The Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment

Critical intelligence: Computing defensive behaviour

Characterising the mechanisms underlying naturalistic defensive behavior remains a significant challenge. While substantial progress has been made in unravelling the neural basis of tightly constrained behaviors, a critical gap persists in our comprehension of the circuits that implement algorithms capable of generating the diverse defensive responses observed outside experimental restrictions. Recent advancements in neuroscience technology now allow for an unprecedented examination of naturalistic behaviour. To help provide a theoretical grounding for this nascent experimental programme, we summarise the main computational and statistical challenges of defensive decision making, encapsulated in the concept of critical intelligence. Next, drawing from an extensive literature in biology, machine learning, and decision theory, we explore a range of candidate solutions to these challenges. While the proposed solutions offer insights into potential adaptive strategies, they also present inherent trade-offs and limitations in their applicability across different biological contexts. Ultimately, we propose series of experiments designed to differentiate between these candidate solutions, providing a roadmap for future investigations into the fundamental defensive algorithms utilized by biological agents and their neural implementation. Thus, our work aims to provide a roadmap towards broader understanding of how complex defensive behaviors are orchestrated in the brain, with implications for both neuroscience research and the development of more sophisticated artificial intelligence systems.

Choice anticipation as gated accumulation of sensory predictions

Predictions are combined with sensory information when making choices. Accumulator models have conceptualized predictions as trial-by-trial updates to a baseline evidence level. These models have been successful in explaining the influence of choice history across-trials, however, they do not account for how sensory information is transformed into choice evidence. Here, we derive a gated accumulator that models the onset of evidence accumulation as a combination of delayed sensory information and a prediction of sensory timing. To test how delays interact with predictions, we designed a free-choice saccade task where participants directed eye movements to either of two targets that appeared with variable delays and asynchronies. Despite instructions not to anticipate, participants responded before target onset on some trials. We reasoned that anticipatory responses reflected a trade-off between inhibiting and facilitating the onset of evidence accumulation via a gating mechanism as target appearance became more likely. We then found that anticipatory responses were more likely following repeated choices, suggesting that the balance between anticipatory and sensory responses was driven by a prediction of sensory timing. By fitting the gated accumulator model to the data, we found that variance in within-trial fluctuations in baseline evidence best explained the joint increase of anticipatory responses and faster sensory-guided responses with longer delays. Thus, we conclude that a prediction of sensory timing is involved in balancing the costs of anticipation with lowering the amount of accumulated evidence required to trigger saccadic choice.NEW & NOTEWORTHY Evidence accumulation models are used to study how recent history impacts the processes underlying how we make choices. Biophysical evidence suggests that the accumulation of evidence is gated, however, classic accumulator models do not account for this. In this work, we show that predictions of the timing of sensory information are important in controlling how evidence accumulation is gated and that signatures of these predictions can be detected even in randomized task environments.

Reaching vigor tracks learned prediction error

Movement vigor across multiple modalities increases with reward, suggesting that the neural circuits that represent value influence the control of movement. Dopaminergic neuron (DAN) activity in the basal ganglia has been suggested as the potential mediator of this response. If DAN activity is the bridge between value and vigor, then vigor should track canonical mediators of this activity, namely reward expectation and reward prediction error. Here we ask if a similar time-locked response is present in vigor of reaching movements. We explore this link by leveraging the known phasic dopaminergic response to stochastic rewards, where activity is modulated by both reward expectation at cue and the prediction error at feedback. We used probabilistic rewards to create a reaching task rich in reward expectation, reward prediction error, and learning. In one experiment, target reward probabilities were explicitly stated, and in the other, were left unknown and to be learned by the participants. We included two stochastic rewards (probabilities 33% and 66%) and two deterministic ones (probabilities 100% and 0%). Outgoing peak velocity in both experiments increased with increasing reward expectation. Furthermore, we observed a short-latency response in the vigor of the ongoing movement, that tracked reward prediction error: either invigorating or enervating velocity consistent with the sign and magnitude of the error. Reaching kinematics also revealed the value-update process in a trial-to-trial fashion, similar to the effect of prediction error signals typical in dopamine-mediated striatal phasic activity. Lastly, reach vigor increased with reward history over trials, mirroring the motivational effects often linked to fluctuating dopamine levels. Taken together, our results demonstrate and exquisite link between known short-latency reward signals and the invigoration of both discrete and ongoing movements.

Basal ganglia output (entopeduncular nucleus) coding of contextual kinematics and reward in the freely moving mouse

The entopeduncular nucleus (EPN) is often termed as one of the output nuclei of the basal ganglia owing to their highly convergent anatomy. The rodent EPN has been implicated in reward and value coding whereas the primate analog internal Globus Pallidus has been found to be modulated by some movements and in some circumstances. In this study, we sought to understand how the rodent EPN might be coding kinematic, reward, and difficulty parameters, particularly during locomotion. Furthermore, we aimed to understand the level of movement representation: whole-body or specific body parts. To this end, mice were trained in a freely moving two-alternative forced choice task with two periods of displacement (return and go trajectories) and performed electrophysiological recordings together with video-based tracking. We found (1) robust reward coding but not difficulty. (2) Spatio-temporal variables better explain EPN activity during movement compared to kinematic variables, while both types of variables were more robustly represented in reward-related movement. (3) Reward-sensitive units encode kinematics similarly to reward-insensitive ones. (4) Population dynamics that best account for differences between these two periods of movement can be explained by allocentric references like distance to reward port. (5) The representation of paw and licks is not mutually exclusive, discarding a somatotopic muscle-level representation of movement in the EPN. Our data suggest that EPN activity represents movements and reward in a complex way: highly multiplexed, influenced by the objective of the displacement, where trajectories that lead to reward better represent spatial and kinematic variables. Interestingly, there are intertwining representations of whole-body movement kinematics with a single paw and licking variables. Further, reward-sensitive units encode kinematics similarly to reward-insensitive ones, challenging the notion of distinct pathways for reward and movement processing.

