Relaciones entre las l´ıneas de emisi´ on

Motor learning refers to improvements in performance of a motor task through practice.

In this thesis we study a type of motor learning called adaptation, typically thought of as modification of an existing internal model in order to reduce prediction errors that co-occur with changes in the sensorimotor mapping or environmental dynamics (Krakauer and Mazzoni, 2011). We study this in an attempt to reduce real-world learning behaviours to well-controlled laboratory-based tasks in which we can elicit stereotyped changes in performance over the course of an hour or two, and restrict the potential diversity in cognitive learning strategies at play. We highlight that this is a fundamental limitation of the experimental work within this thesis and we have chosen to use this approach regardless. The use of robot devices is now conventional and commonplace in the study of arm movement adaptation, enabling the fluid manipulation of environmental dynamics (Shadmehr and Mussa-Ivaldi, 1994) or sensorimotor mappings Krakauer et al. (1999), while precisely controlling sensory input and measuring behavioural responses online. Here we too use the canonical robotic reaching paradigm for testing our hypotheses.

In particular, in this thesis we focus our study on planar reaching movements performed while healthy participants grasp a robotic manipulandum called the vBOT (Howard et al., 2009) and make goal-directed reaching movements between targets (Figure 1.2A). By altering the dynamics of the manipulandum part-way through the experiment, in our case by introducing curl force fields which perturb participants with a force proportional and perpendicular to their velocity, adaptation can be assessed by monitoring incremental adjustments to participants’

reaching kinematics. We have used such force field perturbations for three primary reasons,

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Fig. 1.2 Virtual reality planar robotic manipulandum (vBOT) for motor adaptation paradigms. A) Schematic of a participant interacting with the vBOT (blue), with an arm support (adapted from Sadeghi (2018) with permission). B) Participants typically reach from a start position (grey circle) to a target (yellow circle) with veridical visual feedback of a cursor (red) vertically above the hand. C) Three different types of trials are typically used, each with different dynamics (top row). Example hand paths are illustrated for each (bottom row). On null trials participants reach with the robot unperturbed, and make canonically straight movements to the target (red paths). On curl field trials participants are perturbed with a force (blue arrows) proportional and perpendicular to their velocity, and on channel trials the participant’s hand is constrained to reach straight to the target and any perpendicular forces are measured by the vBOT to assess whether participants have generated compensatory feed-forward forces. D) The vector field of a clockwise curl perturbation of 15 Ns/m, the standard strength we use for the experiments in this thesis.

• Curl perturbations are unusual dynamic structures to encounter, and so when partic-ipants do experience them in an experiment, they can be presumed to present with similar baseline performance and little transferable skill.

• While curl fields themselves are unusual, changes in movement dynamics are not. Over visuomotor perturbations, movement dynamics regularly change in naturalistic envi-ronments, when interacting with tools or when muscles fatigue. As we are interested in the fundamental properties of motor adaptation, we consider understanding dynamic motor learning, which we believe to be a more naturalistic task for humans, to be the most suitable approach.

• Unlike visuomotor manipulations, by using force field perturbations we do not separate the visual and proprioceptive sensation of the participant’s state of the hand. Therefore we do not have to guess at the inferred hand position which will likely be between these two locations (Wolpert et al., 1995) when we draw conclusions from our data.

The vBOT (virtual reality robot) is a robotic manipulandum which is optimised for motor adaptation experiments, and which allows two-dimensional, planar movements of the handle while minimising the intrinsic dynamics of the manipulandum (Howard et al., 2009). The vBOT is therefore well suited to studying horizontal reaching movements. The vBOT can impose two-dimensional, state-dependent force perturbations on the handle end-point, enabling free, constrained or perturbed movements as desired by the experimenter (Figure 1.2C). Visual feedback of the handle location is provided by displaying the image of a cursor in a monitor mounted above the vBOT, which is reflected via a mirror back to the participant (Figure 1.2A,B). This can either be veridical, that is, located directly above the actual handle position, or altered, to induce visuomotor perturbations. Kinematic data pertaining to the handle end-point is collected from optical encoders at the joints at a rate of 1kHz. To interact with the vBOT, participants hold on to the handle with their right hand, pressing a switch which engages the robot and activates the experiment. Releasing the switch pauses the experiment and terminates any on-going trial. We attempt to minimize the effect of fatigue while interacting with the vBOT by supporting the participant’s forearm arm on a low-friction air sled.

In general participants are assumed to aim for reaching movements that have straight, smooth trajectories, as has been traditionally observed (Flash and Hogan, 1985; Morasso, 1981), such that kinematic performance can be crudely condensed to scalar metrics such as the maximum perpendicular error achieved on a trial. With practice, participants learn to predict the reach dynamics, generating feed-forward compensatory forces against the perturbation

which helps to make movements more direct (Shadmehr and Moussavi, 2000; Shadmehr and Mussa-Ivaldi, 1994; Thoroughman and Shadmehr, 2000). To measure this feed-forward adaptation explicitly, simulated springs can be used to construct a virtual channel that constrains execution to be straight towards the target (Scheidt et al., 2000). Forces generated by the participant which are perpendicular to this virtual channel can then be measured from the lateral deviation in the hand’s position relative to the channel wall.

Finally the following studies examine healthy adult participants between the ages of 18-45 with no known neurological conditions, for the simple reason that we believe understanding motor learning behaviour in a healthy adult population will lead to the simplest and most informative theory of developed brain function. We further restrict to only right-handed participants as we are unsure how handedness may affect the learning or adaptation of control.