Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Decision-making in sensorimotor control


Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.


Real-world tasks typically involve a sequence of actions that are performed to achieve a high-level goal. Such tasks engage decision-making processes that determine which movement to make next and when to make it, how those movements that are selected are planned and controlled and how movement and task goals are dynamically updated in response to changes in the world.

As an example of the inherent links between decision-making and action planning, consider a tennis player. Even at the level of planning and executing a single movement (such as a backhand aimed up the line), substantial decision-making is involved. Specifically, the player must decide how to coordinate and execute the shot to trade off reward (shot success) and costs (energy and injury). The first section of this Review will discuss how various costs and rewards shape the planning and execution of single movements associated with a single goal.

At another level of decision-making, the player must choose, often under time pressure, which of many possible movements they should perform. This choice involves selecting where to aim their shot (short or long, for example) and which type of shot to perform (such as a drop shot or lob). In the second section of this Review, we discuss how the brain plans and executes movements when presented with multiple potential targets or goals (and thus multiple potential movements) and how this is affected when there is limited time to choose between them.

As the player prepares to execute a given shot, having selected a movement goal, their opponent’s actions (such as running to the net) might force a revision of the goal. In the third section, we will discuss how information about choice options that arrives over time can result in modification of the goal and hence the ongoing movement.

Employing decision-making at a strategic level, a player will often prepare a sequence of movements designed to ultimately win the point (they may serve wide, move to the net and volley to the open court, for example). In our final section, we will discuss how decisions related to a sequence of movements tend to optimize performance across a task and maximize the extraction of task-relevant information.

In this Review, we discuss how decision-making processes involved in the shaping, selection, revision and sequencing of movement operate to guide sensorimotor behaviour. Whereas traditional models and theories have viewed the processes of decision-making as being distinct from those involved in action planning and control, a key theme to emerge from this Review is that decision-making, in the context of action, dynamically interacts with the sensorimotor system at multiple levels. In exploring this theme, and integrating insights gained from behavioural, neurophysiological and computational approaches, we hope to provide insight into the bases on which sensorimotor decisions are formed and implemented as well as constraints on biologically plausible models of decision-making.

Movements directed to a single goal

Building on the seminal work of Woodworth1, the generation of purposeful movement has traditionally been conceptualized as involving two distinct phases: a pre-movement planning phase, in which key parameters of the upcoming action are specified and readied for implementation, and a separate control phase, in which online corrective processes fine-tune the movement to ensure successful completion2,3,4. When researchers began developing quantitative models of goal-directed actions in the 1980s, the initial focus of this work was on movement planning. In particular, a number of models were developed to address what is known as the problem of redundancy — the fact that the goal of an action, such as to grasp a cup, can often be achieved with any number of different movement trajectories. One way of resolving this problem is to select motor commands that minimize some cost — a scalar measure that characterizes some attribute of a particular movement. In the context of target-directed reaching movements, a number of such costs have been proposed, including the variability of the final hand position (across repeated movements)5 and the jerkiness (changes in acceleration) of the trajectory of the hand during the reach6. Similarly, it has been shown that motor commands can be selected to optimize explicit reward. For example, when pointing towards a rewarding target surrounded by penalty areas, people choose an aim location that optimizes reward while taking into account natural movement variability7,8.

These early models focused on feedforward planning and often assumed that such planning involves specifying a desired trajectory (for example, a trajectory in which jerkiness is minimized and the hand moves smoothly in a straight line to the target) that the motor system attempts to generate. However, they tended to put little emphasis on the control processes through which ongoing sensory feedback is used to guide action. More recent accounts of goal-directed movement, such as optimal feedback control (OFC) models9,10,11, emphasize an important role for feedback control in target-directed reaching. According to such models, controlling a movement involves the selection of a ‘control policy’ that governs how sensory feedback will be used in real time to generate motor commands. For example, the control policy will specify ‘feedback gains’ that determine how robustly the motor system will respond to mismatches between the current state of the arm and the final goal state. In addition to shaping how the arm is driven towards the goal, these gains determine how the system responds to errors that may arise owing to natural variability in motor commands12 or external perturbations13. The control policy thus determines how the movement will evolve as a function of the state of the motor system and how the motor system will handle errors.

It is important to note that the principle of optimization is still central to OFC models. The parameters of the control policy are selected to minimize a cost, which is typically defined as a combination of energy expenditure and inaccuracy11. However, the traditional distinction between movement planning and movement control is blurred in these models because the control policy can specify, on the basis of the initial state of the system and the goal state, the motor commands involved in initiating movement. According to OFC models, movement planning is concerned with the specification of feedback gains and movement control is the use of these feedback gains to drive movement.

A critical feature of OFC models is the concept of ‘minimum intervention’, whereby sensory feedback is used to correct movement errors that interfere with the goal of the action but not errors that are irrelevant to the goal. Intervening to correct for errors that do not threaten the goal is undesirable because such intervention will generally require increased effort and add noise into the system. The prediction of the minimum intervention principle also provides an opportunity to directly test desired trajectory models against OFC models. Imagine that your hand is bumped sideways when reaching towards either a small knob or a wide lever that opens a door. The OFC model predicts a stronger lateral correction of the hand movement when reaching towards the knob in comparison to the lever because the goal of opening the door can still be achieved by contacting the lever at a different location than might have originally been planned. However, desired trajectory models predict equally strong corrections in both of these two contexts because the putative aim is to follow a particular trajectory. Experimental tests along these lines have provided evidence for the minimum intervention principle and hence support for OFC models14,15 (Fig. 1a).

Fig. 1: Corrective motor responses are tuned to task features.

a | In this task, participants were required to perform target-directed reaching movements using a robotic interface. On some trials, small mechanical perturbations were applied to the limb during the task. Traces show the paths of individual hand movements, recorded using the robotic interface15. Unperturbed movements to narrow or wide targets tend to be straight and to move to the closest point on the target. However, the application of mechanical loads immediately after movement onset disrupts execution of the planned movement. The resulting hand-movement paths obey the principle of minimum intervention. That is, for a narrow target, the hand paths correct to enable the participant to reach the target. For a wide target, no correction is necessary and the hand just reaches to another point on the target. b,c | Visual attention influences the processing of target but not hand-movement information16. In this experiment, subjects performed a bimanual reach task, using a robotic interface to move two cursors to their respective targets. During the task, one of the cursors or one of the targets was displaced and the corrective reflex response was measured as the lateral force applied to the robotic interface by the participant. In addition, immediately after the onset of movement, a flash of light could draw attention either towards (perturbation attended) or away from (perturbation unattended) the side of the perturbation (part b). There was no significant difference in the onset of the lateral corrective forces that occurred in response to cursor displacement when flashes were on the attended versus non-attended sides (part c, left panel); however, there was a significant delay in the corrective response to target displacement when visual attention was drawn away from the side of the perturbation (part c, right panel). Vertical dashed lines represent the time window over which the corrective forces were averaged to obtain an estimate of the strength of reflex response (from 30 ms before to 70 ms after response onset). Grey horizontal line represents zero lateral force. Parts b and c show that distracting visual information is more efficiently filtered during the extraction of hand information than it is during the extraction of target information, suggesting a specialized mechanism that links representation of the hand in visual and motor systems. Part a is adapted with permission from ref.164, Elsevier. Parts b and c are adapted with permission from ref.16, Elsevier.

The ability to respond quickly to perturbations is critical for skilled and dexterous motor behaviour, and a recent focus of work in the field has been to examine the nature and timing of the sensorimotor mechanisms that lead to movement corrections based on visual16,17, proprioceptive15,18 and even tactile19 feedback. A common approach used in this work to study the rapid updating of goal-directed limb movements is to examine how human participants respond to small mechanical perturbations of the limb20. These studies have shown that in addition to spinal-generated corrective responses (termed R1 responses), which reflect simple muscle stretch reflexes and can be observed in electromyogram (EMG) activity within 25–50 ms of the perturbation21, mechanical perturbations also give rise to a second phase of EMG corrective responses, termed R2 responses. R2 responses occur 60 ms after the perturbation and, consistent with being transcortical in nature, can exhibit remarkable sophistication: they have been shown to reflect the physics of the limb and environment22,23,24 as well as features of the target, such as size15. Evidence suggests that R2 responses are in continuous operation, as they occur even for very small disturbances in limb position that are close to the natural variability of limb motion25. In bimanual motor tasks, in which one hand is perturbed, rapid R2 responses can also be seen on the unperturbed hand, albeit with a small delay (~10 ms), indicating the fast integration and coordination of sensory feedback processing across the cortical hemispheres26,27.

When considering the types of sensory information used in rapid movement corrections, the role of visual feedback has received considerable attention. One way in which this has been typically studied has been to examine how the availability or removal of the ability to see the hand at certain times before or during target-directed reaching affects movement direction accuracy and the reach trajectory28,29,30. A general theme to emerge from this work is that early (initial) and late (terminal) components of a reach movement are differentially affected by the removal of visual feedback, suggesting that these components are governed by distinct subprocesses31,32. Other studies have examined cases in which, during the reach movement, either the reach target is displaced6,33 or a visual representation of the hand’s position (such as a cursor) is altered34. Although both of these manipulations in humans result in rapid corrective responses (within ~160 ms)35,36, the influence of the presence of distractors on the corrections made for each type of perturbation suggest that separate mechanisms are involved in processing the two types of visual information16,37 (Fig. 1b,c). Specifically, the fact that corrections to displacements of the hand cursor are relatively unaffected by distractors, in comparison to corrections to displacement of the target, may indicate that there is a dedicated allocation of attentional resources to visual feedback processing of the hand’s position16,37. This distinction may also reflect, in part, the availability of proprioceptive feedback for the limb that can be used in estimating cursor position, but that is not similarly available for estimating target position.

Choosing between competing action goals

In many everyday actions, we must select a particular target from among multiple alternatives. According to traditional serial models of action planning38,39, we first select the target object and only then specify and prepare the corresponding goal-directed movement. However, this serial processing view has been challenged by both behavioural and neurophysiological evidence suggesting that these two processes — selection and specification — can operate continuously and in parallel40.