Acting together: Cortex and striatum specify movement kinematics

In this issue of Neuron, Park et al.1 show that striatal activity is necessary for the specification of movement kinematics using a novel reach-to-pull task for mice. Through simultaneous cortical and subcortical recordings and manipulations, they demonstrate that motor cortex and striatum conjointly specify parameters necessary for shaping flexible, goal-directed actions.

Conjoint specification of action by neocortex and striatum

The interplay between two major forebrain structures-cortex and subcortical striatum-is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated, while the primary motor cortex is involved in specifying the continuous parameters of an upcoming/ongoing movement. Recent data indicate that striatum may also be involved in specification. These alternatives have been difficult to reconcile because comparing very distinct actions, as is often done, makes essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity across similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify continuous parameters governing movement execution.

Basal ganglia components have distinct computational roles in decision-making dynamics under conflict and uncertainty

The basal ganglia (BG) play a key role in decision-making, preventing impulsive actions in some contexts while facilitating fast adaptations in others. The specific contributions of different BG structures to this nuanced behavior remain unclear, particularly under varying situations of noisy and conflicting information that necessitate ongoing adjustments in the balance between speed and accuracy. Theoretical accounts suggest that dynamic regulation of the amount of evidence required to commit to a decision (a dynamic "decision boundary") may be necessary to meet these competing demands. Through the application of novel computational modeling tools in tandem with direct neural recordings from human BG areas, we find that neural dynamics in the theta band manifest as variations in a collapsing decision boundary as a function of conflict and uncertainty. We collected intracranial recordings from patients diagnosed with either Parkinson's disease (PD) (n = 14) or dystonia (n = 3) in the subthalamic nucleus (STN), globus pallidus internus (GPi), and globus pallidus externus (GPe) during their performance of a novel perceptual discrimination task in which we independently manipulated uncertainty and conflict. To formally characterize whether these task and neural components influenced decision dynamics, we leveraged modified diffusion decision models (DDMs). Behavioral choices and response time distributions were best characterized by a modified DDM in which the decision boundary collapsed over time, but where the onset and shape of this collapse varied with conflict. Moreover, theta dynamics in BG structures modulated the onset and shape of this collapse but differentially across task conditions. In STN, theta activity was related to a prolonged decision boundary (indexed by slower collapse and therefore more deliberate choices) during high conflict situations. Conversely, rapid declines in GPe theta during low conflict conditions were related to rapidly collapsing boundaries and expedited choices, with additional complementary decision bound adjustments during high uncertainty situations. Finally, GPi theta effects were uniform across conditions, with increases in theta associated with a prolongation of decision bound collapses. Together, these findings provide a nuanced understanding of how our brain thwarts impulsive actions while nonetheless enabling behavioral adaptation amidst noisy and conflicting information.

Survival in a world of complex dangers

How did our nomadic ancestors continually adapt to the seemingly limitless and unpredictable number of dangers in the natural world? We argue that human defensive behaviors are dynamically constructed to facilitate survival in capricious and itinerant environments. We first hypothesize that internal and external states result in state constructions that combine to form a meta-representation. When a threat is detected, it triggers the action construction. Action constructions are formed through two contiguous survival strategies: generalization strategies, which are used when encountering new threats and ecologies. Generalization strategies are associated with cognitive representations that have high dimensionality and which furnish flexible psychological constructs, including relations between threats, and imagination, and which converge through the construction of defensive states. We posit that generalization strategies drive 'explorative' behaviors including information seeking, where the goal is to increase knowledge that can be used to mitigate current and future threats. Conversely, specialization strategies entail lower dimensional representations, which underpin specialized, sometimes reflexive, or habitual survival behaviors that are 'exploitative'. Together, these strategies capture a central adaptive feature of human survival systems: self-preservation in response to a myriad of threats.

Task goals shape the relationship between decision and movement speed

The speed at which we move is linked to the speed at which we decide to make these movements. Yet the principles guiding such relationship remain unclear: whereas some studies point toward a shared invigoration process boosting decision and movement speed jointly, others rather indicate a trade-off between both levels of control, with slower movements accompanying faster decisions. Here, we aimed 1) at further investigating the existence of a shared invigoration process linking decision and movement and 2) at testing the hypothesis that such a link is masked when detrimental to the reward rate. To this aim, we tested 62 subjects who performed the Tokens task in two experiments (separate sessions): experiment 1 evaluated how changing decision speed affects movement speed, whereas experiment 2 assessed how changing movement speed affects decision speed. In the latter experiment, subjects were encouraged to favor either decision speed (fast decision group) or decision accuracy (slow decision group). Various mixed model analyses revealed a coregulation of decision (urgency) and movement speed in experiment 1 and in the fast decision group of experiment 2 but not in the slow decision group, despite the fact that these same subjects displayed a coregulation effect in experiment 1. Altogether, our findings support the idea that coregulation occurs as a default mode but that this form of control is diminished or supplanted by a trade-off relationship, contingent on reward rate maximization. Drawing from these behavioral observations, we propose that multiple processes contribute to shaping the speed of decisions and movements.NEW & NOTEWORTHY The principles guiding the relationship between decision and movement speed are still unclear. In the present behavioral study involving two experiments conducted with 62 human subjects, we report findings indicating a relationship that varies as a function of the task goals. Coregulation emerges as a default mode of control that fades when detrimental to the reward rate, possibly because of the influence of other processes that can selectively shape the speed of our decisions or movements.

