Computations underlying sensorimotor learning

The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning from a computational perspective. We focus on studies in which computational models are used to elucidate basic mechanisms underlying adaptation and skill acquisition in human behavior.This work was supported by the Wellcome Trust, the Human Frontiers Science Program, the Royal Society (Noreen Murray Professorship in Neurobiology to D.M.W.) and the Canadian Institutes of Health Research.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.conb.2015.12.00

Different gaze strategies during eye versus hand tracking of a moving target

International audienceThe ability to visually track, using smooth pursuit eye movements, moving objects is critical in both perceptual and action tasks. Here, by asking participants to view a moving target or track it with their hand, we tested whether different task demands give rise to different gaze strategies. We hypothesized that during hand tracking, in comparison to eye tracking, the frequency of catch-up saccades would be lower, and the smooth pursuit gain would be greater, because it limits the loss of stable retinal and extra-retinal information due to saccades. In our study participants viewed a visual target that followed a smooth but unpredictable trajectory in a horizontal plane and were instructed to either track the target with their gaze or with a cursor controlled by a manipulandum. Although the mean distance between gaze and target was comparable in both tasks, we found, consistent with our hypothesis, an increase in smooth pursuit gain and a decrease in the frequency of catch-up saccades during hand tracking. We suggest that this difference in gaze behavior arises from different tasks demands. Whereas keeping gaze close to the target is important in both tasks, obtaining stable retinal and extra-retinal information is critical for guiding hand movement

Q&A: Robotics as a tool to understand the brain.

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Computations underlying sensorimotor learning.

The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning from a computational perspective. We focus on studies in which computational models are used to elucidate basic mechanisms underlying adaptation and skill acquisition in human behavior.This work was supported by the Wellcome Trust, the Human Frontiers Science Program, the Royal Society (Noreen Murray Professorship in Neurobiology to D.M.W.) and the Canadian Institutes of Health Research.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.conb.2015.12.00

Probe Brane Dynamics and the Cosmological Constant

Recently a brane world perspective on the cosmological constant and the hierarchy problems was presented. Here, we elaborate on some aspects of that particular scenario and discuss the stability of the stationary brane solution and the dynamics of a probe brane. Even though the brane is unstable under a small perturbation from its stationary position, such instability is harmless when the 4-D cosmological constant is very small, as is the case of our universe. One may also introduce radion stabilizing potentials in a more realistic scenario.Comment: 13 pages, 1 figure, REVTE

Planning Ahead: Object-Directed Sequential Actions Decoded from Human Frontoparietal and Occipitotemporal Networks.

Object-manipulation tasks (e.g., drinking from a cup) typically involve sequencing together a series of distinct motor acts (e.g., reaching toward, grasping, lifting, and transporting the cup) in order to accomplish some overarching goal (e.g., quenching thirst). Although several studies in humans have investigated the neural mechanisms supporting the planning of visually guided movements directed toward objects (such as reaching or pointing), only a handful have examined how manipulatory sequences of actions-those that occur after an object has been grasped-are planned and represented in the brain. Here, using event-related functional MRI and pattern decoding methods, we investigated the neural basis of real-object manipulation using a delayed-movement task in which participants first prepared and then executed different object-directed action sequences that varied either in their complexity or final spatial goals. Consistent with previous reports of preparatory brain activity in non-human primates, we found that activity patterns in several frontoparietal areas reliably predicted entire action sequences in advance of movement. Notably, we found that similar sequence-related information could also be decoded from pre-movement signals in object- and body-selective occipitotemporal cortex (OTC). These findings suggest that both frontoparietal and occipitotemporal circuits are engaged in transforming object-related information into complex, goal-directed movements

Attenuation of Self-Generated Tactile Sensations Is Predictive, not Postdictive

When one finger touches the other, the resulting tactile sensation is perceived as weaker than the same stimulus externally imposed. This attenuation of sensation could result from a predictive process that subtracts the expected sensory consequences of the action, or from a postdictive process that alters the perception of sensations that are judged after the event to be self-generated. In this study we observe attenuation even when the fingers unexpectedly fail to make contact, supporting a predictive process. This predictive attenuation of self-generated sensation may have evolved to enhance the perception of sensations with an external cause

Action plan co-optimization reveals the parallel encoding of competing reach movements.

Several influential cognitive theories propose that in situations affording more than one possible target of action, we prepare multiple competing movements before selecting one. Here we provide direct evidence for this provocative but largely untested idea and demonstrate why preparing multiple movements is computationally advantageous. Using a reaching task in which movements are initiated after one of two potential targets is cued, we show that the movement generated for the cued target borrows components of the movement that would have been required for the other, competing target. This interaction can only arise if multiple potential movements are fully specified in advance and we demonstrate that it reduces the time required to launch a given action plan. Our findings suggest that this co-optimization of motor plans is highly automatic and largely occurs outside conscious awareness.The study was supported by the Natural Sciences and Engineering Research Council of Canada; the Wellcome Trust; the Human Frontiers Science Program; and the Royal Society. J.P.G. was supported by Banting postdoctoral fellowship and CIHR postdoctoral fellowship awards.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms842

Motor, not visual, encoding of potential reach targets

SummaryWe often encounter situations in which there are multiple potential targets for action, as when, for example, we hear the request “could you pass the …” at the dinner table. It has recently been shown that, in such situations, activity in sensorimotor brain areas represents competing reach targets in parallel prior to deciding between, and then reaching towards, one of these targets [1]. One intriguing possibility, consistent with the influential notion of action ‘affordances’ [2], is that this activity reflects movement plans towards each potential target [3]. However, an equally plausible explanation is that this activity reflects an encoding of the visual properties of the potential targets (for example, their locations or directions), prior to any target being selected and the associated movement plan being formed. Notably, previous work showing spatial averaging behaviour during reaching, in which initial movements are biased towards the midpoint of the spatial distribution of potential targets [4–6], remains equally equivocal concerning the motor versus visual encoding of reach targets. Here, using a rapid reaching task that disentangles these two competing accounts, we show that reach averaging behaviour reflects the parallel encoding of multiple competing motor plans. This provides direct evidence for theories proposing that the brain prepares multiple available movements before selecting between them [3]

Multiple motor memories are learned to control different points on a tool.

Skillful object manipulation requires learning the dynamics of objects, linking applied force to motion 1 ,2 . This involves the formation of a motor memory 3 ,4 , which has been assumed to be associated with the object, independent of the point on the object that one chooses to control. Importantly, in manipulation tasks, different control points on an object, such as the rim of a cup when drinking or its base when setting it down, can be associated with distinct dynamics. Here we show that opposing dynamic perturbations, which interfere when controlling a single location on an object, can be learned when each is associated with a separate control point. This demonstrates that motor memory formation is linked to control points on the object, rather than the object per se . We also show that the motor system only generates separate memories for different control points if they are linked to different dynamics, allowing efficient use of motor memory. To account for these results, we develop a normative switching state-space model of motor learning, in which the association between cues (control points) and contexts (dynamics) is learned rather than fixed. Our findings uncover an important mechanism through which the motor system generates flexible and dexterous behavior