Motor Control, 2016, 20, 162 -170 http://dx.doi.org/10.1123/mc.2015-0027 © 2016 Human Kinetics, Inc.
COMMENTARY
Online Corrections are Faster Because Movement Initiation Must Disengage Postural Control Tyler Cluff and Stephen H. Scott Queen’s University The target article by Smeets’ and colleagues examines how visual feedback is used to guide voluntary movements. Smeets et al.’s framework is based on the notion that visual information from our body and environment is processed in a serial manner including detecting (detection) and identifying a behavioral goal (identification), selecting an appropriate action (movement selection), and finally moving the limb (movement execution). Smeets and colleagues argue that corrective responses are initiated faster than voluntary actions because they bypass the detection stage, reasoning that corrective responses are generated in situations where the subject does not consciously perceive any disturbance. In this brief commentary, we suggest the time saved in generating corrective responses is not due to the need to perceive (detection process). Rather, we argue the additional time taken to initiate a simple reaction time task reflects the need to disengage ongoing postural control, which is unnecessary for corrective responses during movement. We also argue that bottomup processes, whether visual or somatosensory, can rapidly alter motor selection/ execution but are not captured in Smeets and colleagues’ serial model. Finally, we suggest the exploration of these processes is difficult when only hand kinematics are quantified, and that detailed analysis of muscle activity (EMG) provides greater fidelity to address these questions. Our preference is to interpret the motor system using optimal feedback control (OFC), a framework that emphasizes the importance of sensory feedback in voluntary motor actions (Crevecoeur et al., 2014; Scott, 2004) and is mentioned briefly at the end of the target article by Smeets and colleagues. In a simplified way, OFC suggests the motor system finds the best way to perform a given task (behavioral goal; Figure 1a; Todorov and Jordan, 2002). This is accomplished by computing feedback gains (optimal feedback control policy; Figure 1a) when presented with a behavioral goal (e.g., reach to a spatial target) that specify how to move to the goal based on the present state of the limb. The resulting feedback gains form a time-varying control policy that implicitly considers the physical properties of the limb and environment (biomechanical plant; Figure 1a), and is setup for the present goal of the task. Cluff and Scott are with the Centre for Neuroscience Studies, Queen’s University, Kingston, ON Canada. Address author correspondence to Stephen H. Scott at [email protected]. 162
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Figure 1 — A. Illustration of basic processes of the OFC framework. The selection of a behavioral goal selects the control policy (task selection). The control policy (optimal feedback control policy) converts sensory feedback into motor commands that best satisfy the task goal. These motor commands are sent to the motor periphery (biomechanical plant). An efferent copy of the motor command is used internally to predict the consequences of motor actions, and combined with feedback from sensory receptors to estimate the state of the body and motor goals (optimal state and goal estimation). State and goal estimation are used by the control policy to make corrective responses or switch goals during movement. Adapted from Crevecoeur et al. (2014) and Scott (2012) with permission. B. Rapid online decisions following mechanical perturbations. On random trials, perturbations were used to bump the subject’s hand toward the obstacle. Subjects either corrected back between the obstacles (blue) or deviated around the obstacles (red) to move to the spatial goal. Decisions to move between (blue) or around the obstacles (red) occurred in muscle activity (∆EMG relative to unperturbed trials) ~60 ms after perturbation onset. C. Reaching task with one or multiple spatial goals. Differences in muscle activity did not emerge until ~75 ms after the perturbation for decisions to move to the left (red) or central target (blue). Muscle responses are aligned to perturbation onset (solid vertical line). R1 (25–50ms), R2 (50–75ms), and R3 (75–105ms) denote standard time epochs of the muscle stretch response, and EV indicates the early voluntary component. Shaded curve traces represent ± 1 SEM *p < .05. Adapted from Nashed et al. (2014) with permission. MC Vol. 20, No. 2, 2016
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Many studies have shown that the somatomotor system can generate rapid, goaldirected corrections following mechanical disturbances of the limb. These flexible corrections account for many features of the task, including the size and position of the behavioral goal (Pruszynski et al., 2008; Nashed et al., 2012; Kurtzer et al., 2014), flexible bimanual control (Diedrichsen, 2007; Dimitriou et al., 2013; Omrani et al., 2013), and the temporal urgency to return to a spatial target when the limb is disturbed during posture control (Crevecoeur et al., 2013). Goal- directed corrections typically emerge in muscle activity in the long-latency time window, ~60–75 ms after the onset of a mechanical perturbation. Neurophysiological investigations have linked the flexibility of long-latency responses with transcortical pathways that recruit distributed brain areas, including cerebellum (Strick, 1979), primary somatosensory cortex (Fromm and Evarts, 1982), premotor cortex (Boudreau et al., 2001), and primary motor cortex (Evarts and Tanji, 