Evidence for interacting but decoupled controls of decisions and movements in nonhuman primates

Many recent studies indicate that control of decisions and actions is integrated during interactive behavior. Among these, several carried out in humans and monkeys conclude that there is a coregulation of choices and movements. Another perspective, based on human data only, proposes a decoupled control of decision duration and movement speed, allowing, for instance, trading decision duration for movement duration when time pressure increases. Crucially, it is not currently known whether this ability to flexibly dissociate decision duration from movement speed is specific to humans, whether it can vary depending on the context in which a task is performed, and whether it is stable over time. These are important questions to address, especially to rely on monkey electrophysiology to infer the neural mechanisms of decision-action coordination in humans. To do so, we trained two macaque monkeys in a perceptual decision-making task and analyzed data collected over multiple behavioral sessions. Our findings reveal a strong and complex relationship between decision duration and movement vigor. Decision duration and action duration can covary but also "compensate" each other. Such integrated but decoupled control of decisions and actions aligns with recent studies in humans, validating the monkey model in electrophysiology as a means of inferring neural mechanisms in humans. Crucially, we demonstrate for the first time that this control can evolve with experience, in an adapted manner. Together, the present findings contribute to deepening our understanding of the integrated control of decisions and actions during interactive behavior.NEW & NOTEWORTHY The mechanism by which the integrated control of decisions and actions occurs, coupled or interactive but decoupled, is debated. In the present study, we show in monkeys that decisions and actions influence each other in a decoupled way. For the first time, we also demonstrate that this control can evolve depending the subject's experience, allowing the trade of movement time for decision time and limiting the temporal discounting of reward value.

Free will: An Example of the Dopaminergic System

Neuroscience has convinced people that much of their behavior is determined by causes unknown to them and beyond their control. However, are advances in neuroscience truly a prerequisite for such beliefs? Robert Kane's theory of ultimate responsibility is libertarian theory. Its innovative nature makes it possible to discuss the neurophysiological basis of its postulates. Using the functions of the midbrain dopaminergic system as an example, this article provides an overview of this neurophysiological basis. According to Kane, if we are to be ultimately responsible for our wills as well as for our actions, some actions in our lives must lack sufficient motives and causes. These are self-forming actions. Dopamine is hypothesized to mediate self-forming action execution. Dopamine not only mediates action but also ensures synaptic plasticity in the brain, that is, learning from action; hence, dopamine changes the acting individual and provides the formation of our own wills. The basal ganglia, which are the main target of dopamine in the brain, act through parallel pathways and are involved in decision-making processes. Dopamine is also involved in the regulation of the neurodynamical properties of prefrontal cortex networks with random spiking noise. It can be assumed that the activity of the dopaminergic system is closely related to the physiological basis of free will.

Rapid integration of face detection and task set in visually guided reaching

The superior colliculus (SC) has been increasingly implicated in the rapid processing of evolutionarily relevant stimuli like faces, but the behavioural relevance of such processing is unclear. The SC has also been implicated in the generation of express visuomotor responses (EVR), which are very short-latency (~80 ms) bursts of muscle activity time-locked to visual target presentation. These observations led us to investigate the influence of faces on EVRs. We recorded upper limb muscle activity from healthy participants as they reached toward targets in the presence of a distractor. In some experiments, faces were used as stimuli. Across blocks of trials, we varied the instruction as to which stimulus served as the target or distractor. Doing so allowed us to assess the impact of instruction on muscle recruitment given identical visual stimuli. We found that responses were uniquely modulated in tasks involving high-contrast faces, promoting reaches toward or away from a face depending on instruction. Follow-up experiments confirmed that the phenomenon required highly salient repeated faces and was not observed to non-facial stimuli nor to faces expressing different affects. This study extends the hypothesis that the SC mediates the EVR by demonstrating that faces impact muscle recruitment at short latencies that precede cortical activity for face perception. Our results constitute direct evidence for the behavioural relevance of face detection in the brainstem, and also implicate a role for top-down cortical pre-setting of the EVR depending on task context.

CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort.

Integrator dynamics in the cortico-basal ganglia loop underlie flexible motor timing

Flexible control of motor timing is crucial for behavior. Before volitional movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity in the cortico-basal ganglia loop may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviors, distinguishing their individual roles in this timer function remains challenging. To address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a flexible lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. These findings suggest the striatum is a part of the network that temporally integrates input from the frontal cortex and generates ramping activity that regulates motor timing.

