity and confronted rats with an unexpected change of reward locations. In a new spatial context, rats with decreased noradrenaline (NA) in the ACC entered unbaited maze arms more persistently which was indicative of perseveration. In contrast, the suppression of the global NA transmission reduced perseveration. Neither chemogenetic manipulation nor inactivation of the ACC by muscimol affected the rate of learning, possibly due to partial virus transduction of the LC neurons and/or the compensatory engagement of other prefrontal regions. Importantly, we observed behavioral deficits in rats with LC damage caused by virus injection. The latter finding highlights the importance of careful histological assessment of virus-transduced brain tissue as inadvertent damage of the targeted cell population due to virus neurotoxicity or other factors might cause unwanted side effects. Although the specific role of ACC in the flexibility of spatial behavior has not been convincingly demonstrated, our results support the beneficial role of noradrenergic transmission for an optimal function of the ACC. Overall, our findings suggest the LC exerts the projection-specific modulation of neural circuits mediating the flexibility of spatial behavior.

Neural activity ramps in frontal cortex signal extended motivation during learning

Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward-associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection-specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward-associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.

Neural dynamics underlying minute-timescale persistent behavior in the human brain

The ability to pursue long-term goals relies on a representations of task context that can both be maintained over long periods of time and switched flexibly when goals change. Little is known about the neural substrate for such minute-scale maintenance of task sets. Utilizing recordings in neurosurgical patients, we examined how groups of neurons in the human medial frontal cortex and hippocampus represent task contexts. When cued explicitly, task context was encoded in both brain areas and changed rapidly at task boundaries. Hippocampus exhibited a temporally dynamic code with fast decorrelation over time, preventing cross-temporal generalization. Medial frontal cortex exhibited a static code that decorrelated slowly, allowing generalization across minutes of time. When task context needed to be inferred as a latent variable, hippocampus encoded task context with a static code. These findings reveal two possible regimes for encoding minute-scale task-context representations that were engaged differently based on task demands.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Belief inference for hierarchical hidden states in spatial navigation

Uncertainty abounds in the real world, and in environments with multiple layers of unobservable hidden states, decision-making requires resolving uncertainties based on mutual inference. Focusing on a spatial navigation problem, we develop a Tiger maze task that involved simultaneously inferring the local hidden state and the global hidden state from probabilistically uncertain observation. We adopt a Bayesian computational approach by proposing a hierarchical inference model. Applying this to human task behaviour, alongside functional magnetic resonance brain imaging, allows us to separate the neural correlates associated with reinforcement and reassessment of belief in hidden states. The imaging results also suggest that different layers of uncertainty differentially involve the basal ganglia and dorsomedial prefrontal cortex, and that the regions responsible are organised along the rostral axis of these areas according to the type of inference and the level of abstraction of the hidden state, i.e. higher-order state inference involves more anterior parts.

Mimicking opioid analgesia in cortical pain circuits

The anterior cingulate cortex plays a pivotal role in the cognitive and affective aspects of pain perception. Both endogenous and exogenous opioid signaling within the cingulate mitigate cortical nociception, reducing pain unpleasantness. However, the specific functional and molecular identities of cells mediating opioid analgesia in the cingulate remain elusive. Given the complexity of pain as a sensory and emotional experience, and the richness of ethological pain-related behaviors, we developed a standardized, deep-learning platform for deconstructing the behavior dynamics associated with the affective component of pain in mice-LUPE (Light aUtomated Pain Evaluator). LUPE removes human bias in behavior quantification and accelerated analysis from weeks to hours, which we leveraged to discover that morphine altered attentional and motivational pain behaviors akin to affective analgesia in humans. Through activity-dependent genetics and single-nuclei RNA sequencing, we identified specific ensembles of nociceptive cingulate neuron-types expressing mu-opioid receptors. Tuning receptor expression in these cells bidirectionally modulated morphine analgesia. Moreover, we employed a synthetic opioid receptor promoter-driven approach for cell-type specific optical and chemical genetic viral therapies to mimic morphine's pain-relieving effects in the cingulate, without reinforcement. This approach offers a novel strategy for precision pain management by targeting a key nociceptive cortical circuit with on-demand, non-addictive, and effective analgesia.