1976; Cheney and Fetz, 1984; Pruszynski et al., 2011, 2014; Omrani et al., 2014). Although slower than somatosensory feedback, visual feedback can also lead to rapid goal-directed corrections (Goodale et al., 1986; Pélisson et al., 1986; van Sonderen et al., 1988; Prablanc and Martin, 1992). Visuomotor responses are modulated by features of the task, including the size and orientation of the goal (Knill et al., 2011), and the relevance of visual perturbations to task completion (Franklin and Wolpert, 2008). Visual shifts of a spatial goal evoke distributed patterns of activity in limb motor circuits, including dorsal premotor cortex (>70 ms; Archambault et al., 2009, 2011), primary motor cortex (>100 ms, Georgopoulos et al., 1983; Archambault et al., 2009; 2011), and posterior parietal cortex (>120 ms; Archambault et al., 2009). Flexible responses to visual perturbations are slower than somatosensory feedback largely due to processing times in visual circuits, including the retina (15–35 ms poststimulus; Maunsell et al., 1999), lateral geniculate nucleus (30–50 ms poststimulus; Maunsell & Gibson, 1992), and primary visual cortex (60–100 ms poststimulus; Schmolesky et al., 1998). From the perspective of OFC, a key process that must be performed to initiate movement is to change the control policy from postural control to reaching. Although some argue that movement is simply a shift in postural control (Feldman, 1986), many studies suggest distinct neural processes govern the control of posture and movement. At the behavioral level, the motor system responds quite differently to small disturbances of the limb depending on whether the goal is to maintain a fixed arm posture (Pruszynski and Scott, 2012) or move to a spatial goal (Dufresne et al., 1980; Bennett, 1994; Shapiro et al., 2002; Kimura and Gomi, 2009; Kurtzer et al., 2009; Nashed et al., 2012; Cluff and Scott, 2013). If the perturbation occurs before movement, corrective responses briefly return the limb toward the start position before moving to the spatial goal (Ahmadi-Pajouh et al., 2012). Neurophysiological recordings in M1 suggest substantive changes in neural processing when switching from posture to movement control (Kurtzer et al., 2005). Mechanical disturbances applied while monkeys maintain posture control elicit strong excitatory responses in M1 to return the limb to its initial position. In contrast, the same perturbation applied during movement pushes the limb toward the target and elicits inhibitory responses in the same M1 neurons (Evarts and Fromm, 1978). Taken together, behavioral and neurophysiological studies highlight distinct control processes involved in posture and movement. This suggests an additional MC Vol. 20, No. 2, 2016
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processing step is necessary to disengage posture control before engaging a policy to initiate movement to a new spatial location. We have outlined this process in Figure 1a, which provides a schematic outlining different processes involved in the control of movement. Notably, Figure 1a shows that visual feedback is required to alter task selection, which then engages a process that switches the control policy from posture to movement control. This change in control policy can account for the additional time taken to plan and initiate movement to a goal (reach to a spatial target) as compared with making a rapid corrective response following a visual or mechanical perturbation. In some situations, it appears that switching between control policies can be hastened by mechanical disturbances to the limb. Many studies have instructed subjects performing postural control to switch to a different task when the limb is bumped by a perturbation, such as the instruction to “resist” (i.e., intervene) or “let go” (i.e., do not intervene). This requires a change in control policy from maintaining a fixed limb posture to initiating a rapid correction to ‘resist’ the perturbation, or ignoring the perturbation and relaxing the limb when instructed to let go (Colebatch et al., 1979; Rothwell et al., 1980). Recently, Pruszynski et al. (2008) developed a spatial variant of this task where perturbations were used to bump the hand toward or away from a spatial goal while maintaining posture control. In all these studies, EMG analysis revealed that goal-directed muscle responses were initiated as early as 60–70 ms after the perturbation. The article by Smeets and colleagues does not distinguish between processes associated with shifting hand feedback versus shifting the location of the spatial goal. Thus, their perspective appears to be similar to the notion that corrective responses are based on the vector difference (i.e., error) between the movement path and spatial goal (Bullock and Grossberg, 1988; Bullock et al., 1998). While shifts in hand feedback or small changes in the spatial goal may be tolerated by the control policy (when the control policy is roughly linear), larger jumps of the spatial goal may require more time to update the control policy to move the limb to the new goal. Our recent work highlights that corrective responses to attain a goal are indeed faster than switching goals (Nashed et al., 2014). In this task, subjects made reaching movements with obstacles located to the sides of a straight path between the start position and goal. Two corrective strategies were observed when the limb was bumped toward the obstacle by a perturbation (Figure 1b). Critically, this study demonstrated that subjects could switch to a secondary goal when their limb was bumped by a perturbation (Figure 1c Nashed et al., 2014). While the selection to move to the right or left of the