The current summary is based on modern studies from the 1990s by a group of engineers and computational scientists, outside the domain of neuropsychology and kinesiology. Emphasis will be made on the upper limb (arm reaching) as its core manipulation.
Components of Sensorimotor Control
First, gathering sensory information associated with the task. When one wants to move or perform a task with one's limb, visual information is gathered through saccades. This gaze behavior is also task-specific. Our brain appears to be able to filter irrelevant sensory inputs. For example: the study in inattentional blindness when one fails to notice prominent visual stimuli unrelated to the task one is attending. It is worth noting that sensory streams are temporally delayed and noisy.
Second, motor tasks involve a sequence of decision-making processes in the presence of delay and noise. Why does the noise come into play? Because both sensory and motor system are inherently noisy, arising naturally at the molecular, synaptic, and system levels. The final product is that trial-by-trial movement production to the same target in space is bound to exhibit some variability. These underlying risks can be viewed with the context of reward, especially during a period of motor learning. Even when a person faces the same motor learning task, one can be a risk-averse (exploitation) or risk-seeking (exploration).
Third, our nervous system can be modeled as a controller. Traditionally, there are two basic controllers for motor control and learning: the feedback and feedforward controller. Feedback control, as the name implies, refers to the control of voluntary movements using sensory feedback. In contrast, feedforward control doesn't depend on any feedback mechanisms. Given that our sensory inflow has an inherent delay (150 - 200 msec), it becomes unreliable to rely on for an accurate movement control. To meet the demand of the motor tasks, we often rely on feedforward control which is a predictive control. As we produce certain movement, we make also make prediction about the sensory consequences of that movement. This is done using of efference copy of the motor command and the difference between the predicted sensory consequence and the actual sensory feedback will be used in state estimation.
The last controller is related to the biomechanical properties of the body and the tools used. It is within the topic of arm impedance. Impedance control depends on a few factors such as arm stiffness, i.e. how springy the musculatures are. Like the internal model, impedance control is also inspired by concepts in engineering and biomechanics. Example: we can modify the way we grip a tool (hand stiffness, arm stiffness) produced through co-contraction of the opposing muscles. Although co-contraction can be a solution to the motor task, it is inherently unstable as the sensorimotor system is noisy.
Recent Techniques or Methodologies
We can study motor learning in various ways, It can be studied through behavioral studies and quantitative movement analysis. Modern motor learning literature typically includes three well-known behavioral paradigms:
- Sequence learning: a type of motor skill learning where it employs serial reaction time tasks (SRTT). Here, a participant is asked to make a sequence of button pressing, key tap, or finger flexion. Motor performance is measured by the reaction or response time. It does not directly deal with the kinematic and dynamic features of motor learning. In more specific ways, learning is measured by the difference in reaction time between the random sequence and learned sequence.
- Visuomotor rotation: this can be achieved by e.g., providing a set of prism worn by the participant or by a certain mechanism to distort the association between the visual feedback and the actual arm movement. The performance is measured by the movement deviation. This method introduces a mismatch between two related sensory inputs: visual and proprioception.
- Force field paradigm: a participant performs reaching movement with a robotic manipulandum, capable of producing a velocity-dependent force that perturbs the movement trajectory. The presence of the force changes the dynamic of the motor task. The process of reaching motor performance signifies adaptation. The sudden removal of the force causes the trajectory to deflect to the opposite direction know as an after-effect. A more novel idea probes trial-by-trial performance in terms of the magnitude of the lateral force the participant produces. This is achieved by introducing catch trials in the form of force channels. This is the method used by Shadmehr and colleagues.
The processes of motor learning can be classified by the type of information the motor system learns.