Control over a mixture of policies determines change of mind topology during continuous choice

Behavior is naturally organized into categorically distinct states with corresponding patterns of neural activity; how does the brain control those states? We propose that states are regulated by specific neural processes that implement meta-control that can blend simpler control processes. To test this hypothesis, we recorded from neurons in the dorsal anterior cingulate cortex (dACC) and dorsal premotor cortex (PMd) while macaques performed a continuous pursuit task with two moving prey that followed evasive strategies. We used a novel control theoretic approach to infer subjects' moment-to-moment latent control variables, which in turn dictated their blend of distinct identifiable control processes. We identified low-dimensional subspaces in neuronal responses that reflected the current strategy, the value of the pursued target, and the relative value of the two targets. The top two principal components of activity tracked changes of mind in abstract and change-type-specific formats, respectively. These results indicate that control of behavioral state reflects the interaction of brain processes found in dorsal prefrontal regions that implement a mixture over low-level control policies.

Neural activity ramps in frontal cortex signal extended motivation during learning

Learning requires the ability to link actions to outcomes. How motivation facilitates learning is not well understood. We designed a behavioral task in which mice self-initiate trials to learn cue-reward contingencies and found that the anterior cingulate region of the prefrontal cortex (ACC) contains motivation-related signals to maximize rewards. In particular, we found that ACC neural activity was consistently tied to trial initiations where mice seek to leave unrewarded cues to reach reward-associated cues. Notably, this neural signal persisted over consecutive unrewarded cues until reward associated cues were reached, and was required for learning. To determine how ACC inherits this motivational signal we performed projection specific photometry recordings from several inputs to ACC during learning. In doing so, we identified a ramp in bulk neural activity in orbitofrontal cortex (OFC)-to-ACC projections as mice received unrewarded cues, which continued ramping across consecutive unrewarded cues, and finally peaked upon reaching a reward associated cue, thus maintaining an extended motivational state. Cellular resolution imaging of OFC confirmed these neural correlates of motivation, and further delineated separate ensembles of neurons that sequentially tiled the ramp. Together, these results identify a mechanism by which OFC maps out task structure to convey an extended motivational state to ACC to facilitate goal-directed learning.

The mediodorsal thalamus in executive control

Executive control, the ability to organize thoughts and action plans in real time, is a defining feature of higher cognition. Classical theories have emphasized cortical contributions to this process, but recent studies have reinvigorated interest in the role of the thalamus. Although it is well established that local thalamic damage diminishes cognitive capacity, such observations have been difficult to inform functional models. Recent progress in experimental techniques is beginning to enrich our understanding of the anatomical, physiological, and computational substrates underlying thalamic engagement in executive control. In this review, we discuss this progress and particularly focus on the mediodorsal thalamus, which regulates the activity within and across frontal cortical areas. We end with a synthesis that highlights frontal thalamocortical interactions in cognitive computations and discusses its functional implications in normal and pathological conditions.

Learning attentional templates for value-based decision-making

Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

The successor representation subserves hierarchical abstraction for goal-directed behavior

Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.