obstacle occurred ~60 ms (Figure 1b), corrections to reach the secondary target did not emerge until ~75 ms after perturbation onset (Figure 1c). This 15 ms time shift suggests additional time is required to update to a control policy that is appropriate for the new goal. The Nashed et al. (2014) study highlights distinctions in bottom-up processing based on the nature of the required corrective response. The model proposed by Smeets and colleagues seems to suggest that corrections to avoid an obstacle and to select alternate goals both occur above the stage of movement execution in their serial model (at the movement selection and/or identification stage). However, the timing of EMG responses is clearly different for these two types of corrective responses. This suggests there is a distinction between processes to select motor MC Vol. 20, No. 2, 2016
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commands on how to attain the goal (including avoiding an obstacle) based on the present state of the limb, as compared with processes to select what goal to reach to update the control policy (Nashed et al., 2014). Perhaps we need to discuss the meaning of the detection process in the serial model proposed by Smeets and colleagues. They claim it is impossible to initiate a movement without detection, whereas detection is unnecessary for adjusting an ongoing movement. In many cases, they state that “not detecting” during online control refers to the fact that subjects did not perceive a change in the position of the target or visual feedback of their hand’s position. This suggests that detection means the conscious awareness of one’s actions. However, an elegant set of experiments that used backward masking found that subjects could react to stimuli they did not consciously perceive, even when this required selecting and initiating an action that depended on the unperceived stimulus (Taylor and McCloskey, 1990, 1996; also see Schmidt, 2002 mentioned in target article). Thus, processes to initiate movement (or make large corrective responses) may slowly update our perception of movement, but perception is not required to initiate (or correct) movement. This last point is even mentioned briefly in the article by Smeets and colleagues. Thus, it is unclear what is meant by the word detection and what evidence exists that a process called detection is required for movement initiation and not for online corrections. At one point, the authors define target detection as a comparison of sensors at two moments in time. Again, it is not clear what evidence exists that such a process must occur to initiate movement and that it does not occur for online corrections. This confusion is surprising and unfortunate given that detection is such a key concept in their article. The Nashed et al. (2014) study observed, on a few trials, kinematic evidence that selecting a secondary goal during movement occurred after a decision on how to avoid the obstacle (see Figure 7a in Nashed et al., 2014). However, in general, kinematic analysis can be less precise than EMG for identifying the timing of processes associated with rapid corrective responses. In our own series of studies, EMG changes following mechanical disturbances generate goal-directed changes in muscle activity at ~60ms, and yet, corresponding changes in hand motion were not detectable until as early as 120ms (Pruszynski et al., 2008) or as late as 190ms (Nashed et al., 2012) depending on the accuracy constraints of the task. The latency of visual corrective responses during movement is also variable across studies, ranging from 90–100 ms (Oostwoud-Wijdenes et al., 2011) to longer than 300 ms (Flash and Henis, 1991; Sarlegna et al., 2003; Saunders and Knill, 2004; d’Avella et al., 2011). However, it is unclear whether EMG changes (the earliest output of the motor system) occurred at the same time. The ability to detect differences in kinematics depends on the amplitude of change in muscle activity (Cavanagh and Komi, 1979; Norman and Komi, 1979), limb geometry, which influences relative motion of the hand in different axes of movement (Hollerbach and Flash, 1982; Graham et al., 2003), and even linear filtering methods (Robertson and Dowling, 2003). These factors make it difficult to separate distinct processes involved in visual corrective responses based only on movement kinematics. Further, it is surprising to see kinematic onsets that are detected in less than 100 ms for visual perturbations. In particular, such rapid kinematic onsets are surprising as they are faster than the detection of kinematic onsets from mechanical perturbations despite longer processing times in visual than somatosensory circuits (earliest MC Vol. 20, No. 2, 2016
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visual response at ~85ms versus goal-directed corrections at ~60ms, Cluff et al., 2014). Analysis of EMG would support claims that visual corrective responses can be observed as quickly as 100 ms in movement kinematics. Both of these issues point to the fact that EMG analysis needs to be done in conjunction with limb kinematics as this provides a comprehensive approach for understanding the timing of control processes associated with corrective responses. Although EMG signals are inherently noisy, averaging across many trials can provide very precise timing information on when the motor system generates corrective responses (don’t filter too much!). This approach helps to identify small but important shifts in the timing of corrective responses, providing important insight on the many processes involved in the generation of corrective responses that are not observable from kinematics alone.