Some of the earliest evidence for this idea stems from behavioural findings showing that the movement trajectories of individuals reaching towards targets will, depending on the context, initially deviate towards non-target stimuli placed nearby41,42,43,44. This finding suggests that, in some cases, movement specification can precede target selection. Consistent with this idea, neurophysiological studies in humans and nonhuman primates have shown that competing reach targets elicit separate neural representations in sensorimotor brain areas before one of the targets being selected45,46,47,48,49. For example, it has been demonstrated that when there is uncertainty about which of two rules — each specifying a particular movement goal — would be applied to a single spatial cue, neurons in frontoparietal reach areas simultaneously represent both movement goals before rule specification50 (Fig. 2a). Notably, this was despite the fact that the sequence of events during the task afforded the animals the possibility of first waiting for the rule to be specified and only then representing the single corresponding movement associated with that rule.

Fig. 2: Multiple potential actions can be specified before target selection.

a | Schematic depiction of an experiment measuring neural activity in the parietal cortex of a monkey taking part in a potential motor goal task50. In this task, a single ambiguous spatial cue was presented before a delay (memory) period. Animals were then given a contextual rule cue, which provided the instruction for them either to initiate a reach towards the position indicated by the preceding spatial cue (a direct motor goal) or to reach towards the opposite direction (an inferred motor goal). Importantly, the latter reach was made towards a location in which no object had been presented visually, ruling out the possibility that any activity related to this reach option could be due to some form of visual memory. The move line indicates the animal’s movement onset time. The surface plot depicts the average activity of parietal reach region (PRR) neurons over the course of the experiment, aligned to the preferred direction of the neurons relative to the reach direction specified by the cue. Examination of activity during the memory period shows that PRR neurons simultaneously represent both motor goals (direct and inferred) when the rule associated with the spatial cue is not yet known. Similar results were observed in dorsal premotor cortex. Note that once the animals execute the rule-instructed reach, the neurons with preferred directions corresponding to the reach direction significantly increase their activity. b | Reach averaging in ‘go-before-you-know’ tasks is influenced by required movement speeds69. In this task, participants performed reaches towards two competing targets, with the final reach target being revealed only after movement onset. Reach trajectories, from a single participant over multiple trials, for slow (left) and fast (right) movement speeds, are shown. Spatial averaging, wherein initial movements are launched towards an intermediate (or averaged) spatial location, occurs only for slow movements, when time allows for corrective movements to be made. c | Spatial averaging reflects an average of corresponding movement, and not visual, directions76. The left panel shows representative reach directions, from a single participant, for one-target and two-target trials (in which the two outermost targets were presented) in go-before-you-know tasks. Two-target trials show standard spatial averaging behaviour. The right panel shows that, when gradual, imperceptible mismatches between the hand position and the viewed cursor representing the hand position (that is, visuomotor rotation of the hand cursor) are applied to the rightmost target (denoted by curved blue arrow), such that subjects must move their hands straight ahead in order to reach both the central and rightmost targets, individuals in two-target trials tended to reach in the direction that was the average of the movement paths associated with the two targets. This leftward shift in averaging behaviour from pre-adaptation (left panel) to post-adaptation (right panel) learning of the visuomotor rotation suggests that spatial averaging occurs at the level of motor representations. Part a is adapted with permission from ref.50, Elsevier. Part b is adapted from ref.69, Springer Nature Limited. Part c is adapted with permission from ref.76, Elsevier.

One interpretation of these neurophysiological findings, which resonates with the influential notion of action affordances51, is that the motor system, before target selection, prepares competing movement plans for potential targets40,52. However, it is also plausible (and difficult to rule out) that the activity of neurons in reaching-related areas instead represents purely spatial and/or memory-related information about multiple potential action targets (such as their directions)45,53,54. Indeed, because in these tasks the final target is often cued before the requirement to execute a movement, it is not immediately obvious why the brain would go through the computational expense of automatically converting viewed or remembered potential targets into corresponding movement representations in the sensorimotor cortex.

Work on the oculomotor system has shown that movement-related neurons in oculomotor control structures, such as the superior colliculus and frontal eye fields, simultaneously encode competing targets for eye saccades55,56,57,58,59,60. Although a matter of ongoing debate, it has been suggested that this activity represents competing target-directed saccadic eye movements. Likewise, the extent to which competing reach movements may be specified in advance of target selection remains a matter of some debate. Indeed, there are reasons why this idea may be viewed as less plausible for reach movements than it is for eye movements.

First, the control of reaching movements (unlike oculomotor control) can require the parameterization of multiple and often redundant degrees of freedom. That is, for any given target-directed limb movement, there are often an abundance of possible motor solutions that can be used to achieve the same goal. This is the case not only because of the infinite number of different reach paths and the range of hand speeds along each path that could, in principle, be specified but also because any one single joint motion can be achieved by different combinations of muscles61. Furthermore, in addition to often having to account for external forces applied to the hand14,22, the limb control system must compensate for complex intersegmental dynamics. Together, this indicates that there are several additional complexities in planning limb movements when compared with eye movements.

Second, the oculomotor and limb movement systems perform vastly different functions in everyday life. Limb movements occur relatively infrequently and are usually voluntary in nature, whereas we typically perform multiple saccadic eye movements per second when sampling the visual environment. It is perhaps not surprising then that the oculomotor system might, at any given moment, prepare multiple competing eye movements to salient visual items in one’s immediate surroundings. That the limb control system should follow the same rule is not as directly inferred from its everyday function62. Indeed, there is evidence that, when performing coordinated eye and hand movements towards the same target location, the two effector systems are differentially affected by the presence of a non-target (distractor) stimulus63.

A number of behavioural studies have sought to provide direct evidence in support of the view that the brain specifies competing reach movements in advance of target selection. Many have used tasks in which participants are simultaneously presented with multiple potential reach targets and, before knowing the final target location (which is cued after movement onset), are required to launch a reach movement towards the competing targets (termed go-before-you-know tasks)64,65,66,67,68,69,70. In such tasks, one might expect the initial movement to be influenced by competing motor plans. Indeed, it has been shown that people initially launch reaching movements towards an intermediate or midpoint location between the competing targets, which has been interpreted as arising from the specification, and simultaneous execution, of competing action plans64,70. However, it has been shown that launching movements towards an intermediate or averaged spatial location also minimizes the motor costs associated with the corrective movements required once the target has been cued71,72,73,74. When the strategic benefits of intermediate movements in go-before-you-know tasks are mitigated by reducing the time available to make in-flight corrective movements or by increasing the spatial separation between targets69,71,75 (Fig. 2b), such spatial averaging is largely abated. This finding would seem to argue instead that spatial averaging reflects an optimization based on task constraints and motor costs69,71,72. Nevertheless, these findings do not actually address how such optimized movements may themselves be computed or explain the representations on which they are based.

One obvious possibility is that approximately optimal averaging behaviour could be based on visual representations of the potential targets. That is, participants could prepare and execute a single movement in a direction that is the average of the visual directions to the competing targets. This possibility would seem to provide a useful, and readily implementable, heuristic for optimizing movements in cases of target uncertainty. Alternatively, the visual targets could be transformed into motor representations of these targets (that is, reach directions or final hand positions) and these representations could be used when determining the optimal reach direction. To disentangle these two interpretations, a recent study76 applied target-specific, gradual visuomotor rotations to dissociate the visual direction of the potential targets from the direction of the movements required to reach the same targets, unbeknownst to participants (Fig. 2c). This dissociation revealed that movements executed towards multiple potential targets constitute a weighted average of the movement paths executed towards each target separately and were not the result of visual averaging. As such, these findings provide robust evidence that spatial averaging results from a direct visual-to-motor mapping of target locations onto corresponding motor representations of those targets, a transformation that may provide the basis for the computation and launching of optimal initial movement directions.

A challenge for future neurophysiological work will be to determine whether, in go-before-you-know tasks, competing targets elicit separate neural representations in reach-related brain areas45,50 or a single representation, corresponding only to the initial movement trajectory. In addition, it will be important for future behavioural work to investigate cases in which averaging behaviour should, and should not, be expected. Although the work discussed above provides evidence that reach parameters such as direction or final hand position are used to compute optimal initial movements, there is also evidence that the dynamics associated with competing reach movements, such as grip force, are not averaged77. Furthermore, given that spatial averaging can be influenced by a wide array of cognitive factors (such as reward history78, task set and attention79) (Box 1), how such top-down cognitive processes and biases modify representations of the competing targets remains an open and active area of investigation50,80,81.

Directly mapping potential visual targets onto associated motor representations might provide a mechanism through which movement-related costs and constraints can be incorporated when making decisions about action selection. Consistent with this idea, recent work shows that when humans make free choices between two potential reaching movements, they tend to choose the movement that has the lowest movement-related cost82,83. A more recent study shows that the prediction of the effort associated with candidate movements is computed very quickly and can influence decisions at the level of the motor cortex within 200 ms (ref.84). This suggests that the decision-making process can rapidly access knowledge of the future biomechanical costs of both movements in order to compare these and select the lowest-cost option. Other recent work has explored the subjective (internal effort) and objective (energy expenditure) cost functions utilized in choosing between effortful reaching movements85,86 and how free choices between reach targets are biased by prior target predictability and expected value87. Together, this work indicates that decision-making related to action selection is governed by many of the same underlying optimization principles that are utilized in the control of actions, suggesting considerable overlap in the neural processes supporting these two processes.

The above findings suggest that in both go-before-you-know situations and free-choice scenarios, at least some motor parameters associated with competing targets are specified before movement selection and initiation. However, these results do not provide direct support for the idea that individuals specify competing movements in the much more naturalistic case in which target cuing occurs before movement execution (that is, go-after-you-know tasks). Direct evidence for this idea instead comes from a recent study88 in which participants, before target cuing, were presented with two potential targets, one of which could be reached using two different trajectories (that is, an ambiguous target). It was found that the reach movement generated when the ambiguous target was cued often borrowed kinematic components of the movement that would have been required for the non-cued, competing target (the non-ambiguous target). This interaction, which resulted in faster reaction and movement times, could arise only if multiple potential movements were specified in advance of target cuing. Follow-up work further demonstrated that this movement ‘co-optimization’ effect can also be observed across sequentially presented potential targets89, suggesting that individuals successively prepare actions for each potential target as it appears in the sequence. This finding is noteworthy as it may shed insight into the mechanisms through which the sensorimotor system operates in natural, everyday environments, in which the available action options change from one moment to the next as we move in the world.