Metacognition and Confidence: A Review and Synthesis

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

ACC neural ensemble dynamics are structured by strategy prevalence

Medial frontal cortical areas are thought to play a critical role in the brain's ability to flexibly deploy strategies that are effective in complex settings, yet the underlying circuit computations remain unclear. Here, by examining neural ensemble activity in male rats that sample different strategies in a self-guided search for latent task structure, we observe robust tracking during strategy execution of a summary statistic for that strategy in recent behavioral history by the anterior cingulate cortex (ACC), especially by an area homologous to primate area 32D. Using the simplest summary statistic - strategy prevalence in the last 20 choices - we find that its encoding in the ACC during strategy execution is wide-scale, independent of reward delivery, and persists through a substantial ensemble reorganization that accompanies changes in global context. We further demonstrate that the tracking of reward by the ACC ensemble is also strategy-specific, but that reward prevalence is insufficient to explain the observed activity modulation during strategy execution. Our findings argue that ACC ensemble dynamics is structured by a summary statistic of recent behavioral choices, raising the possibility that ACC plays a role in estimating - through statistical learning - which actions promote the occurrence of events in the environment.

Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex

Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.

Decoding Depression Severity From Intracranial Neural Activity

BACKGROUND

Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis.

METHODS

We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings.

RESULTS

Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression.

CONCLUSIONS

The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.

Mixtures of strategies underlie rodent behavior during reversal learning

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

Long- and short-term history effects in a spiking network model of statistical learning

The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.

Frontopolar cortex represents complex features and decision value during choice between environments

Important decisions often involve choosing between complex environments that define future item encounters. Despite its importance for adaptive behavior and distinct computational challenges, decision-making research primarily focuses on item choice, ignoring environment choice altogether. Here we contrast previously studied item choice in ventromedial prefrontal cortex with lateral frontopolar cortex (FPl) linked to environment choice. Furthermore, we propose a mechanism for how FPl decomposes and represents complex environments during decision making. Specifically, we trained a choice-optimized, brain-naive convolutional neural network (CNN) and compared predicted CNN activation with actual FPl activity. We showed that the high-dimensional FPl activity decomposes environment features to represent the complexity of an environment to make such choice possible. Moreover, FPl functionally connects with posterior cingulate cortex for guiding environment choice. Further probing FPl's computation revealed a parallel processing mechanism in extracting multiple environment features.

Explaining reversal learning deficits in anxiety with electrophysiological evidence

Reversal learning is a crucial aspect of behavioral flexibility that plays a significant role in environmental adaptation and development. While previous studies have established a link between anxiety and impaired reversal learning ability, the underlying mechanisms behind this association remain unclear. This study employed a probabilistic reversal learning task with electroencephalographic recording to investigate these mechanisms. Participants were divided into two groups based on their scores on Spielberger's State-Trait Anxiety Inventory: high trait-anxiety (HTA) and low trait-anxiety (LTA), consisting of 50 individuals in each group. The results showed that the HTA group had poorer reversal learning performance than the LTA group, including a lower tendency to shift to the new optimal option after rule reversals (reversal-shift). The study also examined event-related potentials elicited by reversals and found that although the N1 (related to attention allocation), feedback-related negativity (FRN: related to belief updating), and P3 (related to response inhibition) were all sensitive to the grouping factor, only the FRN elicited by reversal-shift mediated the relationship between anxiety and the number/reaction time of reversal-shift. From these findings, we suggest that abnormalities in belief updating may contribute to the impaired reversal learning performance observed in anxious individuals. In our opinion, this study sheds light on potential targets for interventions aimed at improving behavioral flexibility in anxious individuals.

Detailed characterization of neural selectivity in free viewing primates

Fixation constraints in visual tasks are ubiquitous in visual and cognitive neuroscience. Despite its widespread use, fixation requires trained subjects, is limited by the accuracy of fixational eye movements, and ignores the role of eye movements in shaping visual input. To overcome these limitations, we developed a suite of hardware and software tools to study vision during natural behavior in untrained subjects. We measured visual receptive fields and tuning properties from multiple cortical areas of marmoset monkeys who freely viewed full-field noise stimuli. The resulting receptive fields and tuning curves from primary visual cortex (V1) and area MT match reported selectivity from the literature which was measured using conventional approaches. We then combined free viewing with high-resolution eye tracking to make the first detailed 2D spatiotemporal measurements of foveal receptive fields in V1. These findings demonstrate the power of free viewing to characterize neural responses in untrained animals while simultaneously studying the dynamics of natural behavior.