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Kurtzer, I., Herter, T.M., & Scott, S.H. (2005). Random change in cortical load representation suggests distinct control of posture and movement. Nature Neuroscience, 8, 498–504. PubMed Kurtzer, I., Pruszynski, J.A., & Scott, S.H. (2009). Long-Latency Responses During Reaching Account for the Mechanical Interaction Between the Shoulder and Elbow Joints. Journal of Neurophysiology, 102, 3004–3015. PubMed doi:10.1152/jn.00453.2009 Maunsell, J.H., & Gibson, J.R. (1992). Visual response latencies in striate cortex of the macaque monkey. Journal of Neurophysiology, 68, 1332–1344. PubMed Maunsell, J.H., Ghose, G.M., Assad, J.A., Mc, A.D.A.M.S.C.J., Boudreau, C.E., & Noerager, B.D. (1999). Visual response latencies of magnocellular and parvocellular LGN neurons in macaque monkeys. Visual Neuroscience, 16, 1–14. PubMed doi:10.1017/ S0952523899156177 Nashed, J.Y., Crevecoeur, F., & Scott, S.H. (2012). Influence of the behavioral goal and environmental obstacles on rapid feedback responses. Journal of Neurophysiology, 108, 999–1009. PubMed doi:10.1152/jn.01089.2011 Nashed, J.Y., Crevecoeur, F., & Scott, S.H. (2014). Selection between Multiple Motor Plans. The Journal of Neuroscience, 34, 1769–1780. PubMed doi:10.1523/JNEUROSCI.3063-13.2014 Norman, R.W., & Komi, P.V. (1979). Electromechanical delay in skeletal muscle under normal movement conditions. Acta Physiologica Scandinavica, 106, 241–248. PubMed doi:10.1111/j.1748-1716.1979.tb06394.x Omrani, M., Diedrichsen, J., & Scott, S.H. (2013). Rapid feedback corrections during a bimanual postural task. Journal of Neurophysiology, 109, 147–161. PubMed doi:10.1152/jn.00669.2011 Omrani, M., Pruszynski, J.A., Murnaghan, C.D., & Scott, S.H. (2014). Perturbation-evoked responses in primary motor cortex are modulated by behavioral context. Journal of Neurophysiology, 112, 2985–3000. PubMed doi:10.1152/jn.00270.2014 Oostwoud Wijdenes, L., Brenner, E., & Smeets, J.B.J. (2011). Fast and fine-tuned corrections when the target of a hand movement is displaced. Experimental Brain Research, 214, 453–462. PubMed doi:10.1007/s00221-011-2843-4 Pélisson, D., Prablanc, C., Goodale, M.A., & Jeannerod, M. (1986). Visual control of reaching movements without vision of the limb. Experimental Brain Research, 62, 303–311. PubMed doi:10.1007/BF00238849 Prablanc, C., & Martin, O. (1992). Automatic control during hand reaching at undetected twodimensional target displacements. Journal of Neurophysiology, 67, 455–469. PubMed Pruszynski, J.A., Kurtzer, I., Nashed, J.Y., Omrani, M., Brouwer, B., & Scott, S.H. (2011). Primary motor cortex underlies multi-joint integration for fast feedback control. Nature, 478, 387–390. PubMed doi:10.1038/nature10436 Pruszynski, J.A., Kurtzer, I., & Scott, S.H. (2008). Rapid motor responses are appropriately tuned to the metrics of a visuospatial task. Journal of Neurophysiology, 100, 224–238. PubMed doi:10.1152/jn.90262.2008 Pruszynski, J.A., Omrani, M., & Scott, S.H. (2014). Goal-Dependent Modulation of Fast Feedback Responses in Primary Motor Cortex. The Journal of Neuroscience, 34, 4608–4617. PubMed doi:10.1523/JNEUROSCI.4520-13.2014 Pruszynski, J.A., & Scott, S.H. (2012). Optimal feedback control and the long-latency stretch response. Experimental Brain Research, 218, 341–359. PubMed doi:10.1007/ s00221-012-3041-8 Robertson, D.G.E., & Dowling, J.J. (2003). Design and responses of Butterworth and critically damped digital filters. Journal of Electromyography and Kinesiolog, 13, 569–573. Rothwell, J.C., Traub, M.M., & Marsden, C.D. (1980). Influence of voluntary intent on the human long-latency stretch reflex. Nature, 286, 496–498. PubMed doi:10.1038/286496a0 Sarlegna, F., Blouin, J., Bresciani, J-P., Bourdin, C., Vercher, J-L., & Gauthier, G.M. (2003). Target and hand position information in the online control of goal-directed MC Vol. 20, No. 2, 2016
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arm movements. Experimental Brain Research, 151, 524–535. PubMed doi:10.1007/ s00221-003-1504-7 Saunders, J.A., & Knill, D.C. (2004). Visual Feedback Control of Hand Movements. The Journal of Neuroscience, 24, 3223–3234. PubMed doi:10.1523/JNEUROSCI.4319-03.2004 Schmidt, T. (2002). The Finger in Flight: Real-Time Motor Control by Visually Masked Color Stimuli. Psychological Science, 13, 112–118. PubMed doi:10.1111/1467-9280.00421 Schmolesky, M.T., Wang, Y., Hanes, D.P., Thompson, K.G., Leutgeb, S., Schall, J.D., & Leventhal, A.G. (1998). Signal Timing Across the Macaque Visual System. Journal of Neurophysiology, 79, 3272–3278. PubMed Scott, S.H. (2004). Optimal feedback