Revising movement

In response to changes in the world or our evaluation of the values of different action options, the brain continuously adjusts and refines its goals in order to achieve the desired outcome. One strategy used by the sensorimotor system, which facilitates rapid decision-making during unfolding actions, is the specification of sophisticated contingency plans in situations in which the goal of an action may be threatened. An example is provided by a recent study90 in which participants performed reaching movements with obstacles located either side of a direct path between the hand’s starting location and the target (Fig. 3a). On some trials, a mechanical load was briefly applied to the limb early in the movement so that the hand was perturbed towards one of the obstacles. For small perturbations, the corrective response counteracted the perturbation so that the hand passed between the obstacles, whereas for large perturbations the response was in the direction of the perturbation so that the hand passed around the obstacle. For intermediate perturbations, the hand would sometimes pass between, and sometimes around, the obstacle, and this choice was evident in muscle activity as little as 60 ms after the perturbation. Thus, the motor system can switch, with remarkable speed, from one motor plan to another in order to accomplish the task goal.

Fig. 3: Rapid switching between sensorimotor decisions on the basis of context.

a | Obstacle avoidance behaviour reveals the preparation of contingency plans during reaching90. Reaching movements were made between a starting location and a target, with obstacles on either side of the straight line path to the target. When small mechanical perturbations were applied, the subjects corrected their reach to pass between the targets (lines show paths on individual trials). When large perturbations were applied, the subject corrected their reach in the direction of the load so as to pass around the left obstacle. When medium perturbations were applied, on some trials the subject chose to pass around the obstacles (red lines) and on other trials they chose to pass between the obstacles (blue lines). Traces at the bottom show electromyography (EMG) responses recorded from the lateral triceps and averaged across subjects during performance of the reaching task. The decision to avoid the obstacle to the left versus right can be observed as a difference in muscle activity at ~60 ms after application of the perturbation (indicated by the asterisk). This finding shows that, for a single reach target, the motor system can flexibly switch between motor plans with remarkable speed. The stretch response epochs of muscle activity are as follows: R1 = 20–45 ms; R2 = 45–75 ms; R3 = 75–105 ms; and early voluntary (EV) = 105–135 ms. These epochs range from purely spinal-generated corrective responses (R1) to those involving increasing cortical involvement (R2–EV)20. b | Target selection behaviour reveals rapid switching between multiple spatial goals90. Black paths show unperturbed movements when a participant reaches to either a single target (left) or two possible targets (right). When perturbed leftward, a rapid correction is seen to a single target (blue traces). However, in the presence of two targets, the participant on most trials reaches for the second target (red traces). Bottom, EMG responses from the lateral triceps, averaged across subjects, during the performance of the target selection task. Differences in muscle activity corresponding to redirected movements to the second target versus the single target were observed ~75 ms after the perturbation. When compared with panel a, which shows the decision time required to implement a new plan for the same spatial goal, this result shows that there is only an additional 15 ms of decision time required to switch to a new goal. This marginal time cost suggests that the motor system separately maintains contingency plans alongside the executed action in the event that the task goal becomes threatened. a.u., arbitrary units. Adapted with permission from Society for Neuroscience, from Rapid online selection between multiple motor plans, Nashed, J. Y. et al., 34, 2014 (ref.90); permission conveyed through Copyright Clearance Center, Inc.

The same study also examined a situation in which participants were presented with two targets and told they could reach for either target (Fig. 3b). In the absence of a perturbation, participants always selected the closest target. However, when a perturbation was applied that pushed the hand towards the alternative target, participants almost always switched their movement goal to this other target. The muscle activity in the switch trials could be distinguished from muscle activity in single-target trials (in which the same perturbation was applied) within ~75 ms of the perturbation. Thus, there is a 15 ms cost associated with the decision to switch to a new goal rather than execute a new plan for the same goal. The impressive speed of this target updating process suggests that the re-routed movement was not planned de novo but rather specified in advance of movement as a contingency plan and maintained in parallel during the unfolding action to be used in the event that the movement goal became compromised. As discussed above, the notion that the brain specifies parallel motor goals would allow rapid movement revisions in response to unexpected changes in the environment and is broadly consistent with the idea that the brain can maintain multiple motor representations or movement plans52,73,91,92,93.

To study in more detail the evolution of decision-making processes and their links to sensorimotor systems, perceptual decision-making tasks have also been used. Typically, individuals are required to discriminate between two directions of motion in a noisy random dot display and to indicate their decision with an eye movement. Such tasks have led to a quantitative understanding of the mechanisms that evaluate sensory evidence and reach a decision94,95,96. Several formalisms propose that a representation of noisy evidence is accumulated in favour of each choice until it reaches a threshold, leading to that decision94. Such formalisms have well-described neural correlates and can explain both the accuracy and speed of decision-making97. Although perceptual decision-making has primarily been studied with saccadic eye movements and has been generally viewed as a process that is completed before actions are specified, more recent studies in humans performing reaching movements have revealed a much more continuous flow from the decision process to the motor system98.

In decision-making, the decision variable is the accumulated evidence in favour of one decision over another. In sensorimotor control, a key variable is the strength of feedback gains, which are thought to be set by the control policy governing an action. To examine the relationship between decision-making and motor control processes, a recent study asked participants to indicate the direction of motion in a random dot display by making either an elbow flexion or extension movement99 and manipulating the difficulty of the task. The cue to respond was a rapid extension of the elbow generated by a robotic interface, which resulted in flexion stretch reflexes (to counteract the elbow extension), the magnitude of which could be assessed through EMG. By fitting an accumulate to bound (drift-diffusion) model to the accuracy of the participants’ responses, it was possible to estimate the accumulated evidence in favour of the choice (the decision variable) on each trial. This estimate allowed the researchers to show that the magnitude (the gain) of the medium and long-latency flexion stretch reflexes (R2 and R3 responses) increased almost linearly with the accumulated evidence in favour of a flexion movement. This finding suggests that the motor system receives a continuous flow of information from the decision process, which it uses to preset the gain of the flexion reflex so as to reach the appropriate target more quickly when cued. This result complements findings in eye movements in nonhuman primates100 and suggests that the brain does not wait for a decision to be completed before recruiting the motor system but rather continuously relays information linked to a probable motor response.

Decision-making and sensorimotor control processes are further entwined by the time delays that are present in the sensorimotor system. For example, there is a sensory delay between the presentation of a stimulus and the accumulation of evidence in brain areas involved in eye movements, such as the lateral intraparietal area97,101. In addition, even after the decision variable crosses a threshold leading to a decision, there is a delay in generating the appropriate motor response97,101. The sum of these delays can be as long as 450 ms for a manual response. This delay means that whenever we initiate an action on the basis of a stream of perceptual information — even if the onset of the action terminates the perceptual stream (in the case of an eye movement away from the stimulus, for example) — there will be additional information in the processing pipeline that cannot be used for the initial decision but could still be processed. In tasks in which the response is a ballistic eye movement, such processing is hard to appreciate. However, studies of reaching movements have shown that such post-initiation processing can have important effects. In a perceptual decision-making task in which the required response was a reach towards one of two targets, specified by the stimulus, movements sometimes started towards one target before veering towards the other, even when the stimulus was extinguished on initiation of the movement102. Such ‘change-of-mind’ trials can be explained by the accumulation process continuing after movement initiation, with the evidence changing in favour of the other target. Using such a model, it was possible to explain the frequency of trials in which subjects changed their mind to either correct an error or spoil a good start as a function of trial difficulty102. The neural representation of such changes of mind has recently been described in the single-trial neural population activity of the motor cortex103 and prefrontal cortex104 of nonhuman primates, wherein the neural state initially reflects one choice before changing to reflect the final choice (Fig. 4a,b).

Fig. 4: Evidence for changes of mind.

a | The schematic depicts a decision-maze task in which monkeys were presented with two targets surrounded by barriers that could change configuration during a trial103. Neural population activity was recorded using 96-electrode arrays in the dorsal premotor cortex and primary motor cortex. b | In switch trials, initially only one target was attainable, but the barrier configuration could be switched so as to make a second, previously unattainable target, the easiest target to attain. Trials in which the monkey either did not, or did, take advantage of the switch are shown in the left and right panels, respectively. A decoding algorithm for target choice (left or right) was trained using neural population activity from forced-choice trials, in which only one target was attainable, and used to predict target choice in switch trials over time from the activity recorded during switch trial performance. The traces depict decoded final target choice over time for single trials in which the left or right target was ultimately chosen (blue traces or red traces, respectively). The red and blue dots superimposed on the traces indicate the time of the barrier switch. These findings not only show that free-choice trials are neurally represented in a similar fashion to forced-choice trials but also show that, on the barrier switch trials, the neural activity would sometimes initially indicate one choice, before switching to the opposite choice, consistent with a change of mind. c | Trajectory revision in response to post-initiation processing116. In this study, human subjects watched a noisy random dot display, wherein the direction coherence of the dots was varied, and had to indicate both the net direction of motion of the dots (left versus right) and how confident they were in their choice (high versus low) by reaching from a central location to one of four choice targets. Although the random dot display was extinguished on movement initiation, a small percentage of trials showed changes in reach paths (traces) indicative of changes of decision (top) or changes of confidence (bottom; only trials in which a change of decision/confidence are shown). d | A drift-diffusion model can account for initial decisions, confidence and reaction times and changes of decisions and confidence. In this model, noisy momentary evidence of the direction of dot motion accumulates in two processes providing evidence for rightward (green trace) and leftward (red trace) motion, respectively. The first process to cross a decision bound determines the initial choice (rightward in this case; top plot). There is a correspondence between the level of the accumulated evidence, the elapsed time and the confidence (log odds; bottom plot) that a rightward decision would be correct. In the model, the boundaries between confidence–choice combinations can change after the initial decision (vertical dashed line) to capture the physical cost of altering the reach for a change of mind. In the example trial shown, the boundary between low and high confidence for rightward motion changes after the initial decision, requiring greater evidence to be accumulated (black trace) in favour of high confidence before the participant changes their confidence decision during movement, as in this example. Non-decision time refers to the sum of sensory and motor delays and corresponds to the unused information that can be processed after commitment to an initial choice (post-initiation processing). a.u., arbitrary units; DVleft, decision variable left; DVright, decision variable right. Parts a and b are adapted with permission from ref.103, eLife. Parts c and d are adapted with permission from ref.116, PLOS.