Environmental dynamics shape perceptual decision bias

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer's continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.

A reservoir of foraging decision variables in the mouse brain

In any given situation, the environment can be parsed in different ways to yield decision variables (DVs) defining strategies useful for different tasks. It is generally presumed that the brain only computes a single DV defining the current behavioral strategy. Here to test this assumption, we recorded neural ensembles in the frontal cortex of mice performing a foraging task admitting multiple DVs. Methods developed to uncover the currently employed DV revealed the use of multiple strategies and occasional switches in strategy within sessions. Optogenetic manipulations showed that the secondary motor cortex (M2) is needed for mice to use the different DVs in the task. Surprisingly, we found that regardless of which DV best explained the current behavior, M2 activity concurrently encoded a full basis set of computations defining a reservoir of DVs appropriate for alternative tasks. This form of neural multiplexing may confer considerable advantages for learning and adaptive behavior.

F = ma. Is the macaque brain Newtonian?

Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.

Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.

Neurophysiological mechanisms of error monitoring in human and non-human primates

Performance monitoring is an important executive function that allows us to gain insight into our own behaviour. This remarkable ability relies on the frontal cortex, and its impairment is an aspect of many psychiatric diseases. In recent years, recordings from the macaque and human medial frontal cortex have offered a detailed understanding of the neurophysiological substrate that underlies performance monitoring. Here we review the discovery of single-neuron correlates of error monitoring, a key aspect of performance monitoring, in both species. These neurons are the generators of the error-related negativity, which is a non-invasive biomarker that indexes error detection. We evaluate a set of tasks that allows the synergistic elucidation of the mechanisms of cognitive control across the two species, consider differences in brain anatomy and testing conditions across species, and describe the clinical relevance of these findings for understanding psychopathology. Last, we integrate the body of experimental facts into a theoretical framework that offers a new perspective on how error signals are computed in both species and makes novel, testable predictions.

Parametric control of flexible timing through low-dimensional neural manifolds

Biological brains possess an unparalleled ability to adapt behavioral responses to changing stimuli and environments. How neural processes enable this capacity is a fundamental open question. Previous works have identified two candidate mechanisms: a low-dimensional organization of neural activity and a modulation by contextual inputs. We hypothesized that combining the two might facilitate generalization and adaptation in complex tasks. We tested this hypothesis in flexible timing tasks where dynamics play a key role. Examining trained recurrent neural networks, we found that confining the dynamics to a low-dimensional subspace allowed tonic inputs to parametrically control the overall input-output transform, enabling generalization to novel inputs and adaptation to changing conditions. Reverse-engineering and theoretical analyses demonstrated that this parametric control relies on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds while preserving their geometry. Comparisons with data from behaving monkeys confirmed the behavioral and neural signatures of this mechanism.

Recurrent networks endowed with structural priors explain suboptimal animal behavior

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.

Flexible sensory-motor mapping rules manifest in correlated variability of stimulus and action codes across the brain

Humans and non-human primates can flexibly switch between different arbitrary mappings from sensation to action to solve a cognitive task. It has remained unknown how the brain implements such flexible sensory-motor mapping rules. Here, we uncovered a dynamic reconfiguration of task-specific correlated variability between sensory and motor brain regions. Human participants switched between two rules for reporting visual orientation judgments during fMRI recordings. Rule switches were either signaled explicitly or inferred by the participants from ambiguous cues. We used behavioral modeling to reconstruct the time course of their belief about the active rule. In both contexts, the patterns of correlations between ongoing fluctuations in stimulus- and action-selective activity across visual- and action-related brain regions tracked participants' belief about the active rule. The rule-specific correlation patterns broke down around the time of behavioral errors. We conclude that internal beliefs about task state are instantiated in brain-wide, selective patterns of correlated variability.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Neural cognitive signals during spontaneous movements in the macaque