control and the neural basis of volitional motor control. Nature Reviews. Neuroscience, 5, 532–546. PubMed doi:10.1038/nrn1427 Scott, S.H. (2012). The computational and neural basis of voluntary motor control and planning. Trends in Cognitive Sciences, 11, 541–549. Shapiro, M.B., Gottlieb, G.L., Moore, C.G., & Corcos, D.M. (2002). Electromyographic Responses to an Unexpected Load in Fast Voluntary Movements: Descending Regulation of Segmental Reflexes. Journal of Neurophysiology, 88, 1059–1063. PubMed Strick, P.L. (1979). Cerebellar involvement in volitional muscle responses to load changes. (Vol. IV. Cerebal motor control in man: Long loop mechanisms. Progress in Clinical Neurophysiology. New York, USA: S. Karger. Taylor, J.L., & McCloskey, D.I. (1990). Triggering of preprogrammed movements as reactions to masked stimuli. Journal of Neurophysiology, 63, 439–446. PubMed Taylor, J.L., & McCloskey, D.I. (1996). Selection of motor responses on the basis of unperceived stimuli. Experimental Brain Research, 110, 62–66. PubMed doi:10.1007/ BF00241375 Todorov, E., & Jordan, M.I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5, 1226–1235. PubMed doi:10.1038/nn963 van Sonderen JF, Gon JJD van der, Gielen CC a. M. (1988). Conditions determining early modification of motor programmes in response to changes in target location. Experimental Brain Research, 71, 320–328. PubMed doi:10.1007/BF00247492
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COMMENTARY
Online Corrections are Faster Because Movement Initiation Must Disengage Postural Control Tyler Cluff and Stephen H. Scott Queen’s University The target article by Smeets’ and colleagues examines how visual feedback is used to guide voluntary movements. Smeets et al.’s framework is based on the notion that visual information from our body and environment is processed in a serial manner including detecting (detection) and identifying a behavioral goal (identification), selecting an appropriate action (movement selection), and finally moving the limb (movement execution). Smeets and colleagues argue that corrective responses are initiated faster than voluntary actions because they bypass the detection stage, reasoning that corrective responses are generated in situations where the subject does not consciously perceive any disturbance. In this brief commentary, we suggest the time saved in generating corrective responses is not due to the need to perceive (detection process). Rather, we argue the additional time taken to initiate a simple reaction time task reflects the need to disengage ongoing postural control, which is unnecessary for corrective responses during movement. We also argue that bottomup processes, whether visual or somatosensory, can rapidly alter motor selection/ execution but are not captured in Smeets and colleagues’ serial model. Finally, we suggest the exploration of these processes is difficult when only hand kinematics are quantified, and that detailed analysis of muscle activity (EMG) provides greater fidelity to address these questions. Our preference is to interpret the motor system using optimal feedback control (OFC), a framework that emphasizes the importance of sensory feedback in voluntary motor actions (Crevecoeur et al., 2014; Scott, 2004) and is mentioned briefly at the end of the target article by Smeets and colleagues. In a simplified way, OFC suggests the motor system finds the best way to perform a given task (behavioral goal; Figure 1a; Todorov and Jordan, 2002). This is accomplished by computing feedback gains (optimal feedback control policy; Figure 1a) when presented with a behavioral goal (e.g., reach to a spatial target) that specify how to move to the goal based on the present state of the limb. The resulting feedback gains form a time-varying control policy that implicitly considers the physical properties of the limb and environment (biomechanical plant; Figure 1a), and is setup for the present goal of the task. Cluff and Scott are with the Centre for Neuroscience Studies, Queen’s University, Kingston, ON Canada. Address author correspondence to Stephen H. Scott at [email protected]. 162
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Figure 1 — A. Illustration of basic processes of the OFC framework. The selection of a behavioral goal selects the control policy (task selection). The control policy (optimal feedback control policy) converts sensory feedback into motor commands that best satisfy the task goal. These motor commands are sent to the motor periphery (biomechanical plant). An efferent copy of the motor command is used internally to predict the consequences of motor actions, and combined with feedback from sensory receptors to estimate the state of the body and motor goals (optimal state and goal estimation). State and goal estimation are used by the control policy to make corrective responses or switch goals during movement. Adapted from Crevecoeur et al. (2014) and Scott (2012) with permission. B. Rapid online decisions following mechanical perturbations. On random trials, perturbations were used to bump the subject’s hand toward the obstacle. Subjects either corrected back between the obstacles (blue) or deviated around the obstacles (red) to move to the spatial goal. Decisions to move between (blue) or around the obstacles (red) occurred in muscle activity (∆EMG relative to unperturbed trials) ~60 ms after perturbation onset. C. Reaching task with one or multiple spatial goals. Differences in muscle activity did not emerge until ~75 ms after the perturbation for decisions to move to the left (red) or central target (blue). Muscle responses are aligned to perturbation onset (solid vertical line). R1 (25–50ms), R2 (50–75ms), and R3 (75–105ms) denote standard time epochs of the muscle stretch response, and EV indicates the early voluntary component. Shaded curve traces represent ± 1 SEM *p < .05. Adapted from Nashed et al. (2014) with permission. MC Vol. 20, No. 2, 2016
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Many studies have shown that the somatomotor system can generate rapid, goaldirected corrections following mechanical disturbances of the limb. These flexible corrections account for many features of the task, including the size and position of the behavioral goal (Pruszynski et al., 2008; Nashed et al., 2012; Kurtzer et al., 2014), flexible bimanual control (Diedrichsen, 2007; Dimitriou et al., 2013; Omrani et al., 2013), and the temporal urgency to return to a spatial target when the limb is disturbed during posture control (Crevecoeur et al., 2013). Goal- directed corrections typically emerge in muscle activity in the long-latency time window, ~60–75 ms after the onset of a mechanical perturbation. Neurophysiological investigations have linked the flexibility of long-latency responses with transcortical pathways that recruit distributed brain areas, including cerebellum (Strick, 1979), primary somatosensory cortex (Fromm and Evarts, 1982), premotor cortex (Boudreau et al., 2001), and primary motor cortex (Evarts and Tanji, 1976; Cheney and Fetz, 1984; Pruszynski et al., 2011, 2014; Omrani et al., 2014). Although slower than somatosensory feedback, visual feedback can also lead to rapid goal-directed corrections (Goodale et al., 1986; Pélisson et al., 1986; van Sonderen et al., 1988; Prablanc and Martin, 1992). Visuomotor responses are modulated by features of the task, including the size and orientation of the goal (Knill et al., 2011), and the relevance of visual perturbations to task completion (Franklin and Wolpert, 2008). Visual shifts of a spatial goal evoke distributed patterns of activity in limb motor circuits, including dorsal premotor cortex (>70 ms; Archambault et al., 2009, 2011), primary motor cortex (>100 ms, Georgopoulos et al., 1983; Archambault et al., 2009; 2011), and posterior parietal cortex (>120 ms; Archambault et al., 2009). Flexible responses to visual perturbations are slower than somatosensory feedback largely due to processing times in visual circuits, including the retina (15–35 ms poststimulus; Maunsell et al., 1999), lateral geniculate nucleus (30–50 ms poststimulus; Maunsell & Gibson, 1992), and primary visual cortex (60–100 ms poststimulus; Schmolesky et al., 1998). From the perspective of OFC, a key process that must be performed to initiate movement is to change the control policy from postural control to reaching. Although some argue that movement is simply a shift in postural control (Feldman, 1986), many studies suggest distinct neural processes govern the control of posture and movement. At the behavioral level, the motor system responds quite differently to small disturbances of the limb depending on whether the goal is to maintain a fixed arm posture (Pruszynski and Scott, 2012) or move to a spatial goal (Dufresne et al., 1980; Bennett, 1994; Shapiro et al., 2002; Kimura and Gomi, 2009; Kurtzer et al., 2009; Nashed et al., 2012; Cluff and Scott, 2013). If the perturbation occurs before movement, corrective responses briefly return the limb toward the start position before moving to the spatial goal (Ahmadi-Pajouh et al., 2012). Neurophysiological recordings in M1 suggest substantive changes in neural processing when switching from posture to movement control (Kurtzer et al., 2005). Mechanical disturbances applied while monkeys maintain posture control elicit strong excitatory responses in M1 to return the limb to its initial position. In contrast, the same perturbation applied during movement pushes the limb toward the target and elicits inhibitory responses in the same M1 neurons (Evarts and Fromm, 1978). Taken together, behavioral and neurophysiological studies highlight distinct control processes involved in posture and movement. This suggests an additional MC Vol. 20, No. 2, 2016