Although this recent work demonstrates that incoming sensory information can influence and update the motor responses used for perceptual reporting, it has also been recently shown that the features of the motor responses themselves can influence perceptual decisions. One study105 showed that different levels of physical effort associated with reporting two directions of dot motion with the hand could bias perceptual reports towards the less-effortful option. Strikingly, these biasing effects were effector-independent, transferring (at least initially) to other response modalities that did not have increased effort, such as a vocal response. It has also been demonstrated that increasing the physical effort associated with a target-directed motor response results in a corresponding reduction in the frequency of changes of mind towards those targets, suggesting that subjects trade off accuracy and effort106,107. Taken together, this recent work suggests that in perceptual decision-making tasks, the motor system, rather than acting as an impartial observer and conveyer of an upstream perceptual decision, can influence how sensory inputs are transformed into decision variables. This general notion fits within the broader context of psychological studies on embodied cognition that show, for example, that the perceived steepness of hills and distances of landmarks, estimates of jumping height and even selection of target versus non-target objects, can all be influenced by the presumed efforts and limitations of the motor system108,109,110,111.

The role of confidence — the subjective degree of certainty that a correct choice has been made — has received a great deal of attention in recent work on decision-making112,113,114. This attention is because confidence can be influenced by factors such as decision time115 and plays an important role in optimizing choices. For example, subjects in post-decision wagering tasks tend to choose a small but guaranteed reward in regimes in which they are likely to have low confidence compared with their choices in regimes in which confidence is high112. One recent study showed that initial confidence, choice and reaction time (as well as changes of confidence and choice) can be coherently explained by a simple race model116 (Fig. 4c,d).

Confidence can also have a substantial effect on sequences of movements. When we make a sequence of choices, we often need to get them all correct to achieve a goal; however, we do not receive feedback on whether individual decisions are correct. It has recently been shown that the confidence in the first of two decisions affects the way the decision-making process is set up for a second decision117. If both choices must be correct in order for subjects to receive a reward, subjects take longer (and are therefore more accurate) to make the second decision if they have high confidence in the first decision. That is, participants invest more time on the second decision when they have high confidence that the first decision was correct. In a drift-diffusion model developed to capture this behaviour, the height of the threshold on the decision variable for the second decision increased linearly with confidence in the first decision117. This finding suggests that confidence has a role in setting the parameters for future decisions and that decision criteria can be adjusted rapidly between decisions so as to optimize performance.

Sequencing movements

Real-world action tasks typically involve a sequence of movements in which the final state associated with one movement in the sequence sets the initial state for the next and so on118. Researchers interested in the sequencing of movements have often used tasks in which participants generate a series of finger presses. These include the serial reaction time task, in which participants have to respond to visual stimuli using a finger press at a prescribed pace, and the discrete sequence production task, in which participants execute a known sequence of finger presses as fast as possible, either from memory or supported by sequential cues119. However, unlike many natural action tasks, the movements in these paradigms are not only fixed, they are largely independent; that is, the initial state of a given movement does not depend on the final state of the previous movement. By contrast, in many real-world tasks, individuals can choose the sequence of actions that they perform and each of these actions can be executed in different ways (with different kinematics or even different effectors). Thus, real-world tasks involve a decision-making process about which actions to perform and when and how to perform them.

In general, the efficient performance of tasks involving action sequencing requires future constituent actions to be considered when planning the current action. In principle, such ‘looking ahead’ would enable the motor system to better optimize costs and rewards across the entire task. This ability has often been examined in the context of ‘travelling salesperson’ problems, in which participants attempt to choose the shortest possible path that connects a fixed set of targets120. Behavioural studies have shown that, when given enough time, individuals are often capable of coming up with near-optimal solutions121,122. However, a key question is how well people optimize the sequencing of movement components in action tasks in which there are substantial movement-related costs, tight time restrictions and different task constraints and rewards.

Recent work123 has investigated decision-making in a movement foraging task in which individuals could choose the order in which they ‘harvested’ multiple targets that varied in size, value and location across a workspace, either by moving a hand-held handle to targets (hand task) or by briefly fixating each target (eye task) (Fig. 5a). The short trial duration (3.25 s) meant that, in both tasks, participants could harvest only around half of the targets, placing a premium on rapid and efficient decision-making about which targets to harvest. The foraging data were analysed with a probabilistic model that was inspired by optimal foraging theory124,125, which predicts target-by-target harvesting probabilities on the basis of the rate of reward, costs associated with target distance and size, and decision noise. This model showed that, in both tasks, participants rapidly and naturally selected near-optimal harvesting paths that maximized reward. Whereas target value and distance influenced choice behaviour in both the hand and eye tasks, the relative influence of distance was stronger for the hand task. A key feature of the model was that it can incorporate a number of future successive harvests by employing temporal discounting; that is, it can ‘look ahead’. Using the model, it was possible for the researchers to determine, for each participant, the number of targets that they considered ahead when using the eye or arm (that is, the number that best accounted for their behaviour). Whereas eye movement decisions were typically made in isolation of potential future targets, hand-movement decisions considered multiple future targets in advance (Fig. 5b), a process that presumably involves attending to future target locations and their properties126. The above findings indicate that the motor system can rapidly and flexibly adjust its sequencing decisions on the basis of the motor effectors used and their associated movement-related costs (including biomechanical effort, movement and time). Notably, although it has been shown that, in navigational planning in rodents, the hippocampus generates sequences of neural events encoding spatial trajectories from the current location to the known goal location127, the neural correlates governing route planning through reachable space remains unknown. An exciting challenge for future work will be to explore how movement-related costs, which are presumably computed in the motor system84, may interface with the brain’s navigational systems located in the medial temporal lobe128 and ultimately be factored into such decision processes.

Fig. 5: Optimized sensorimotor decisions for sequences of actions.

a | The schematic depicts hand (left) and eye (right) foraging tasks. In these tasks, individuals choose the order in which to ‘harvest’ multiple targets, either by moving a hand-held handle to targets (hand foraging task) or by briefly fixating each target (eye foraging task). In the examples shown, smaller targets yield higher rewards, grey targets indicate those targets that have been successfully harvested, black traces indicate the paths used to harvest those targets and the numbers indicate the corresponding order of harvests123. b | Histogram shows the number of subjects in the hand (red bars) and eye (blue bars) task for which the best-fitting model incorporated a given number of look-ahead targets. This model, applied on an individual basis, allowed an estimation of the number of targets that each participant considered in advance of their next movement (that is, looked ahead to) and showed that, although hand-movement decisions considered multiple future targets in advance, eye movement decisions were largely made in isolation of potential future targets. ce | Active sensing maximizes visual discrimination130. In this task, subjects had to determine whether an image was patchy or stripy (part c). A gaze-contingent display was used in which the image was initially overlaid by a mask. In active trials, for each fixated location, a small aperture of the image was revealed (that is, the location revealed was actively selected through an eye movement). In passive trials, the locations that were revealed were either randomly chosen (random trials) or determined by a Bayesian active sensor (BAS) model (ideal BAS trials), which determined the optimal (that is, most informative) location to reveal (part d). Following a number of fixations (25 are shown here), the subject indicated whether they thought the underlying image was patchy or stripy. Plot in part e shows, for active (red trace), passive (blue trace) and BAS trials (black trace), the proportion of correct choices as a function of number of locations revealed. Performance in active trials was superior to passive random trials but not as good as in the passive ideal BAS trials (part e). An observer model was able to account for the performance (lines ± shaded s.e.). f | Performance of the BAS model for a single trial with a stripy (S) underlying image (left panel). The maps (four right-hand panels) show the expected ‘informativeness’ (BAS score) as a function of the location of the next potential fixation at different points in the trial (defined by fixation numbers 1, 4, 7 and 14). Histogram insets show the evolving probability assigned by the model to the two categories, which by saccade 14 correctly favours S rather than patchy (P). Note that although each fixation can be spatially distant from the optimal location, they are still high in terms of the information gain percentile. Parts a and b adapted with permission from ref.123, PLOS. Parts cf are adapted with permission from ref.130, eLife.

The ability to sequence behaviours intelligently is not only important for structuring actions but is also crucial for extracting the sensory information about the world that ultimately shapes the actions we make. For example, the efficient sequencing of eye movements when sampling the visual environment should take into account the information that has been garnered through each previous fixation. Consistent with this idea, it has been shown that individuals, when searching for a target location among distractors, exhibit sequential eye movement patterns in which each eye movement minimizes the uncertainty of the target location over the visual scene129. This active sensing strategy, which qualitatively approximates an optimal extraction of task-relevant information129, suggests that the oculomotor system selects, from one saccade to the next, eye movements that sample sensory information in a way that maximizes task performance.

Whereas the goal of visual search tasks can typically be obtained by fixating a single location (the target), in many everyday tasks fixations at multiple locations are required to extract the information needed to accomplish the task goal. Tasks such as categorization often involve accumulating information across multiple separate fixations and integrating this information with prior knowledge of the stimulus being viewed. Recent work130 directly examined the efficiency of information extraction for individual fixations by employing a visual categorization task in which, for each fixated location, a small aperture of a masked image was revealed (Fig. 5c,d). Participants’ eye movements in this task were compared with those generated by a Bayes-optimal algorithm seeking to maximize, with each individual eye movement, information relevant to stimulus categorization (Fig. 5e,f). Specifically, the active sensing strategy employed by the algorithm involved computing, from the information already acquired about the scene via eye movements and knowledge of the statistical structure of patterns, the location in the scene that, when fixated, was likely to lead to the best reduction in categorization error. Notably, the authors showed that, although participants’ scan paths in this task were not quite optimal (~70% efficient compared with the Bayes-optimal algorithm), their resulting discrimination accuracy was far better than it was when the image locations revealed were randomly chosen. This result shows that the sensorimotor system integrates information across multiple fixated locations in order to select eye movements that enhance information extraction.

Conclusions and future directions

Interactions between decision-making and motor control occur at multiple levels in the planning and control of action. In this Review, we have discussed four key aspects of decision-making related to sensorimotor control: how the brain selects a particular movement when reaching toward a single target, how it represents and selects between competing movement goals, how it flexibly revises ongoing movements and movement goals and how it chooses sequences of movements. We predict three overarching challenges for the future of the field.