C-SMB 2.0: Integrating over 25 years of motor sequencing research with the Discrete Sequence Production task

An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB 2.0), which brings together known models from cognitive psychology, cognitive neuroscience, and motor learning. This processing framework accounts for the many different behavioral results obtained with the DSP task and unveils important properties of the cognitive system. C-SMB 2.0 assumes that a versatile central processor (CP) develops multimodal, central-symbolic representations of short motor segments by repeatedly storing the elements of these segments in short-term memory (STM). Independently, the repeated processing by modality-specific perceptual and motor processors (PPs and MPs) and by the CP when executing sequences gradually associates successively used representations at each processing level. The high dependency of these representations on active context information allows for the rapid serial activation of the sequence elements as well as for the executive control of tasks as a whole. Speculations are eventually offered as to how the various cognitive processes could plausibly find their neural underpinnings within the intricate networks of the brain.

Cortical β Power Reflects a Neural Implementation of Decision Boundary Collapse in Speeded Decisions

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms underlying dynamic response thresholds remains significantly incomplete. To investigate this issue, we used a multistage drift-diffusion model (DDM) and also analyzed EEG β power lateralization (BPL). The latter served as a neural proxy for decision signals. We analyzed a large dataset (n = 863; 434 females and 429 males) from a speeded flanker task and data from an independent confirmation sample (n = 119; 70 females and 49 males). We showed that a DDM with collapsing decision thresholds, a process wherein the decision boundary reduces over time, captured participants' time-dependent decision policy more accurately than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak, coinciding with the motor response, may serve as a neural proxy for the decision threshold. We show that BPL around the response decreased with increasing RTs. Together, our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.

Clinical severity in Parkinson's disease is determined by decline in cortical compensation

Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

Effort cost of harvest affects decisions and movement vigor of marmosets during foraging

Our decisions are guided by how we perceive the value of an option, but this evaluation also affects how we move to acquire that option. Why should economic variables such as reward and effort alter the vigor of our movements? In theory, both the option that we choose and the vigor with which we move contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by choosing to work longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to an increased effort cost of harvest, marmosets extended their work duration, but slowed their movements. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and the vigor of licks and saccades increased. Thus, when acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.

Neural mechanisms for executive control of speed-accuracy trade-off

The medial frontal cortex (MFC) plays an important but disputed role in speed-accuracy trade-off (SAT). In samples of neural spiking in the supplementary eye field (SEF) in the MFC simultaneous with the visuomotor frontal eye field and superior colliculus in macaques performing a visual search with instructed SAT, during accuracy emphasis, most SEF neurons discharge less from before stimulus presentation until response generation. Discharge rates adjust immediately and simultaneously across structures upon SAT cue changes. SEF neurons signal choice errors with stronger and earlier activity during accuracy emphasis. Other neurons signal timing errors, covarying with adjusting response time. Spike correlations between neurons in the SEF and visuomotor areas did not appear, disappear, or change sign across SAT conditions or trial outcomes. These results clarify findings with noninvasive measures, complement previous neurophysiological findings, and endorse the role of the MFC as a critic for the actor instantiated in visuomotor structures.

Preserved neural dynamics across animals performing similar behaviour

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

Lost in time: Relocating the perception of duration outside the brain

It is well-accepted in neuroscience that animals process time internally to estimate the duration of intervals lasting between one and several seconds. More than 100 years ago, Henri Bergson nevertheless remarked that, because animals have memory, their inner experience of time is ever-changing, making duration impossible to measure internally and time a source of change. Bergson proposed that quantifying the inner experience of time requires its externalization in movements (observed or self-generated), as their unfolding leaves measurable traces in space. Here, studies across species are reviewed and collectively suggest that, in line with Bergson's ideas, animals spontaneously solve time estimation tasks through a movement-based spatialization of time. Moreover, the well-known scalable anticipatory responses of animals to regularly spaced rewards can be explained by the variable pressure of time on reward-oriented actions. Finally, the brain regions linked with time perception overlap with those implicated in motor control, spatial navigation and motivation. Thus, instead of considering time as static information processed by the brain, it might be fruitful to conceptualize it as a kind of force to which animals are more or less sensitive depending on their internal state and environment.

Cortical contribution to visuomotor coordination in locomotion and reaching

One of the hallmarks of mammals is their ability to make precise visually guided limb movements to attain objects. This is best exemplified by the reach and grasp movements of primates, although it is not unique to this mammalian order. Precise, coordinated, visually guided movements are equally as important during locomotion in many mammalian species, especially in predators. In this context, vision is used to guide paw trajectory and placement. In this review we examine the contribution of the fronto-parietal network in the control of such movements. We suggest that this network is responsible for visuomotor coordination across behaviours and species. We further argue for analogies between cytoarchitectonically similar cortical areas in primates and cats.

Contributions of the Basal Ganglia to Visual Perceptual Decisions

The basal ganglia (BG) make up a prominent nexus between visual and motor-related brain regions. In contrast to the BG's well-established roles in movement control and value-based decision making, their contributions to the transformation of visual input into an action remain unclear, especially in the context of perceptual decisions based on uncertain visual evidence. This article reviews recent progress in our understanding of the BG's contributions to the formation, evaluation, and adjustment of such decisions. From theoretical and experimental perspectives, the review focuses on four key stations in the BG network, namely, the striatum, pallidum, subthalamic nucleus, and midbrain dopamine neurons, which can have different roles and together support the decision process.

Dopamine regulates decision thresholds in human reinforcement learning in males

Dopamine fundamentally contributes to reinforcement learning, but recent accounts also suggest a contribution to specific action selection mechanisms and the regulation of response vigour. Here, we examine dopaminergic mechanisms underlying human reinforcement learning and action selection via a combined pharmacological neuroimaging approach in male human volunteers (n = 31, within-subjects; Placebo, 150 mg of the dopamine precursor L-dopa, 2 mg of the D2 receptor antagonist Haloperidol). We found little credible evidence for previously reported beneficial effects of L-dopa vs. Haloperidol on learning from gains and altered neural prediction error signals, which may be partly due to differences experimental design and/or drug dosages. Reinforcement learning drift diffusion models account for learning-related changes in accuracy and response times, and reveal consistent decision threshold reductions under both drugs, in line with the idea that lower dosages of D2 receptor antagonists increase striatal DA release via an autoreceptor-mediated feedback mechanism. These results are in line with the idea that dopamine regulates decision thresholds during reinforcement learning, and may help to bridge action selection and response vigor accounts of dopamine.

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.

Motor Decision-Making as a Common Denominator in Motor Pathology and a Possible Rehabilitation Target

Despite the substantial progress in motor rehabilitation, patient involvement and motivation remain major challenges. They are typically addressed with communicational and environmental strategies, as well as with improved goal-setting procedures. Here we suggest a new research direction and framework involving Neuroeconomics principles to investigate the role of Motor Decision-Making (MDM) parameters in motivational component and motor performance in rehabilitation. We argue that investigating NE principles could bring new approaches aimed at increasing active patient engagement in the rehabilitation process by introducing more movement choice, and adapting existing goal-setting procedures. We discuss possible MDM implementation strategies and illustrate possible research directions using examples of stroke and psychiatric disorders.

Evidence for a common mechanism supporting invigoration of action selection and action execution

The speed, or vigor, of our movements can vary depending on circumstances. For instance, the promise of a reward leads to faster movements. Reward also leads us to move with a lower reaction time, suggesting that the process of action selection can also be invigorated by reward. It has been proposed that invigoration of action selection and of action execution might occur through a common mechanism, and thus these aspects of behavior might be coupled. To test this hypothesis, we asked participants to make reaching movements to "shoot" through a target at varying speeds to assess whether moving more quickly was also associated with more rapid action selection. We found that, when participants were required to move with a lower velocity, the speed of their action selection was also significantly slowed. This finding was recapitulated in a further dataset in which participants determined their own movement speed, but had to move slowly to stop their movement inside the target. By reanalyzing a previous dataset, we also found evidence for the converse relationship between action execution and action selection; when pressured to select actions more rapidly, people also executed movements with higher velocity. Our results establish that invigoration of action selection and action execution vary in tandem with one another, supporting the hypothesis of a common underlying mechanism.NEW & NOTEWORTHY We show that voluntary increases in the vigor of action execution lead action selection to also occur more rapidly. Conversely, hastening action selection by imposing a deadline to act also leads to increases in movement speed. These findings provide evidence that these two distinct aspects of behavior are modulated by a common underlying mechanism.

Effort cost of harvest affects decisions and movement vigor of marmosets during foraging

Our decisions are guided by how we perceive the value of an option, but this evaluation also affects how we move to acquire that option. Why should economic variables such as reward and effort alter the vigor of our movements? In theory, both the option that we choose and the vigor with which we move contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by choosing to work longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to an increased effort cost of harvest, marmosets extended their work duration, but slowed their movements. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and the vigor of licks and saccades increased. Thus, when acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.

Significance statement

Our results suggest that as the brainstem neuromodulatory circuits that control pupil size respond to effort costs, they alter computations in the brain regions that control decisions, encouraging work and delaying gratification, and the brain regions that control movements, reducing vigor and suppressing energy expenditure. This coordinated response suggests that decisions and actions are part of a single control policy that aims to maximize a variable relevant to fitness: the capture rate.

Movement characteristics impact decision-making and vice versa

Previous studies suggest that humans are capable of coregulating the speed of decisions and movements if promoted by task incentives. It is unclear however whether such behavior is inherent to the process of translating decisional information into movements, beyond posing a valid strategy in some task contexts. Therefore, in a behavioral online study we imposed time constraints to either decision- or movement phases of a sensorimotor task, ensuring that coregulating decisions and movements was not promoted by task incentives. We found that participants indeed moved faster when fast decisions were promoted and decided faster when subsequent finger tapping movements had to be executed swiftly. These results were further supported by drift diffusion modelling and inspection of psychophysical kernels: Sensorimotor delays related to initiating the finger tapping sequence were shorter in fast-decision as compared to slow-decision blocks. Likewise, the decisional speed-accuracy tradeoff shifted in favor of faster decisions in fast-tapping as compared to slow-tapping blocks. These findings suggest that decisions not only impact movement characteristics, but that properties of movement impact the time taken to decide. We interpret these behavioral results in the context of embodied decision-making, whereby shared neural mechanisms may modulate decisions and movements in a joint fashion.

Pupillary dynamics reflect the impact of temporal expectation on detection strategy

Everyday life's perceptual decision-making is informed by experience. In particular, temporal expectation can ease the detection of relevant events in noisy sensory streams. Here, we investigated if humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream. To understand what neural mechanisms implement temporal expectation influence on decision-making, we used pupillometry as a proxy for underlying neuromodulatory activity. We found that participants' detection strategy was influenced by the hidden temporal context and correlated with sound-evoked pupil dilation. A model of urgency fitted on false alarms predicted detection reaction time. Altogether, these findings suggest that temporal expectation informs decision-making and could be implemented through neuromodulatory-mediated urgency signals.