The single-neuron basis of cognitive processing in primates has mostly been studied in laboratory settings where movements are severely restricted. It is unclear, therefore, how natural movements might affect neural signatures of cognition in the brain. Moreover, studies in mice indicate that body movements, when measured, account for most of the neural dynamics in the cortex. To examine these issues, we recorded from single-neuron ensembles in the prefrontal cortex in moving monkeys performing a cognitive task and characterized eye, head and body movements using video tracking. Despite considerable trial-to-trial movement variability, single-neuron tuning could be precisely measured and decision signals accurately decoded on a single-trial basis. Creating or abolishing spontaneous movements through head restraint and task manipulations had no measurable impact on neural responses. However, encoding models showed that uninstructed movements explained as much neural variance as task variables, with most movements aligned to task events. These results demonstrate that cognitive signals in the cortex are robust to natural movements, but also that unmeasured movements are potential confounds in cognitive neurophysiology experiments.

The thalamus in psychosis spectrum disorder

Psychosis spectrum disorder (PSD) affects 1% of the world population and results in a lifetime of chronic disability, causing devastating personal and economic consequences. Developing new treatments for PSD remains a challenge, particularly those that target its core cognitive deficits. A key barrier to progress is the tenuous link between the basic neurobiological understanding of PSD and its clinical phenomenology. In this perspective, we focus on a key opportunity that combines innovations in non-invasive human neuroimaging with basic insights into thalamic regulation of functional cortical connectivity. The thalamus is an evolutionary conserved region that forms forebrain-wide functional loops critical for the transmission of external inputs as well as the construction and update of internal models. We discuss our perspective across four lines of evidence: First, we articulate how PSD symptomatology may arise from a faulty network organization at the macroscopic circuit level with the thalamus playing a central coordinating role. Second, we discuss how recent animal work has mechanistically clarified the properties of thalamic circuits relevant to regulating cortical dynamics and cognitive function more generally. Third, we present human neuroimaging evidence in support of thalamic alterations in PSD, and propose that a similar "thalamocortical dysconnectivity" seen in pharmacological imaging (under ketamine, LSD and THC) in healthy individuals may link this circuit phenotype to the common set of symptoms in idiopathic and drug-induced psychosis. Lastly, we synthesize animal and human work, and lay out a translational path for biomarker and therapeutic development.

Goals, usefulness and abstraction in value-based choice

Colombian drug lord Pablo Escobar, while on the run, purportedly burned two million dollars in banknotes to keep his daughter warm. A stark reminder that, in life, circumstances and goals can quickly change, forcing us to reassess and modify our values on-the-fly. Studies in decision-making and neuroeconomics have often implicitly equated value to reward, emphasising the hedonic and automatic aspect of the value computation, while overlooking its functional (concept-like) nature. Here we outline the computational and biological principles that enable the brain to compute the usefulness of an option or action by creating abstractions that flexibly adapt to changing goals. We present different algorithmic architectures, comparing ideas from artificial intelligence (AI) and cognitive neuroscience with psychological theories and, when possible, drawing parallels.

Fast rule switching and slow rule updating in a perceptual categorization task

To adapt to a changing world, we must be able to switch between rules already learned and, at other times, learn rules anew. Often we must do both at the same time, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior, we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions.

Consensus Goals in the Field of Visual Metacognition

Despite the tangible progress in psychological and cognitive sciences over the last several years, these disciplines still trail other more mature sciences in identifying the most important questions that need to be solved. Reaching such consensus could lead to greater synergy across different laboratories, faster progress, and increased focus on solving important problems rather than pursuing isolated, niche efforts. Here, 26 researchers from the field of visual metacognition reached consensus on four long-term and two medium-term common goals. We describe the process that we followed, the goals themselves, and our plans for accomplishing these goals. If this effort proves successful within the next few years, such consensus building around common goals could be adopted more widely in psychological science.

Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task

Primates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesis is limited to behavioral models that do not emulate neural computations. Here, we test this hypothesis by directly comparing the behavior of primates (humans and monkeys) in a ball interception task to that of a large set of recurrent neural network (RNN) models with or without the capacity to dynamically track the underlying latent variables. Humans and monkeys exhibit similar behavioral patterns. This primate behavioral pattern is best captured by RNNs endowed with dynamic inference, consistent with the hypothesis that the primate brain uses dynamic inferences to support flexible physical predictions. Moreover, our work highlights a general strategy for using model neural systems to test computational hypotheses of higher brain function.

Age differences in the functional organization of the prefrontal cortex: analyses of competing hypotheses

We employed a mixed design task for block and event-related functional magnetic resonance imaging with manipulations of levels of abstraction and duration in task-relevant cues and probes. Age-related differences between younger and older adults in task-related functional brain activity patterns of the prefrontal cortex (PFC) were reported. The results showed that (1) the low episodic condition evoked more activity in the more anterior PFC than the high episodic control condition for both age groups; (2) the low abstraction condition evoked more activity in the more anterior PFC than the high abstraction condition for both age groups; and (3) the signal change did not vary as a function of activity dynamics (transient and sustained responses) and maintenance duration (single-trial and multiple-trial). The findings showed that baseline conditions evoked more activity in the more anterior PFC for the older group than the younger group across most task contrasts and conditions, where these additional activities in the brain regions overlapped within the default mode network (DMN). We tentatively concluded that deficiency in the anterior DMN deactivation during externally driven tasks might be attributed to less efficiency in modulating local connectivity propagate to surrounding tissue, which may paradoxically increase brain activity.

Controllability boosts neural and cognitive signatures of changes-of-mind in uncertain environments

In uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography, and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of one's own actions.

Cingulate-motor circuits update rule representations for sequential choice decisions

Anterior cingulate cortex mediates the flexible updating of an animal’s choice responses upon rule changes in the environment. However, how anterior cingulate cortex entrains motor cortex to reorganize rule representations and generate required motor outputs remains unclear. Here, we demonstrate that chemogenetic silencing of the terminal projections of cingulate cortical neurons in secondary motor cortex in the rat disrupts choice performance in trials immediately following rule switches, suggesting that these inputs are necessary to update rule representations for choice decisions stored in the motor cortex. Indeed, the silencing of cingulate cortex decreases rule selectivity of secondary motor cortical neurons. Furthermore, optogenetic silencing of cingulate cortical neurons that is temporally targeted to error trials immediately after rule switches exacerbates errors in the following trials. These results suggest that cingulate cortex monitors behavioral errors and updates rule representations in motor cortex, revealing a critical role for cingulate-motor circuits in adaptive choice behaviors.

The anterior cingulate cortex allows an animal to update its behaviour when the environment changes. In this work, the authors identify a pathway from cingulate to secondary motor cortex, critical for updating motor rules following behavioural errors.

Dynamic task-belief is an integral part of decision-making

Natural decisions involve two seemingly separable processes: inferring the relevant task (task-belief) and performing the believed-relevant task. The assumed separability has led to the traditional practice of studying task-switching and perceptual decision-making individually. Here, we used a novel paradigm to manipulate and measure macaque monkeys' task-belief and demonstrated inextricable neuronal links between flexible task-belief and perceptual decision-making. We showed that in animals, but not in artificial networks that performed as well or better than the animals, stronger task-belief is associated with better perception. Correspondingly, recordings from neuronal populations in cortical areas 7a and V1 revealed that stronger task-belief is associated with better discriminability of the believed-relevant, but not the believed-irrelevant, feature. Perception also impacts belief updating; noise fluctuations in V1 help explain how task-belief is updated. Our results demonstrate that complex tasks and multi-area recordings can reveal fundamentally new principles of how biology affects behavior in health and disease.