Interactions between cognitive and motor systems

One major challenge for future work in the field of motor control is to better understand how sensorimotor systems interact with cognitive systems. Although tremendous strides have been made in recent years by investigating the interaction between decision-making processes and motor control, other interactions — for example, between memory systems and motor control — remain rich areas for future investigation. Recent work on motor learning, for example, has emphasized the role of explicit or cognitive strategies in adapting to visuomotor rotations131,132,133; however, our current understanding of how different memory systems support the planning and control of action tasks remains quite limited. Real-world action tasks, such as making tea or cooking a meal, involve interacting with multiple objects in our environment, which are lifted, moved about in space and then lifted again. Such tasks involve declarative memories about object properties, spatial memories about object locations and episodic memories about dynamic changes in the environment that are brought about through action. Thus, a full understanding of the planning and control of such tasks will require elucidating how these different memory systems interact with sensorimotor processes.

Decision-making in real-world situations

We now have a detailed understanding of the interaction between decision-making and motor control in a narrow range of tasks. Although these tasks are amenable to analysis and modelling, they do not capture the full complexity of real-world decision-making in the context of action. In our daily lives, we have to make high-level decisions about which tasks to perform and, at an even higher level, which tasks to learn. In terms of the latter, we frequently make decisions about whether to invest in learning a new skill, ranging from trying out a new tennis grip to learning to speak a new language. Such decisions are based on an estimation of the time and effort involved, the motor skill level we will eventually attain, the success we will have and the expected payoff that such learning will provide. Such decisions presumably require some knowledge of the capacity and constraints of our own motor systems and a prediction of future performance. However, it remains poorly understood how such factors influence decision-making processes at this higher level.

From computational mechanisms to neuronal implementation

Although substantial progress has been made in understanding the computational mechanisms underlying decision formation (such as the integration of sensory evidence leading to a decision) and how these correlate with and are causally related to neuronal responses in multiple brain regions94,100,134,135,136,137,138, we still have a fairly sparse view of the whole-brain neural circuits underlying decision-making related to sensorimotor control. Recent work in human neuroimaging, although lacking the spatial and temporal resolution of neuronal recording techniques, has begun making some inroads into understanding how, at the whole-brain level, sensorimotor and cognitive networks in the brain interact and together contribute to the planning and control of action tasks139,140,141,142. However, in comparison to the neuroimaging areas of perception, memory and language143,144,145,146,147,148, this is a relatively poorly investigated topic — a gap due, in part, to the inherent challenges and difficulties in studying motor behaviour in the MRI scanner environment (such as motion-related artefacts in MRI signal and limited workspace). Developments in this particular research area may help reveal how decision-related computations are instantiated across distributed brain areas, which will provide future sites for neuronal recordings in nonhuman primates as well as important constraints for biologically plausible computational theories.

In summary, over the past decade, there has been considerable progress in our understanding of the bidirectional interplay between decision-making and sensorimotor control. The exciting challenges ahead lie in better understanding decision-making as an evolving and continuous process that adjusts and refines ongoing actions as well as the neuronal implementation of this process. The success of this more integrated approach to studying the relationship between cognition and motor behaviour will be measured by how well models are able to explain more naturalistic behavioural tasks, to explain deficits observed in neurological or behavioural disorders and to inform the development of more complex robotics and recovery-of-function options for individuals.


  1. 1.

    Woodworth, R. S. The accuracy of voluntary movement. Psychol.Rev. 3, i114 (1899).

    Google Scholar 

  2. 2.

    Jeannerod, M. The Neural and Behavioural Organization of Goal-directed Movements. (Oxford Univ. Press, Oxford, 1990).

    Google Scholar 

  3. 3.

    Rosenbaum, D. A. in Human Motor Control. 79–118 (Academic Press, Cambridge, 1991).

    Book  Google Scholar 

  4. 4.

    Glover, S. Separate visual representations in the planning and control of action. Behav. Brain Sci. 27, 3–24 (2004). discussion 24–78.

    PubMed  Google Scholar 

  5. 5.

    Harris, C. M. & Wolpert, D. M. Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998). This paper provides a unifying theory of eye and arm movements — the minimum variance theory — which posits that the CNS selects movement trajectories that minimize post-movement variance in the presence of signal-dependent noise in the neural control signal.

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Flash, T. & Hogan, N. The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985).

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Neyedli, H. F. & Welsh, T. N. Optimal weighting of costs and probabilities in a risky motor decision-making task requires experience. J. Exp. Psychol. Hum. Percept. Perform. 39, 638–645 (2013).

    PubMed  Article  Google Scholar 

  8. 8.

    Trommershäuser, J., Landy, M. & Maloney, L. Statistical decision theory and trade-offs in the control of motor response. Spat. Vis. 16, 255–275 (2003).

    PubMed  Article  Google Scholar 

  9. 9.

    Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002). This paper introduces the OFC framework as a theory for understanding motor control.

    PubMed  Article  CAS  Google Scholar 

  10. 10.

    Scott, S. H. Optimal feedback control and the neural basis of volitional motor control. Nat. Rev. Neurosci. 5, 532–546 (2004).

    PubMed  Article  CAS  Google Scholar 

  11. 11.

    Todorov, E. Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. 13.

    Crevecoeur, F. & Scott, S. H. Beyond muscles stiffness: importance of state-estimation to account for very fast motor corrections. PLoS Comput. Biol. 10, e1003869 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. 14.

    Cluff, T. & Scott, S. H. Apparent and actual trajectory control depend on the behavioral context in upper limb motor tasks. J. Neurosci. 35, 12465–12476 (2015).

    PubMed  Article  CAS  Google Scholar 

  15. 15.

    Nashed, J. Y., Crevecoeur, F. & Scott, S. H. Influence of the behavioral goal and environmental obstacles on rapid feedback responses. J. Neurophysiol. 108, 999–1009 (2012).

    PubMed  Article  Google Scholar 

  16. 16.

    Reichenbach, A., Franklin, D. W., Zatka-Haas, P. & Diedrichsen, J. A dedicated binding mechanism for the visual control of movement. Curr. Biol. 24, 780–785 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17.

    de Brouwer, A. J., Jarvis, T., Gallivan, J. P. & Flanagan, J. R. Parallel specification of visuomotor feedback gains during bimanual reaching to independent goals. eNeuro 4, ENEURO.0026-17.2017 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Nashed, J. Y., Kurtzer, I. L. & Scott, S. H. Context-dependent inhibition of unloaded muscles during the long-latency epoch. J. Neurophysiol. 113, 192–202 (2015).

    PubMed  Article  Google Scholar 

  19. 19.

    Pruszynski, J. A., Johansson, R. S. & Flanagan, J. R. A. Rapid tactile-motor reflex automatically guides reaching toward handheld objects. Curr. Biol. 26, 788–792 (2016). This study describes a rapid tactile-motor reflex in which spatial information about a change in target location provided by tactile feedback in one hand can elicit a fast correction of the opposite hand’s trajectory to reach the target.

    PubMed  Article  CAS  Google Scholar 

  20. 20.

    Pruszynski, J. A. & Scott, S. H. Optimal feedback control and the long-latency stretch response. Exp. Brain Res. 218, 341–359 (2012).

    PubMed  Article  Google Scholar 

  21. 21.

    Bedingham, W. & Tatton, W. G. Dependence of EMG responses evoked by imposed wrist displacements on pre-existing activity in the stretched muscles. Can. J. Neurol. Sci. 11, 272–280 (1984).

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Kurtzer, I., Pruszynski, J. A. & Scott, S. H. Long-latency responses during reaching account for the mechanical interaction between the shoulder and elbow joints. J. Neurophysiol. 102, 3004–3015 (2009).

    PubMed  Article  Google Scholar 

  23. 23.

    Kurtzer, I. L., Pruszynski, J. A. & Scott, S. H. Long-latency reflexes of the human arm reflect an internal model of limb dynamics. Curr. Biol. 18, 449–453 (2008).

    PubMed  Article  CAS  Google Scholar 

  24. 24.

    Diamond, J. S., Nashed, J. Y., Johansson, R. S., Wolpert, D. M. & Flanagan, J. R. Rapid visuomotor corrective responses during transport of hand-held objects incorporate novel object dynamics. J. Neurosci. 35, 10572–10580 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Crevecoeur, F., Kurtzer, I. & Scott, S. H. Fast corrective responses are evoked by perturbations approaching the natural variability of posture and movement tasks. J. Neurophysiol. 107, 2821–2832 (2012).

    PubMed  Article  CAS  Google Scholar 

  26. 26.

    Dimitriou, M., Franklin, D. W. & Wolpert, D. M. Task-dependent coordination of rapid bimanual motor responses. J. Neurophysiol. 107, 890–901 (2012).

    PubMed  Article  Google Scholar 

  27. 27.

    Mutha, P. K. & Sainburg, R. L. Shared bimanual tasks elicit bimanual reflexes during movement. J. Neurophysiol. 102, 3142–3155 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Burkitt, J. J., Grierson, L. E. M., Staite, V., Elliott, D. & Lyons, J. The impact of prior knowledge about visual feedback on motor performance and learning. Adv. Phys. Educ. 3, 1–9 (2013).

    Article  Google Scholar 

  29. 29.

    Hansen, S., Glazebrook, C. M., Anson, J. G., Weeks, D. J. & Elliott, D. The influence of advance information about target location and visual feedback on movement planning and execution. Can. J. Exp. Psychol. 60, 200–208 (2006).

    PubMed  Article  Google Scholar 

  30. 30.

    Khan, M. A., Elliot, D., Coull, J., Chua, R. & Lyons, J. Optimal control strategies under different feedback schedules: kinematic evidence. J. Mot. Behav. 34, 45–57 (2002).

    PubMed  Article  Google Scholar 

  31. 31.

    Elliott, D. et al. Goal-directed aiming: two components but multiple processes. Psychol. Bull. 136, 1023–1044 (2010).

    PubMed  Article  Google Scholar 

  32. 32.

    Elliott, D. et al. The multiple process model of goal-directed reaching revisited. Neurosci. Biobehav. Rev. 72, 95–110 (2017).

    PubMed  Article  Google Scholar 

  33. 33.

    Brenner, E. & Smeets, J. B. J. Fast corrections of movements with a computer mouse. Spat. Vis. 16, 365–376 (2003).

    PubMed  Article  Google Scholar 

  34. 34.