Computational analysis of speed-accuracy tradeoff

Speed-accuracy tradeoff (SAT) in the decision making of humans and animals is a well-documented phenomenon, but its underlying neuronal mechanism remains unclear. Modeling approaches have conceptualized SAT through the threshold hypothesis as adjustments to the decision threshold. However, the leading neurophysiological view is the gain modulation hypothesis. This hypothesis postulates that the SAT mechanism is implemented through changes in the dynamics of the choice circuit, which increase the baseline firing rate and the speed of neuronal integration. In this paper, I investigated alternative computational mechanisms of SAT and showed that the threshold hypothesis was qualitatively consistent with the behavioral data, but the gain modulation hypothesis was not. In order to reconcile the threshold hypothesis with the neurophysiological evidence, I considered the interference of alpha oscillations with the decision process and showed that alpha oscillations could increase the discriminatory power of the decision system, although they slowed down the decision process. This suggests that the magnitude of alpha waves suppression during the event related desynchronization (ERD) of alpha oscillations depends on a SAT condition and the amplitude of alpha oscillations is lower in the speed condition. I also showed that the lower amplitude of alpha oscillations resulted in an increase in the baseline firing rate and the speed of neuronal intergration. Thus, the interference of the event related desynchronization of alpha oscillations with a SAT condition explains why an increase in the baseline firing rate and the speed of neuronal integration accompany the speed condition.

Saccade vigor reflects the rise of decision variables during deliberation

During deliberation, as we quietly consider our options, the neural activities representing the decision variables that reflect the goodness of each option rise in various regions of the cerebral cortex.1,2,3,4,5,6,7 If the options are depicted visually, we make saccades, focusing gaze on each option. Do the kinematics of these saccades reflect the state of the decision variables? To test this idea, we engaged human participants in a decision-making task in which they considered two effortful options that required walking across various distances and inclines. As they deliberated, they made saccades between the symbolic representations of their options. These deliberation period saccades had no bearing on the effort they would later expend, yet saccade velocities increased gradually and differentially: the rate of rise was faster for saccades toward the option that they later indicated as their choice. Indeed, the rate of rise encoded the difference in the subjective value of the two options. Importantly, the participants did not reveal their choice at the conclusion of deliberation, but rather waited during a delay period, and finally expressed their choice by making another saccade. Remarkably, vigor for this saccade dropped to baseline and no longer encoded subjective value. Thus, saccade vigor appeared to provide a real-time window to the otherwise hidden process of option evaluation during deliberation.

Integrated neural dynamics of sensorimotor decisions and actions

Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional "decision manifold" defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution.

Dynamic control of decision and movement speed in the human basal ganglia

To optimally adjust our behavior to changing environments we need to both adjust the speed of our decisions and movements. Yet little is known about the extent to which these processes are controlled by common or separate mechanisms. Furthermore, while previous evidence from computational models and empirical studies suggests that the basal ganglia play an important role during adjustments of decision-making, it remains unclear how this is implemented. Leveraging the opportunity to directly access the subthalamic nucleus of the basal ganglia in humans undergoing deep brain stimulation surgery, we here combine invasive electrophysiological recordings, electrical stimulation and computational modelling of perceptual decision-making. We demonstrate that, while similarities between subthalamic control of decision- and movement speed exist, the causal contribution of the subthalamic nucleus to these processes can be disentangled. Our results show that the basal ganglia independently control the speed of decisions and movement for each hemisphere during adaptive behavior.

Normative decision rules in changing environments

Models based on normative principles have played a major role in our understanding of how the brain forms decisions. However, these models have typically been derived for simple, stable conditions, and their relevance to decisions formed under more naturalistic, dynamic conditions is unclear. We previously derived a normative decision model in which evidence accumulation is adapted to fluctuations in the evidence-generating process that occur during a single decision (Glaze et al., 2015), but the evolution of commitment rules (e.g. thresholds on the accumulated evidence) under dynamic conditions is not fully understood. Here, we derive a normative model for decisions based on changing contexts, which we define as changes in evidence quality or reward, over the course of a single decision. In these cases, performance (reward rate) is maximized using decision thresholds that respond to and even anticipate these changes, in contrast to the static thresholds used in many decision models. We show that these adaptive thresholds exhibit several distinct temporal motifs that depend on the specific predicted and experienced context changes and that adaptive models perform robustly even when implemented imperfectly (noisily). We further show that decision models with adaptive thresholds outperform those with constant or urgency-gated thresholds in accounting for human response times on a task with time-varying evidence quality and average reward. These results further link normative and neural decision-making while expanding our view of both as dynamic, adaptive processes that update and use expectations to govern both deliberation and commitment.

Clinical neuroscience and neurotechnology: An amazing symbiosis

In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.

Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease

Disruption of subthalamic nucleus dynamics in Parkinson's disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson's disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson's disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson's disease.

The effect of self-generated versus externally generated actions on timing, duration, and amplitude of blood oxygen level dependent response for visual feedback processing

It has been widely assumed that internal forward models use efference copies to create predictions about the sensory consequences of our own actions. While these predictions have frequently been associated with a reduced blood oxygen level dependent (BOLD) response in sensory cortices, the timing and duration of the hemodynamic response for the processing of video feedback of self‐generated (active) versus externally generated (passive) movements is poorly understood. In the present study, we tested the hypothesis that predictive mechanisms for self‐generated actions lead to early and shorter neural processing compared with externally generated movements. We investigated active and passive movements using a custom‐made fMRI‐compatible movement device. Visual video feedback of the active and passive movements was presented in real time or with variable delays. Participants had to judge whether the feedback was delayed. Timing and duration of BOLD impulse response was calculated using a first (temporal derivative [TD]) and second‐order (dispersion derivative [DD]) Taylor approximation. Our reanalysis confirmed our previous finding of reduced BOLD response for active compared to passive movements. Moreover, we found positive effects of the TD and DD in the supplementary motor area, cerebellum, visual cortices, and subcortical structures, indicating earlier and shorter hemodynamic responses for active compared to passive movements. Furthermore, earlier activation in the putamen for active compared to passive conditions was associated with reduced delay detection performance. These findings indicate that efference copy‐based predictive mechanisms enable earlier processing of action feedback, which might have reduced the ability to detect short delays between action and feedback.

Oscillatory waveform sharpness asymmetry changes in motor thalamus and motor cortex in a rat model of Parkinson's disease

Parkinson's disease (PD) causes bursty and oscillatory activity in basal ganglia output that is thought to contribute to movement deficits through impact on motor thalamus and motor cortex (MCx). We examined the effect of dopamine loss on motor thalamus and motor cortex activity by recording neuronal and LFP activities in ventroanterior-ventrolateral (VAVL) thalamus and MCx in urethane-anesthetised control and parkinsonian rats. Dopamine lesion decreased the firing rate and increased the bursting of putative pyramidal neurons in layer V, but not layer VI, of the MCx without changing other aspects of firing pattern. In contrast, dopamine lesion did not affect VAVL firing rate, pattern or low threshold calcium spike bursts. Slow-wave (~1 Hz) oscillations in LFP recordings were analyzed with conventional power and waveform shape analyses. While dopamine lesion did not influence total power, it was consistently associated with an increase in oscillatory waveform sharpness asymmetry (i.e., sharper troughs vs. peaks) in both motor thalamus and MCx. Furthermore, we found that measures of sharpness asymmetry were positively correlated in paired motor thalamus-MCx recordings, and that correlation coefficients were larger in dopamine lesioned rats. These data support the idea that dysfunctional MCx activity in parkinsonism emerges from subsets of cell groups (e.g. layer V pyramidal neurons) and is evident in the shape but not absolute power of slow-wave oscillations. Hypoactive layer V pyramidal neuron firing in dopamine lesioned rats is unlikely to be driven by VAVL thalamus and may, therefore, reflect the loss of mesocortical dopaminergic afferents and/or changes in intrinsic excitability.

Neural Algorithms and Circuits for Motor Planning

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

Identifying control ensembles for information processing within the cortico-basal ganglia-thalamic circuit

In situations featuring uncertainty about action-reward contingencies, mammals can flexibly adopt strategies for decision-making that are tuned in response to environmental changes. Although the cortico-basal ganglia thalamic (CBGT) network has been identified as contributing to the decision-making process, it features a complex synaptic architecture, comprised of multiple feed-forward, reciprocal, and feedback pathways, that complicate efforts to elucidate the roles of specific CBGT populations in the process by which evidence is accumulated and influences behavior. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how variations in CBGT network properties, including subpopulation firing rates and synaptic weights, map to variability of parameters in a normative drift diffusion model (DDM), representing algorithmic aspects of information processing during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles within the CBGT network that impact specific qualities of the emergent decision policy: responsiveness (a measure of how quickly evidence evaluation gets underway, associated with overall activity in corticothalamic and direct pathways), pliancy (a measure of the standard of evidence needed to commit to a decision, associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (a measure of commitment toward one available option, associated with differences in direct and indirect pathways across action channels). These analyses provide mechanistic predictions about the roles of specific CBGT network elements in tuning the way that information is accumulated and translated into decision-related behavior.

Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost

Finding the right amount of deliberation, between insufficient and excessive, is a hard decision making problem that depends on the value we place on our time. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the opportunity cost of time, including deliberation time. Importantly, this cost can itself vary with the environmental context and is not trivial to estimate. Here, we propose how the opportunity cost of deliberation can be estimated adaptively on multiple timescales to account for non-stationary contextual factors. We use it in a simple decision-making heuristic based on average-reward reinforcement learning (AR-RL) that we call Performance-Gated Deliberation (PGD). We propose PGD as a strategy used by animals wherein deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We show PGD outperforms AR-RL solutions in explaining behaviour and urgency of non-human primates in a context-varying random walk prediction task and is consistent with relative performance and urgency in a context-varying random dot motion task. We make readily testable predictions for both neural activity and behaviour.