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processing step is necessary to disengage posture control before engaging a policy to initiate movement to a new spatial location. We have outlined this process in Figure 1a, which provides a schematic outlining different processes involved in the control of movement. Notably, Figure 1a shows that visual feedback is required to alter task selection, which then engages a process that switches the control policy from posture to movement control. This change in control policy can account for the additional time taken to plan and initiate movement to a goal (reach to a spatial target) as compared with making a rapid corrective response following a visual or mechanical perturbation. In some situations, it appears that switching between control policies can be hastened by mechanical disturbances to the limb. Many studies have instructed subjects performing postural control to switch to a different task when the limb is bumped by a perturbation, such as the instruction to “resist” (i.e., intervene) or “let go” (i.e., do not intervene). This requires a change in control policy from maintaining a fixed limb posture to initiating a rapid correction to ‘resist’ the perturbation, or ignoring the perturbation and relaxing the limb when instructed to let go (Colebatch et al., 1979; Rothwell et al., 1980). Recently, Pruszynski et al. (2008) developed a spatial variant of this task where perturbations were used to bump the hand toward or away from a spatial goal while maintaining posture control. In all these studies, EMG analysis revealed that goal-directed muscle responses were initiated as early as 60–70 ms after the perturbation. The article by Smeets and colleagues does not distinguish between processes associated with shifting hand feedback versus shifting the location of the spatial goal. Thus, their perspective appears to be similar to the notion that corrective responses are based on the vector difference (i.e., error) between the movement path and spatial goal (Bullock and Grossberg, 1988; Bullock et al., 1998). While shifts in hand feedback or small changes in the spatial goal may be tolerated by the control policy (when the control policy is roughly linear), larger jumps of the spatial goal may require more time to update the control policy to move the limb to the new goal. Our recent work highlights that corrective responses to attain a goal are indeed faster than switching goals (Nashed et al., 2014). In this task, subjects made reaching movements with obstacles located to the sides of a straight path between the start position and goal. Two corrective strategies were observed when the limb was bumped toward the obstacle by a perturbation (Figure 1b). Critically, this study demonstrated that subjects could switch to a secondary goal when their limb was bumped by a perturbation (Figure 1c Nashed et al., 2014). While the selection to move to the right or left of the obstacle occurred ~60 ms (Figure 1b), corrections to reach the secondary target did not emerge until ~75 ms after perturbation onset (Figure 1c). This 15 ms time shift suggests additional time is required to update to a control policy that is appropriate for the new goal. The Nashed et al. (2014) study highlights distinctions in bottom-up processing based on the nature of the required corrective response. The model proposed by Smeets and colleagues seems to suggest that corrections to avoid an obstacle and to select alternate goals both occur above the stage of movement execution in their serial model (at the movement selection and/or identification stage). However, the timing of EMG responses is clearly different for these two types of corrective responses. This suggests there is a distinction between processes to select motor MC Vol. 20, No. 2, 2016
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commands on how to attain the goal (including avoiding an obstacle) based on the present state of the limb, as compared with processes to select what goal to reach to update the control policy (Nashed et al., 2014). Perhaps we need to discuss the meaning of the detection process in the serial model proposed by Smeets and colleagues. They claim it is impossible to initiate a movement without detection, whereas detection is unnecessary for adjusting an ongoing movement. In many cases, they state that “not detecting” during online control refers to the fact that subjects did not perceive a change in the position of the target or visual feedback of their hand’s position. This suggests that detection means the conscious awareness of one’s actions. However, an elegant set of experiments that used backward masking found that subjects could react to stimuli they did not consciously perceive, even when this required selecting and initiating an action that depended on the unperceived stimulus (Taylor and McCloskey, 1990, 1996; also see Schmidt, 2002 mentioned in target article). Thus, processes to initiate movement (or make large corrective responses) may slowly update our perception of movement, but perception is not required to initiate (or correct) movement. This last point is even mentioned briefly in the article by Smeets and colleagues. Thus, it is unclear what is meant by the word detection and what evidence exists that a process called detection is required for movement initiation and not for online corrections. At one point, the authors define target detection as a comparison of sensors at two moments in time. Again, it is not clear what evidence exists that such a process must occur to initiate movement and that it does not occur for online corrections. This confusion is surprising and unfortunate given that detection is such a key concept in their article. The Nashed et al. (2014) study observed, on a few trials, kinematic evidence that selecting a secondary goal during movement occurred after a decision on how to avoid the obstacle (see Figure 7a in Nashed et al., 2014). However, in general, kinematic analysis can be less precise than EMG for identifying the timing of processes associated with rapid corrective responses. In our own series of studies, EMG changes following mechanical disturbances generate goal-directed changes in muscle activity at ~60ms, and yet, corresponding changes in hand motion were not detectable until as early as 120ms (Pruszynski et al., 2008) or as late as 190ms (Nashed et al., 2012) depending on the accuracy constraints of the task. The latency of visual corrective responses during movement is also variable across studies, ranging from 90–100 ms (Oostwoud-Wijdenes et al., 2011) to longer than 300 ms (Flash and Henis, 1991; Sarlegna et al., 2003; Saunders and Knill, 2004; d’Avella et al., 2011). However, it is unclear whether EMG changes (the earliest output of the motor system) occurred at the same time. The ability to detect differences in kinematics depends on the amplitude of change in muscle activity (Cavanagh and Komi, 1979; Norman and Komi, 1979), limb geometry, which influences relative motion of the hand in different axes of movement (Hollerbach and Flash, 1982; Graham et al., 2003), and even linear filtering methods (Robertson and Dowling, 2003). These factors make it difficult to separate distinct processes involved in visual corrective responses based only on movement kinematics. Further, it is surprising to see kinematic onsets that are detected in less than 100 ms for visual perturbations. In particular, such rapid kinematic onsets are surprising as they are faster than the detection of kinematic onsets from mechanical perturbations despite longer processing times in visual than somatosensory circuits (earliest MC Vol. 20, No. 2, 2016
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visual response at ~85ms versus goal-directed corrections at ~60ms, Cluff et al., 2014). Analysis of EMG would support claims that visual corrective responses can be observed as quickly as 100 ms in movement kinematics. Both of these issues point to the fact that EMG analysis needs to be done in conjunction with limb kinematics as this provides a comprehensive approach for understanding the timing of control processes associated with corrective responses. Although EMG signals are inherently noisy, averaging across many trials can provide very precise timing information on when the motor system generates corrective responses (don’t filter too much!). This approach helps to identify small but important shifts in the timing of corrective responses, providing important insight on the many processes involved in the generation of corrective responses that are not observable from kinematics alone.
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