    Saunders, J. A. & Knill, D. C. Humans use continuous visual feedback from the hand to control fast reaching movements. Exp. Brain Res. 152, 341–352 (2003).

    PubMed  Article  Google Scholar 

  35. 35.

    Dimitriou, M., Wolpert, D. M. & Franklin, D. W. The temporal evolution of feedback gains rapidly update to task demands. J. Neurosci. 33, 10898–10909 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Saunders, J. A. & Knill, D. C. Visual feedback control of hand movements. J. Neurosci. 24, 3223–3234 (2004). This study uses visual perturbations to examine the relative contributions of motion and position information on feedback control and shows that both forms of visual feedback control operate continuously during target-directed reaching.

    PubMed  Article  CAS  Google Scholar 

  37. 37.

    Franklin, D. W., Reichenbach, A., Franklin, S. & Diedrichsen, J. Temporal evolution of spatial computations for visuomotor control. J. Neurosci. 36, 2329–2341 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    McClelland, J. L. On the time relations of mental processes: an examination of systems of processes in cascade. Psychol. Rev. 86, 287–330 (1979).

    Article  Google Scholar 

  39. 39.

    Miller, G. A., Galanter, E. & Pribram, K. H. Plans and the structure of behavior. (Holt, Rinehart and Winston, Inc, New York, 1960).

    Book  Google Scholar 

  40. 40.

    Cisek, P. Cortical mechanisms of action selection: the affordance competition hypothesis. Phil. Trans. R. Soc. B 362, 1585–1599 (2007). This influential paper provides a theoretical framework for interpreting the results of several neurophysiological and behavioural studies. In contrast to serial models of planning that argue that target selection precedes action specification, this theory proposes that both target selection and action specification operate continuously and in parallel.

    PubMed  Article  Google Scholar 

  41. 41.

    Howard, L. A. & Tipper, S. P. Hand deviations away from visual cues: indirect evidence for inhibition. Exp. Brain Res. 113, 144–152 (1997).

    PubMed  Article  CAS  Google Scholar 

  42. 42.

    Tipper, S. P., Howard, L. A. & Jackson, S. R. Selective reaching to grasp: evidence for distractor interference effects. Vis. Cogn. 4, 1–38 (1997).

    Article  Google Scholar 

  43. 43.

    Welsh, T. N., Elliott, D. & Weeks, D. J. Hand deviations toward distractors. Evidence for response competition. Exp. Brain Res. 127, 207–212 (1999).

    PubMed  Article  CAS  Google Scholar 

  44. 44.

    Welsh, T. N. & Elliott, D. Movement trajectories in the presence of a distracting stimulus: evidence for a response activation model of selective reaching. Q. J. Exp. Psychol. A 57, 1031–1057 (2004).

    PubMed  Article  Google Scholar 

  45. 45.

    Cisek, P. & Kalaska, J. F. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45, 801–814 (2005). This paper shows, using neural recordings, that the primate motor system, when presented with two reaching options, simultaneously represents both reach options before later reflecting the selection between them.

    PubMed  Article  CAS  Google Scholar 

  46. 46.

    Coallier, É., Michelet, T. & Kalaska, J. F. Dorsal premotor cortex: neural correlates of reach target decisions based on a color-location matching rule and conflicting sensory evidence. J. Neurophysiol. 113, 3543–3573 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Cui, H. & Andersen, R. A. Different representations of potential and selected motor plans by distinct parietal areas. J. Neurosci. 31, 18130–18136 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Dekleva, B. M., Ramkumar, P., Wanda, P. A., Kording, K. P. & Miller, L. E. Uncertainty leads to persistent effects on reach representations in dorsal premotor cortex. eLife 5, e14316 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Grent-’t-Jong, T., Oostenveld, R., Medendorp, W. P. & Praamstra, P. Separating visual and motor components of motor cortex activation for multiple reach targets: a visuomotor adaptation study. J. Neurosci. 35, 15135–15144 (2015).

    PubMed  Article  CAS  Google Scholar 

  50. 50.

    Klaes, C., Westendorff, S., Chakrabarti, S. & Gail, A. Choosing goals, not rules: deciding among rule-based action plans. Neuron 70, 536–548 (2011).

    PubMed  Article  CAS  Google Scholar 

  51. 51.

    Gibson, J. J. The Ecological Approach to Visual Perception: Classic Edition. (Psychology Press, London, 2014).

    Book  Google Scholar 

  52. 52.

    Cisek, P. & Kalaska, J. F. Neural mechanisms for interacting with a world full of action choices. Annu. Rev. Neurosci. 33, 269–298 (2010).

    PubMed  Article  CAS  Google Scholar 

  53. 53.

    Pesaran, B., Nelson, M. J. & Andersen, R. A. Dorsal premotor neurons encode the relative position of the hand, eye, and goal during reach planning. Neuron 51, 125–134 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    Pearce, T. M. & Moran, D. W. Strategy-dependent encoding of planned arm movements in the dorsal premotor cortex. Science 337, 984–988 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  55. 55.

    Munoz, D. P. & Wurtz, R. H. Saccade-related activity in monkey superior colliculus. II. Spread of activity during saccades. J. Neurophysiol. 73, 2334–2348 (1995).

    PubMed  Article  CAS  Google Scholar 

  56. 56.

    Basso, M. A. & Wurtz, R. H. Modulation of neuronal activity by target uncertainty. Nature 389, 66–69 (1997).

    PubMed  Article  CAS  Google Scholar 

  57. 57.

    Platt, M. L. & Glimcher, P. W. Responses of intraparietal neurons to saccadic targets and visual distractors. J. Neurophysiol. 78, 1574–1589 (1997).

    PubMed  Article  CAS  Google Scholar 

  58. 58.

    McPeek, R. M. Competition between saccade goals in the superior colliculus produces saccade curvature. J. Neurophysiol. 89, 2577–2590 (2003).

    PubMed  Article  Google Scholar 

  59. 59.

    Bhutani, N. et al. Queuing of concurrent movement plans by basal ganglia. J. Neurosci. 33, 9985–9997 (2013).

    PubMed  Article  CAS  Google Scholar 

  60. 60.

    Costello, M. G., Zhu, D., Salinas, E. & Stanford, T. R. Perceptual modulation of motor — but not visual — responses in the frontal eye field during an urgent-decision task. J. Neurosci. 33, 16394–16408 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61.

    Franklin, D. W. & Wolpert, D. M. Computational mechanisms of sensorimotor control. Neuron 72, 425–442 (2011).

    PubMed  Article  CAS  Google Scholar 

  62. 62.

    Gottlieb, J. Attention, learning, and the value of information. Neuron 76, 281–295 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  63. 63.

    Tipper, S. P., Howard, L. A. & Houghton, G. in Control of Cognitive Processes: Attention and Performance XVIII (eds. Monsell, S. & Driver J.) 223–245 (MIT, Cambridge, 1999).

    Google Scholar 

  64. 64.

    Gallivan, J. P. et al. One to four, and nothing more: nonconscious parallel individuation of objects during action planning. Psychol. Sci. 22, 803–811 (2011).

    PubMed  Article  Google Scholar 

  65. 65.

    Gallivan, J. P., Logan, L., Wolpert, D. M. & Flanagan, J. R. Parallel specification of competing sensorimotor control policies for alternative action options. Nat. Neurosci. 19, 320–326 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  66. 66.

    Stewart, B. M., Baugh, L. A., Gallivan, J. P. & Flanagan, J. R. Simultaneous encoding of the direction and orientation of potential targets during reach planning: evidence of multiple competing reach plans. J. Neurophysiol. 110, 807–816 (2013).

    PubMed  Article  Google Scholar 

  67. 67.

    Stewart, B. M., Gallivan, J. P., Baugh, L. A. & Flanagan, J. R. Motor, not visual, encoding of potential reach targets. Curr. Biol. 24, R953–954 (2014).

    PubMed  Article  CAS  Google Scholar 

  68. 68.

    Haith, A. M., Pakpoor, J. & Krakauer, J. W. Independence of movement preparation and movement initiation. J. Neurosci. 36, 3007–3015 (2016).

    PubMed  Article  CAS  Google Scholar 

  69. 69.

    Wong, A. L. & Haith, A. M. Motor planning flexibly optimizes performance under uncertainty about task goals. Nat. Commun. 8, 14624 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Chapman, C. S. et al. Reaching for the unknown: multiple target encoding and real-time decision-making in a rapid reach task. Cognition 116, 168–176 (2010).

    PubMed  Article  Google Scholar 

  71. 71.

    Haith, A. M., Huberdeau, D. M. & Krakauer, J. W. Hedging your bets: intermediate movements as optimal behavior in the context of an incomplete decision. PLoS Comput. Biol. 11, e1004171 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. 72.

    Hudson, T. E., Maloney, L. T. & Landy, M. S. Movement planning with probabilistic target information. J. Neurophysiol. 98, 3034–3046 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Christopoulos, V. & Schrater, P. R. Dynamic integration of value information into a common probability currency as a theory for flexible decision making. PLoS Comput. Biol. 11, e1004402 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  74. 74.

    Christopoulos, V., Bonaiuto, J. & Andersen, R. A. A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives. PLoS Comput. Biol. 11, e1004104 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. 75.

    Ghez, C. et al. Discrete and continuous planning of hand movements and isometric force trajectories. Exp. Brain Res. 115, 217–233 (1997).

    PubMed  Article  CAS  Google Scholar 

  76. 76.

    Gallivan, J. P., Stewart, B. M., Baugh, L. A., Wolpert, D. M. & Flanagan, J. R. Rapid automatic motor encoding of competing reach options. Cell Rep. 18, 1619–1626 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  77. 77.

    Nashed, J. Y., Diamond, J. S., Gallivan, J. P., Wolpert, D. M. & Flanagan, J. R. Grip force when reaching with target uncertainty provides evidence for motor optimization over averaging. Sci. Rep. 7, 11703 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. 78.

    Chapman, C. S., Gallivan, J. P., Wong, J. D., Wispinski, N. J. & Enns, J. T. The snooze of lose: rapid reaching reveals that losses are processed more slowly than gains. J. Exp. Psychol. Gen. 144, 844–863 (2015).

    PubMed  Article  Google Scholar 

  79. 79.