Dopamine Modulation of Drosophila Ellipsoid Body Neurons, a Nod to the Mammalian Basal Ganglia

The central complex (CX) is a neural structure located on the midline of the insect brain that has been widely studied in the last few years. Its role in navigation and goal-oriented behaviors resembles those played by the basal ganglia in mammals. However, the neural mechanisms and the neurotransmitters involved in these processes remain unclear. Here, we exploited an in vivo bioluminescence Ca²⁺ imaging technique to record the activity in targeted neurons of the ellipsoid body (EB). We used different drugs to evoke excitatory Ca²⁺-responses, depending on the putative neurotransmitter released by their presynaptic inputs, while concomitant dopamine administration was employed to modulate those excitations. By using a genetic approach to knockdown the dopamine 1-like receptors, we showed that different dopamine modulatory effects are likely due to specific receptors expressed by the targeted population of neurons. Altogether, these results provide new data concerning how dopamine modulates and shapes the response of the ellipsoid body neurons. Moreover, they provide important insights regarding the similitude with mammals as far as the role played by dopamine in increasing and stabilizing the response of goal-related information.

Evidence integration and decision confidence are modulated by stimulus consistency

Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise.

Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making

Nervous systems evolved to effectively navigate the dynamics of the environment to achieve their goals. One framework used to study this fundamental problem arose in the study of learning and decision-making. In this framework, the demands of effective behavior require slow dynamics-on the scale of seconds to minutes-of networks of neurons. Here, we review the phenomena and mechanisms involved. Using vignettes from a few species and areas of the nervous system, we view neuromodulators as key substrates for temporal scaling of neuronal dynamics. Expected final online publication date for the Annual Review of Neuroscience, Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Hasty sensorimotor decisions rely on an overlap of broad and selective changes in motor activity

Humans and other animals are able to adjust their speed–accuracy trade-off (SAT) at will depending on the urge to act, favoring either cautious or hasty decision policies in different contexts. An emerging view is that SAT regulation relies on influences exerting broad changes on the motor system, tuning its activity up globally when hastiness is at premium. The present study aimed to test this hypothesis. A total of 50 participants performed a task involving choices between left and right index fingers, in which incorrect choices led either to a high or to a low penalty in 2 contexts, inciting them to emphasize either cautious or hasty policies. We applied transcranial magnetic stimulation (TMS) on multiple motor representations, eliciting motor-evoked potentials (MEPs) in 9 finger and leg muscles. MEP amplitudes allowed us to probe activity changes in the corresponding finger and leg representations, while participants were deliberating about which index to choose. Our data indicate that hastiness entails a broad amplification of motor activity, although this amplification was limited to the chosen side. On top of this effect, we identified a local suppression of motor activity, surrounding the chosen index representation. Hence, a decision policy favoring speed over accuracy appears to rely on overlapping processes producing a broad (but not global) amplification and a surround suppression of motor activity. The latter effect may help to increase the signal-to-noise ratio of the chosen representation, as supported by single-trial correlation analyses indicating a stronger differentiation of activity changes in finger representations in the hasty context.

Many have argued that the regulation of the speed-accuracy tradeoff relies on an urgency signal, which implements "collapsing decision thresholds" by tuning neural activity in a global manner in decision-related structures. This study indicates that the reality is more subtle, with several aspects of "urgency" being specifically targeted to particular corticospinal populations within the motor system.

Evolution of behavioural control from chordates to primates

This article outlines a hypothetical sequence of evolutionary innovations, along the lineage that produced humans, which extended behavioural control from simple feedback loops to sophisticated control of diverse species-typical actions. I begin with basic feedback mechanisms of ancient mobile animals and follow the major niche transitions from aquatic to terrestrial life, the retreat into nocturnality in early mammals, the transition to arboreal life and the return to diurnality. Along the way, I propose a sequence of elaboration and diversification of the behavioural repertoire and associated neuroanatomical substrates. This includes midbrain control of approach versus escape actions, telencephalic control of local versus long-range foraging, detection of affordances by the dorsal pallium, diversified control of nocturnal foraging in the mammalian neocortex and expansion of primate frontal, temporal and parietal cortex to support a wide variety of primate-specific behavioural strategies. The result is a proposed functional architecture consisting of parallel control systems, each dedicated to specifying the affordances for guiding particular species-typical actions, which compete against each other through a hierarchy of selection mechanisms. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.

How do humans flexibly adapt action sequences? For instance, musicians can learn a song and quickly speed up or slow down the tempo, or even play the song following a completely different rhythm (e.g., a rock song using a bossa nova rhythm). In this work, we build a biologically plausible network of cortico-basal ganglia interactions that explains how this temporal flexibility may emerge in the brain. Crucially, our model factorizes sequence order and action timing, respectively represented in cortical and basal ganglia dynamics. This factorization allows full temporal flexibility, i.e. the timing of a learned action sequence can be recomposed without interfering with the order of the sequence. As such, our model is capable of learning asynchronous action sequences, and flexibly shift, rescale, and recompose them, while accounting for biological data.

Reinforcement-learning in fronto-striatal circuits

We review the current state of knowledge on the computational and neural mechanisms of reinforcement-learning with a particular focus on fronto-striatal circuits. We divide the literature in this area into five broad research themes: the target of the learning-whether it be learning about the value of stimuli or about the value of actions; the nature and complexity of the algorithm used to drive the learning and inference process; how learned values get converted into choices and associated actions; the nature of state representations, and of other cognitive machinery that support the implementation of various reinforcement-learning operations. An emerging fifth area focuses on how the brain allocates or arbitrates control over different reinforcement-learning sub-systems or "experts". We will outline what is known about the role of the prefrontal cortex and striatum in implementing each of these functions. We then conclude by arguing that it will be necessary to build bridges from algorithmic level descriptions of computational reinforcement-learning to implementational level models to better understand how reinforcement-learning emerges from multiple distributed neural networks in the brain.