- Error-based learning: motor learning through the presence of an error, i.e. the discrepancy between the desired trajectory and the actual movement outcome. The term error also means the mismatch between the predicted sensory consequences and the observed sensory feedback. Three important behavioral paradigms to study this type of learning include visuomotor rotation, prism goggle, and force-field adaptation. Error reduction happens reasonably quick and this type of learning is known as adaptation. Improvements in adaptation reach plateau after 8-10 trials. We will focus more on this type of motor learning processes as it has been widely studied for the past decade.
- Reinforcement learning: normally observed in a redundant system. This type of learning can happen even when there is no error involved. Learning is achieved through exploration by finding the best solution in the solution manifold. It is highly dependent on the available rewards, e.g. points, punishment, currency. A study by Izawa & Shadmehr (2011) shows that reinforcement learning, in some circumstances, can substitute for adaptation when there is uncertainty about, or no, sensory prediction error.
- Use-dependent learning: not strictly a learning process, but rather, adaptation through repetitive movements. Movements to a certain direction are able to reduce variability in that direction and induce a bias towards this trained direction when reaching to other directions.
- Learning by observation: typically it involves watching others doing the movements. This type stems from the findings of mirror neurons. Observational learning may include learning from predicting error by observing the action of others.
- Structural learning: learning to extract common features of different task variants. When we know the underlying structure of the task, learning can be faster, e.g. learning to swing a tennis racket bears similarity with learning using a squash racket.
Modern research of human motor behavior has been marked by the incorporation of control engineering theories, in particular, the concepts of the internal model (see: Jordan, 1995; Kawato et al. 1987). A controller Gc(s) is used to control the process Gp(s). A good controller should be able to represent the process to be controlled. It is said that the motor system is composed of the limbs (i.e. the plant) and the controller in the nervous system, the internal model.
The internal model is an approximation of the inverse dynamics of the system being controlled. It is a model that mimics the behavior of the natural process being controlled, which refers to our motor system. This model can be adapted at any time to a novel environment, making it a suitable computational model for motor learning (refer to studies by Shadmehr's group). There are 2 variants of the internal model: forward model and inverse model.
- Forward model predicts sensory consequences from the efference copy generated during movement. Forward model is likened to a motor-to-sensory mapping. The efference copy is issued in conjunction with the motor command from the CNS. The model tries to anticipate the next state so that the movement goal is achieved and the error is minimized.
- Inverse model tries to approximate motor commands through an inverse transformation from the incoming sensory streams. This model is reactive rather than predictive. There is a close relationship between model (1) and (2).
Why can't we tickle ourselves? When we tickle our body, the central nervous system predicts the sensory consequence using the efference copy of "tickling". At the same time, there is this somatic sensation generated by the "tickling". As this sensation matches the predicted sensory consequence through the forward model, the comparator circuit in the CNS doesn't detect any mismatch.
Can Motor Learning Generalize?
After going through training of a task in one context or situation, a person is able to perform as well to a similar task but in a different context or situation. This concept is called generalization. When generalization is beneficial, it is usually termed transfer. Conversely, when it is detrimental, it is termed interference. Traditionally, the studies of generalization made use of dynamic or force field paradigm. The principles derived from those studies are associated with the concept of the internal model. On top of that, generalization is related to another concept called "motor memory". If interference occurs, the transfer of learning fails.
Using force field paradigm, it is thought that motor adaptation is able to generalize in the intrinsic coordinate system, i.e. based on internal muscular patterns of activity. A salient example of intrinsic transfer is when writing "9" by right and left hand. On the other hand, using the visuomotor paradigm (Krakauer et al., 2000), motor learning generalizes in the extrinsic coordinate system, i.e. based on the external spatial coordinate frame. Transfer in motor learning has also been studied in relevant to transfer across different movement direction. There is a limited transfer of dynamic (Gandolfo et al., 1996; Sainburg et al., 1999).
Transfer occurs in different configurations of the same arm (Shadmehr & Mussa-Ivaldi, 1994; Ghez et al., 2000; Malfait et al., 2002; Shadmehr & Moussavi, 2000). How about the interlimb transfer? Tranfer occurs from the dominant arm to the non-dominant arm and this happens in the extrinsic coordinate system (Criscimagna-Hemminger et al., 2003). The opposite is not true. Further, the interlimb transfer from the dominant to non-dominant hand occurs only when the force field is introduced abruptly (Malfait & Ostry, 2004). The gradual force field, on the other hand, does not cause the apparent interlimb transfer. It seems that interlimb transfer is regarded as a cognitive process.
The Concept of Motor Memory
The initial part of learning involves more cognitive processes, where one makes use of one's memory buffer to carry out and finish the task. This readily available, temporary buffer or space is known as working memory. A popular example of working memory is when you solve mathematical problems. The later part of learning is the period when motor performance stabilizes and involves consolidation, a term related to long-term storage of motor skills. This is why after a year of not playing the piano (or skiing), we are still able to play it as well. However, what is stored inside the memory (e.g. motor commands, task dynamic, somatic experience, etc.) is still debatable and the nature of consolidation is also conflicting, e.g. Caithness et al (2006).
Memories that can be consciously recalled are named declarative memories, e.g. memory of events, words, or facts. Conversely, memories on skills and knowledge to perform some particular actions are called procedural memories, e.g. the ability to walk or ski. Typically, the domain of motor learning deals with procedural memory, the nature of which is interesting to characterize. Smith et al. introduced a two-rate state-space model of the force-field adaptation. The model says that adaptation consists of fast learning with poor retention and slow learning with more stable retention. Krakauer & Shadmehr discuss whether the formation of such memory progresses over time from a labile state, which is susceptible to interference to a stable state, which is resistant to such interference.
The memory from experiences obtained from a rapidly changing environment leads to faster decaying and unstable motor memory. As opposed, more stable memory is achieved when exposed to a gradually changing environment. What happens when, after learning A, a person immediately learns B? Called retrograde interference, task B is able to disrupt the consolidation of A. This phenomenon does not appear after a longer period of training. There is even evidence suggesting sleeps enhance/improve consolidation. Sometimes, although we forget to do a certain task, a quick relearning is sufficient to meet the expected performance. Such a phenomenon is called saving, that is, faster relearning.
Lastly, the concepts of implicit and explicit processes have a place in the context of motor learning. Explicit processes require declarative knowledge of something. When a person learns to make a golf swing, voluntary explicit processes include, for example, adjustment to the weight of the golf stick or the knowledge on the target location. In contrast, proficiency in skill performance itself or the correct timing (or speed) involves implicit processes. In some conditions, implicit planning may override explicit strategies during a visuomotor adaptation task (Mazzoni & Krakauer, 2006). Scientists are still debating which of the two are dominant during motor learning, at different stages of learning.
The famous case of HM who couldn't recall practicing a mirror writing task but performed well in the task several days later prompts scientists to think that motor learning is purely implicit. Adaptation is seen as an implicit process, but it does not rule out the involvement of explicit processes. Such "cognitive" explicit processes are thought to serve as a form of learning strategy. Taylor and Ivry (2011) found two competing processes: explicit knowledge of target error and implicit knowledge of sensory prediction error (a la the usual adaptation mechanism). Keisler and Shadmehr used an interesting approach to examine declarative memory contribution to force-field adaptation. Subjects were adapted to force-A and then a brief exposure to force-B. After a 3-min interval, they experienced channel trials. At the same time, they had to memorize words in between. This memorization interfered with the memory of the second task B.
References
[1] Wolpert D.M., Diedrichsen J & Flanagan J.R. (2011). "Principles of sensorimotor learning". Nature Rev. Neurosci. 12: 739-751.
[2] Krakauer, J. W. and P. Mazzoni (2011). "Human sensorimotor learning: adaptation, skill, and beyond." Curr Opin Neurobiol, 21(4): 636-644.
No comments:
Post a Comment