    Welsh, T. N. The relationship between attentional capture and deviations in movement trajectories in a selective reaching task. Acta Psychol. 137, 300–308 (2011).

    Article  Google Scholar 

  80. 80.

    Pastor-Bernier, A. & Cisek, P. Neural correlates of biased competition in premotor cortex. J. Neurosci. 31, 7083–7088 (2011).

    PubMed  Article  CAS  Google Scholar 

  81. 81.

    Cisek, P. & Pastor-Bernier, A. On the challenges and mechanisms of embodied decisions. Phil. Trans. R. Soc. B 369, 20130479–20130479 (2014).

    PubMed  Article  Google Scholar 

  82. 82.

    Cos, I., Bélanger, N. & Cisek, P. The influence of predicted arm biomechanics on decision making. J. Neurophysiol. 105, 3022–3033 (2011).

    PubMed  Article  Google Scholar 

  83. 83.

    Cos, I., Medleg, F. & Cisek, P. The modulatory influence of end-point controllability on decisions between actions. J. Neurophysiol. 108, 1764–1780 (2012).

    PubMed  Article  Google Scholar 

  84. 84.

    Cos, I., Duque, J. & Cisek, P. Rapid prediction of biomechanical costs during action decisions. J. Neurophysiol. 112, 1256–1266 (2014). This study shows, using transcranial magnetic stimulation over motor cortex, that the prediction of the effort associated with competing reach options is computed rapidly and influences reach decisions within 200 ms of the presentation of those options.

    PubMed  Article  Google Scholar 

  85. 85.

    Shadmehr, R., Huang, H. J. & Ahmed, A. A. A. Representation of effort in decision-making and motor control. Curr. Biol. 26, 1929–1934 (2016).

    PubMed  Article  CAS  Google Scholar 

  86. 86.

    Morel, P., Ulbrich, P. & Gail, A. What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control. PLOS Biol. 15, e2001323 (2017). This study systematically determined the internal cost function for choosing between effortful movements and showed, using force-loaded reaches, that the effort cost associated with decision-making was similar to the cost functions in motor control. This supports the notion that motor control and decision-making are governed by similar optimization principles.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  87. 87.

    Suriya-Arunroj, L. & Gail, A. I. Plan therefore i choose: free-choice bias due to prior action-probability but not action-value. Front. Behav. Neurosci. 9, 315 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Gallivan, J. P., Barton, K. S., Chapman, C. S., Wolpert, D. M. & Flanagan, J. R. Action plan co-optimization reveals the parallel encoding of competing reach movements. Nat. Commun. 6, 7428 (2015). This paper shows, using a reaching task in which target-directed movements were executed only after one of two potential targets was cued, that the reach trajectory selected for the cued target often shares components of the movement that would have been required for the other (uncued) target, suggesting that movement specification often precedes target selection.

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Gallivan, J. P., Bowman, N. A. R., Chapman, C. S., Wolpert, D. M. & Flanagan, J. R. The sequential encoding of competing action goals involves dynamic restructuring of motor plans in working memory. J. Neurophysiol. 115, 3113–3122 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Nashed, J. Y., Crevecoeur, F. & Scott, S. H. Rapid online selection between multiple motor plans. J. Neurosci. 34, 1769–1780 (2014). This study uses mechanical perturbations to reveal the remarkable speed of sensorimotor decision-making during target-directed reaching and shows that corrective responses associated with changes in the path towards a spatial target are expressed earlier (at ~60 ms) than corrective responses associated with changes in what spatial target to attain (at ~75 ms).

    PubMed  Article  CAS  Google Scholar 

  91. 91.

    Cisek, P. Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26, 9761–9770 (2006).

    PubMed  Article  CAS  Google Scholar 

  92. 92.

    Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  93. 93.

    Wang, X.-J. Decision making in recurrent neuronal circuits. Neuron 60, 215–234 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  94. 94.

    Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    PubMed  Article  CAS  Google Scholar 

  95. 95.

    Schall, J. D. Neural basis of deciding, choosing and acting. Nat. Rev. Neurosci. 2, 33–42 (2001).

    PubMed  Article  CAS  Google Scholar 

  96. 96.

    Sugrue, L. P., Corrado, G. S. & Newsome, W. T. Choosing the greater of two goods: neural currencies for valuation and decision making. Nat. Rev. Neurosci. 6, 363–375 (2005).

    PubMed  Article  CAS  Google Scholar 

  97. 97.

    Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002). This influential paper examines the neural correlates of gradual decision formation during a random dot motion task and shows that the lateral intraparietal area accumulates sensory evidence for or against a behavioural response (in this case, an eye movement).

    PubMed  Article  CAS  Google Scholar 

  98. 98.

    Song, J. H. & Nakayama, K. Hidden cognitive states revealed in choice reaching tasks. Trends Cogn. Sci. 13, 360–366 (2009).

    PubMed  Article  Google Scholar 

  99. 99.

    Selen, L. P., Shadlen, M. N. & Wolpert, D. M. Deliberation in the motor system: reflex gains track evolving evidence leading to a decision. J. Neurosci. 32, 2276–2286 (2012). This study shows, by eliciting reflex responses via mechanical perturbations at different times during a decision task, that the state of the motor system dynamically tracks the accumulated evidence in favour of one movement versus another. This result suggests the continuous flow of an evolving decision to the motor system.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  100. 100.

    Gold, J. I. & Shadlen, M. N. Representation of a perceptual decision in developing oculomotor commands. Nature 404, 390–394 (2000).

    PubMed  Article  CAS  Google Scholar 

  101. 101.

    Kiani, R., Hanks, T. D. & Shadlen, M. N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J. Neurosci. 28, 3017–3029 (2008).

    PubMed  Article  CAS  Google Scholar 

  102. 102.

    Resulaj, A., Kiani, R., Wolpert, D. M. & Shadlen, M. N. Changes of mind in decision-making. Nature 461, 263–266 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  103. 103.

    Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex. eLife 4, e04677 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Kiani, R., Cueva, C. J., Reppas, J. B. & Newsome, W. T. Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials. Curr. Biol. 24, 1542–1547 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  105. 105.

    Hagura, N., Haggard, P. & Diedrichsen, J. Perceptual decisions are biased by the cost to act. eLife 6, e18422 (2017). This study shows that perceptual decisions can be biased by imperceptibly changing the physical resistances of reaching movements used to report those decisions. This finding suggests that the motor system, rather than simply conveying an upstream decision, can recursively influence how sensory information is processed and transformed into a decision variable.

    PubMed  PubMed Central  Article  Google Scholar 

  106. 106.

    Burk, D., Ingram, J. N., Franklin, D. W., Shadlen, M. N. & Wolpert, D. M. Motor effort alters changes of mind in sensorimotor decision making. PLoS ONE 9, e92681 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107.

    Moher, J. & Song, J. H. Perceptual decision processes flexibly adapt to avoid change-of-mind motor costs. J. Vis. 14, 1 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Proffitt, D. R., Stefanucci, J., Banton, T. & Epstein, W. The role of effort in perceiving distance. Psychol. Sci. 14, 106–112 (2003).

    PubMed  Article  Google Scholar 

  109. 109.

    Ramenzoni, V. C., Riley, M. A., Shockley, K. & Davis, T. Short article: Carrying the height of the world on your ankles: encumbering observers reduces estimates of how high an actor can jump. Q. J. Exp. Psychol. 61, 1487–1495 (2008).

    Article  Google Scholar 

  110. 110.

    Tipper, S. P., Meegan, D. & Howard, L. A. Action-centred negative priming: Evidence for reactive inhibition. Vis. Cogn. 9, 591–614 (2002).

    Article  Google Scholar 

  111. 111.

    Bhalla, M. & Proffitt, D. R. Visual–motor recalibration in geographical slant perception. J. Exp. Psychol. Hum. Percept. Perform. 25, 1076–1096 (1999).

    PubMed  Article  CAS  Google Scholar 

  112. 112.

    Kiani, R. & Shadlen, M. N. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009). This study shows that the same population of neurons that encode formation of a decision also represent the degree of certainty associated with the decision.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  113. 113.

    Fetsch, C. R., Kiani, R. & Shadlen, M. N. Predicting the accuracy of a decision: a neural mechanism of confidence. Cold Spring Harb. Symp. Quant. Biol. 79, 185–197 (2014).

    PubMed  Article  Google Scholar 

  114. 114.

    Fetsch, C. R., Kiani, R., Newsome, W. T. & Shadlen, M. N. Effects of cortical microstimulation on confidence in a perceptual decision. Neuron 83, 797–804 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  115. 115.

    Kiani, R., Corthell, L. & Shadlen, M. N. Choice certainty is informed by both evidence and decision time. Neuron 84, 1329–1342 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  116. 116.

    van den Berg, R. et al. A common mechanism underlies changes of mind about decisions and confidence. eLife 5, e12192 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    van den Berg, R., Zylberberg, A., Kiani, R., Shadlen, M. N. & Wolpert, D. M. Confidence is the bridge between multi-stage decisions. Curr. Biol. 26, 3157–3168 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  118. 118.

    Johansson, R. S. & Flanagan, J. R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009).

    PubMed  Article  CAS  Google Scholar 

  119. 119.

    Diedrichsen, J. & Kornysheva, K. Motor skill learning between selection and execution. Trends Cogn. Sci. 19, 227–233 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Wiener, J. M., Ehbauer, N. N. & Mallot, H. A. Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving. Psychol. Res. 73, 644–658 (2009).

    PubMed  Article  CAS  Google Scholar 

  121. 121.

    MacGregor, J. N. & Ormerod, T. Human performance on the traveling salesman problem. Percept. Psychophys. 58, 527–539 (1996).

    PubMed  Article  CAS  Google Scholar 

  122. 122.

    Vickers, D., Butavicius, M., Lee, M. & Medvedev, A. Human performance on visually presented Traveling Salesman problems. Psychol. Res. 65, 34–45 (2001).

    PubMed  Article  CAS  Google Scholar 

  123. 123.

    Diamond, J. S., Wolpert, D. M. & Flanagan, J. R. Rapid target foraging with reach or gaze: The hand looks further ahead than the eye. PLoS Comput. Biol. 13, e1005504 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  124. 124.

    Charnov, E. L. Optimal foraging, the marginal value theorem. Theor. Popul. Biol. 9, 129–136 (1976).

    PubMed  Article  CAS  Google Scholar 

  125. 125.

    Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton Univ. Press, Princeton, 1986).

    Google Scholar 

  126. 126.

    Baldauf, D. & Deubel, H. Attentional landscapes in reaching and grasping. Vision Res. 50, 999–1013 (2010).

    PubMed  Article  Google Scholar 

  127. 127.

    Pfeiffer, B. E. & Foster, D. J. Hippocampal place-cell sequences depict future paths to remembered goals. Nature 497, 74–79 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  128. 128.

    Moser, E. I., Kropff, E. & Moser, M.-B. Place cells, grid cells, and the brain’s spatial representation system. Annu. Rev. Neurosci. 31, 69–89 (2008).

    PubMed  Article  CAS  Google Scholar 

  129. 129.

    Najemnik, J. & Geisler, W. S. Optimal eye movement strategies in visual search. Nature 434, 387–391 (2005).

    PubMed  Article  CAS  Google Scholar 

  130. 130.

    Yang, S. C., Lengyel, M. & Wolpert, D. M. Active sensing in the categorization of visual patterns. eLife 5, e12215 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Mazzoni, P. & Krakauer, J. W. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26, 3642–3645 (2006).

    PubMed  Article  CAS  Google Scholar 

  132. 132.

    Taylor, J. A., Krakauer, J. W. & Ivry, R. B. Explicit and implicit contributions to learning in a sensorimotor adaptation task. J. Neurosci. 34, 3023–3032 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  133. 133.

    Fernandez-Ruiz, J., Wong, W., Armstrong, I. T. & Flanagan, J. R. Relation between reaction time and reach errors during visuomotor adaptation. Behav. Brain Res. 219, 8–14 (2011).

    PubMed  Article  Google Scholar 

  134. 134.

    Katz, L. N., Yates, J. L., Pillow, J. W. & Huk, A. C. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  135. 135.

    Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  136. 136.

    Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  137. 137.

    Shadlen, M. N. & Kiani, R. Decision making as a window on cognition. Neuron 80, 791–806 (2013).

    PubMed  Article  CAS  Google Scholar 

  138. 138.

    Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015). This paper records neuronal activity from six cortical regions while monkeys make decisions on the motion or colour of stimuli and shows that sensorimotor choices emerge in a frontoparietal network.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  139. 139.

    Gallivan, J. P., Cant, J. S., Goodale, M. A. & Flanagan, J. R. Representation of object weight in human ventral visual cortex. Curr. Biol. 24, 1866–1873 (2014).

    PubMed  Article  CAS  Google Scholar 

  140. 140.

    Leone, F. T. M., Heed, T., Toni, I. & Medendorp, W. P. Understanding effector selectivity in human posterior parietal cortex by combining information patterns and activation measures. J. Neurosci. 34, 7102–7112 (2014).

    PubMed  Article  CAS  Google Scholar 

  141. 141.

    Kornysheva, K. & Diedrichsen, J. Human premotor areas parse sequences into their spatial and temporal features. eLife 3, e03043 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  142. 142.

    Fabbri, S., Stubbs, K. M., Cusack, R. & Culham, J. C. Disentangling representations of object and grasp properties in the human brain. J. Neurosci. 36, 7648–7662 (2016).

    PubMed  Article  CAS  Google Scholar 

  143. 143.

    Zheng, Z. Z. et al. Multivoxel patterns reveal functionally differentiated networks underlying auditory feedback processing of speech. J. Neurosci. 33, 4339–4348 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  144. 144.

    Formisano, E., De Martino, F., Bonte, M. & Goebel, R. ‘Who’ is saying ‘what’? Brain-based decoding of human voice and speech. Science 322, 970–973 (2008).

    PubMed  Article  CAS  Google Scholar 

  145. 145.

    Polyn, S. M. Category-specific cortical activity precedes retrieval during memory search. Science 310, 1963–1966 (2005).

    PubMed  Article  CAS  Google Scholar 

  146. 146.

    Tong, F. & Pratte, M. S. Decoding patterns of human brain activity. Annu. Rev. Psychol. 63, 483–509 (2012).

    PubMed  Article  Google Scholar 

  147. 147.

    Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37, 435–456 (2014).

    PubMed  Article  CAS  Google Scholar 

  148. 148.

    Rissman, J. & Wagner, A. D. Distributed representations in memory: insights from functional brain imaging. Annu. Rev. Psychol. 63, 101–128 (2012).

    PubMed  Article  Google Scholar 

  149. 149.

    Gallivan, J. P. & Chapman, C. S. Three-dimensional reach trajectories as a probe of real-time decision-making between multiple competing targets. Front. Neurosci. 8, 215 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  150. 150.

    Freeman, J. B., Dale, R. & Farmer, T. A. Hand in motion reveals mind in motion. Front. Psychol. 2, 59 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  151. 151.

    Koop, G. J. & Johnson, J. G. The response dynamics of preferential choice. Cogn. Psychol. 67, 151–185 (2013).

    PubMed  Article  Google Scholar 

  152. 152.

    Scherbaum, S., Dshemuchadse, M., Fischer, R. & Goschke, T. How decisions evolve: the temporal dynamics of action selection. Cognition 115, 407–416 (2010).

    PubMed  Article  Google Scholar 

  153. 153.

    Freeman, J. B., Nakayama, K. & Ambady, N. Finger in flight reveals parallel categorization across multiple social dimensions. Soc. Cogn. 31, 792–805 (2013).

    Article  Google Scholar 

  154. 154.

    Spivey, M. J. & Dale, R. Continuous dynamics in real-time cognition. Curr. Dir. Psychol. Sci. 15, 207–211 (2006).

    Article  Google Scholar 

  155. 155.

    Welsh, T. N. & Elliott, D. The effects of response priming on the planning and execution of goal-directed movements in the presence of a distracting stimulus. Acta Psychol. 119, 123–142 (2005).

    Article  Google Scholar 

  156. 156.

    Wood, D. K. et al. Visual salience dominates early visuomotor competition in reaching behavior. J. Vis. 11, 16 (2011).

    PubMed  Article  Google Scholar 

  157. 157.

    Chapman, C. S. et al. Counting on the motor system: rapid action planning reveals the format- and magnitude-dependent extraction of numerical quantity. J. Vis. 14, 30 (2014).

    PubMed  Article  Google Scholar 

  158. 158.

    Dotan, D. & Dehaene, S. How do we convert a number into a finger trajectory? Cognition 129, 512–529 (2013).

    PubMed  Article  Google Scholar 

  159. 159.

    Santens, S., Goossens, S. & Verguts, T. Distance in motion: response trajectories reveal the dynamics of number comparison. PLoS ONE 6, e25429 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  160. 160.

    Song, J.-H. & Nakayama, K. Numeric comparison in a visually-guided manual reaching task. Cognition 106, 994–1003 (2008). This study provides a particularly elegant example of the ways in which top-down cognitive information can influence reach movement trajectories.

    PubMed  Article  Google Scholar 

  161. 161.

    Pesquita, A., Chapman, C. S. & Enns, J. T. Humans are sensitive to attention control when predicting others’ actions. Proc. Natl Acad. Sci. USA 113, 8669–8674 (2016).

    PubMed  Article  CAS  Google Scholar 

  162. 162.

    Rotman, G., Troje, N. F., Johansson, R. S. & Flanagan, J. R. Eye movements when observing predictable and unpredictable actions. J. Neurophysiol. 96, 1358–1369 (2006).

    PubMed  Article  Google Scholar 

  163. 163.

    Vaziri-Pashkam, M., Cormiea, S. & Nakayama, K. Predicting actions from subtle preparatory movements. Cognition 168, 65–75 (2017).

    PubMed  Article  Google Scholar 

  164. 164.

    Scott, S. H. The computational and neural basis of voluntary motor control and planning. Trends Cogn. Sci. 16, 541–549 (2012).

    PubMed  Article  Google Scholar 

Download references


The authors thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Institute of Health Research, the Canadian Foundation for Innovation, the Wellcome Trust and the Royal Society Noreen Murray Professorship in Neurobiology (to D.M.W.).

Reviewer information

Nature Reviews Neuroscience thanks A. Gail, T. Welsh and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information




J.P.G., C.S.C., D.M.W. and J.R.F. researched data for the article, made a substantial contribution to the discussion of content, wrote the article and reviewed and/or edited the manuscript before submission.

Corresponding authors

Correspondence to Jason P. Gallivan or J. Randall Flanagan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Movement trajectories

A series of arm configurations over time.

Feedforward planning

The specification of specific parameters characterizing an action (such as direction, speed or smoothness) in advance of movement.

Feedback control

The use of error signals, such as the difference between actual and desired movement, in generating a corrective action that minimizes these errors.


Random or unpredictable fluctuations and disturbances of neural, neuromuscular or environmental origin.


Processes that are components of a larger process.

Action affordances

Representations of the actions that objects and stimuli in the environment afford at the level of the sensorimotor system.


Rapid movements of the eyes that change fixation from one point to another.

Intersegmental dynamics

The interaction torques that arise when the motion of one individual arm segment propagates to the adjacent segments.

Motor representations

The coding of a stimulus and/or associated action in the motor system.

Random dot display

A visual display of moving dots frequently used in perceptual decision-making experiments. Determining the net direction of the dots can be made difficult for the observer by varying the number of dots that are moving in the same direction (coherence) compared with the number of dots that move in random (non-coherent) directions.

Accumulate to bound (drift-diffusion) model

A well-defined model in which evidence is accumulated for one or other choice options at each time step and integrated until a predetermined decision threshold is reached.

Embodied cognition

The theory that many features of cognition are shaped and constrained by the body of the individual.

Wagering tasks

A set of gambling tasks used in psychology to assess an observer’s belief about the outcome of an event or fact.

Race model

A well-defined model in which evidence for each choice option is accumulated separately. A decision is made either when one of the accumulators reaches a predetermined threshold or, when the decision is forced, the decision associated with the accumulator with the highest evidence is selected.

Temporal discounting

The discounting of the value of items or rewards as a function of time, with their value being deemed greater the closer they approach the present time.

Active sensing

An active strategy through which the body’s sensors are directed so as to maximally extract goal-relevant information.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gallivan, J.P., Chapman, C.S., Wolpert, D.M. et al. Decision-making in sensorimotor control. Nat Rev Neurosci 19, 519–534 (2018).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing