Education, Faculty of
Kinesiology, School of
DSpace
UBCV
Szarka, Annika Marie
Master of Science - MSc
University of British Columbia
The clamped visual feedback task where participants reach towards targets on a visual display while observing task-irrelevant rotated cursor feedback has been developed to isolate the implicit component of adaptation. Despite awareness of the manipulation and instructions to ignore the visual feedback, participantsâ reach direction gradually drifts in the opposite direction of the cursor, eventually reaching an upper bound. While proprioception has been proposed as a critical sensory information source for the error signal driving adaptation, the role of the sensory prediction derived from the efference copy has largely been neglected. Current protocols do not allow for the separation of afferent and efferent sensory contributions; therefore, their individual contributions towards overall implicit adaptation behaviour are poorly understood. The purpose of this study was to examine the effect of dissociating the afferent and efferent information available during implicit sensorimotor adaptation. This was examined through an isometric aiming task. Instead of performing actual reaching movements, participants moved a visual cursor towards various targets by applying horizontal force manually to an immovable handle. During perturbation trials, the cursor followed an invariant path rotated relative to the target. Participants were instructed to ignore this task-irrelevant cursor feedback and continue to "reach" towards the target. We found that participants implicitly adapted in a task where they were required to generate isometric forces to move an error-clamped rotated cursor on a visual display instead of performing actual reaches to the target. Furthermore, we observed faster learning rates and greater overall adaptation than in a typical clamped reaching paradigm. This finding was confirmed in a secondary experiment where participants performed actual reaching movements and demonstrated significantly less adaptation. Although the isometric task did not remove proprioception itself, it decoupled this feedback source from the task. Thus, these findings suggest that while afferent proprioceptive feedback of hand position at the target location most likely plays a role in adaptation behavior, it may not be necessary to induce adaptation. The results of this study have important implications on current computational models of implicit sensorimotor adaptation.
THE ROLE OF AFFERENT VS EFFERENT SIGNALS IN IMPLICIT SENSORIMOTOR ADAPTATION by Annika Marie Szarka B.Kin, The University of British Columbia, 2022 A THESIS SUBMITTED IN PARTIAL FUFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Kinesiology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Annika Marie Szarka, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The role of afferent vs efferent signals in implicit sensorimotor adaptation submitted by Annika Marie Szarka in partial fulfilment of the requirements for the degree of Master of Science in Kinesiology Examining committee: Dr. Romeo Chua, Professor, School of Kinesiology, UBC Supervisor Dr. J. Timothy Inglis, Professor, School of Kinesiology, UBC Supervisory Committee Member Dr. Hyosub Kim, Assistant Professor, School of Kinesiology, UBC Supervisory Committee Member iii Abstract The clamped visual feedback task where participants reach towards targets on a visual display while observing task-irrelevant rotated cursor feedback has been developed to isolate the implicit component of adaptation. Despite awareness of the manipulation and instructions to ignore the visual feedback, participantsâ reach direction gradually drifts in the opposite direction of the cursor, eventually reaching an upper bound. While proprioception has been proposed as a critical sensory information source for the error signal driving adaptation, the role of the sensory prediction derived from the efference copy has largely been neglected. Current protocols do not allow for the separation of afferent and efferent sensory contributions; therefore, their individual contributions towards overall implicit adaptation behaviour are poorly understood. The purpose of this study was to examine the effect of dissociating the afferent and efferent information available during implicit sensorimotor adaptation. This was examined through an isometric aiming task. Instead of performing actual reaching movements, participants moved a visual cursor towards various targets by applying horizontal force manually to an immovable handle. During perturbation trials, the cursor followed an invariant path rotated relative to the target. Participants were instructed to ignore this task-irrelevant cursor feedback and continue to "reach" towards the target. We found that participants implicitly adapted in a task where they were required to generate isometric forces to move an error-clamped rotated cursor on a visual display instead of performing actual reaches to the target. Furthermore, we observed faster learning rates and greater overall adaptation than in a typical clamped reaching paradigm. This finding was confirmed in a secondary experiment where participants performed actual reaching movements and demonstrated significantly less adaptation. Although the isometric task did not remove proprioception itself, it decoupled this feedback source from the iv task. Thus, these findings suggest that while afferent proprioceptive feedback of hand position at the target location most likely plays a role in adaptation behavior, it may not be necessary to induce adaptation. The results of this study have important implications on current computational models of implicit sensorimotor adaptation. v Lay Summary Humans continuously adjust their movements to adapt to changes in both external surroundings (e.g., walking on a slippery surface) or internal conditions (e.g., fatigue). In a tennis match, for instance, players often find themselves adjusting their swings significantly as the ball comes from varying positions, all while adapting their movements to compensate for the gradual fatigue in their muscles. While the larger motor corrections to their swing may be conscious as they notice a change in ball flight path, smaller adjustments often occur implicitly, without awareness of the error signal driving them. These error signals are often created by a mismatch between different sensory feedback modalities, such as vision or position sense. The purpose of this work was to examine the interaction of vision and position sense in these unconscious adaptive mechanisms, and provide a better understanding of the sensory modalities required to induce implicit sensorimotor adaptation. vi Preface The study presented in the current thesis was conducted in the Perceptual-Motor Dynamics Laboratory at the University of British Columbia, Vancouver campus. At the time of completion, the study has not been submitted for publication. The protocol in this thesis was approved by the University of British Columbia behavioural research ethics board (H03-80057, H23-03987). This project was led by Annika Szarka with guidance and mentorship from the supervisory committee composed of Dr. Romeo Chua, Dr. J Timothy Inglis, and Dr. Hyosub Kim. All data presented in this thesis were collected by Annika Szarka. All data analyses and thesis document content was created by Annika Szarka with guidance from Dr. Romeo Chua, Dr. J Timothy Inglis, and Dr. Hyosub Kim. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary .................................................................................................................................. v Preface ........................................................................................................................................... vi Table of Contents .......................................................................................................................... vii List of Tables .................................................................................................................................. x List of Figures ............................................................................................................................... xi List of Abbreviations ................................................................................................................... xiii Acknowledgements ...................................................................................................................... xiv Dedication ..................................................................................................................................... xv Chapter 1: Introduction ................................................................................................................. 1 1.1 Introduction ....................................................................................................................................... 1 1.2 Adaptation: Defining Terms ............................................................................................................ 3 1.2.1 Types of Errors ............................................................................................................................................ 3 1.2.2 Types of Adaptation ..................................................................................................................................... 5 1.2.3 The Efference Copy ..................................................................................................................................... 6 1.2.3 Forward and Inverse Models ....................................................................................................................... 9 1.3 Adaptation Paradigms .................................................................................................................... 10 1.3.1 Force Field Adaptation .............................................................................................................................. 11 1.3.2 Prism Adaptation ....................................................................................................................................... 14 1.4 Visuomotor Rotation ....................................................................................................................... 16 1.4.1 Contingent Visuomotor Rotation ............................................................................................................... 16 viii 1.4.2 Clamped Visuomotor Rotation .................................................................................................................. 23 1.5 Models of Implicit Adaptation ....................................................................................................... 26 1.5.1 Visuocentric Model .................................................................................................................................... 26 1.5.2 Multisensory Integration Hypothesis ......................................................................................................... 27 1.5.3 Proprioceptive Re-Alignment Model ......................................................................................................... 29 1.6 Afferent vs Efferent Contributions to Adaptation ....................................................................... 36 1.7 Summary .......................................................................................................................................... 40 1.8 Purpose ............................................................................................................................................. 41 1.9 Hypothesis ........................................................................................................................................ 43 Chapter 2. Methods ...................................................................................................................... 46 2.1 Participants ...................................................................................................................................... 46 2.2 Experimental Setup ......................................................................................................................... 46 2.3 Experimental Task .......................................................................................................................... 48 2.4 Feedback Types ............................................................................................................................... 48 2.6 Experimental Blocks and Conditions ............................................................................................ 51 2.5 Data Collection and Measures ....................................................................................................... 53 Chapter 3: Results ........................................................................................................................ 56 3.1 Implicit Adaptation ......................................................................................................................... 56 3.2 Movement and Reaction Times ...................................................................................................... 58 3.3 Angular Error Variability and Implicit Adaptation .................................................................... 59 3.4 Force Movements ............................................................................................................................ 60 3.5 Reaching Movements ...................................................................................................................... 63 ix Chapter 4: Discussion .................................................................................................................. 67 4.1 Models of Implicit Adaptation ....................................................................................................... 68 4.1.1 Visuocentric view ...................................................................................................................................... 68 4.1.2 PReMo ....................................................................................................................................................... 69 4.1.3 Bayesian Cue Combination: the PEA model ............................................................................................. 78 4.2 Future Directions ............................................................................................................................ 86 4.3 Conclusions ...................................................................................................................................... 89 References ..................................................................................................................................... 92 Appendix A ................................................................................................................................. 102 Calibration matrix .............................................................................................................................. 102 Force mapping and cursor display .................................................................................................... 102 Appendix B ................................................................................................................................. 103 x List of Tables Table 1. Correlating angular error with peak resultant force. .................................................... 103 xi List of Figures Figure 1. Schema of the internal forward and inverse models. .................................................... 10 Figure 2. An example of internal and forward models. ................................................................ 10 Figure 3. Forcefield adaptation. ................................................................................................... 13 Figure 4. Prism Adaptation Paradigm. ......................................................................................... 15 Figure 5. Visuomotor rotation task trial types. ............................................................................. 17 Figure 6. Adaptation during a visuomotor rotation paradigm. ..................................................... 18 Figure 7. TE and SPE throughout visuomotor adaptation. ........................................................... 19 Figure 8. Explicit vs. Implicit learning. ........................................................................................ 22 Figure 9. Clamped Visual Feedback. ........................................................................................... 23 Figure 10. Schematic of implicit adaptation during the clamped feedback task. ......................... 25 Figure 11. Proprioceptive Re-alignment Model of implicit motor adaptation. ............................ 32 Figure 12. Early vs. late adaptation explained through PReMo. .................................................. 34 Figure 13. Experimental apparatus and visual display. ................................................................ 47 Figure 14. Feedback types. ........................................................................................................... 49 Figure 15. Example display of cursor movement. ........................................................................ 51 Figure 16. Experimental blocks and conditions. .......................................................................... 53 Figure 17. Measure of implicit adaptation. .................................................................................. 54 Figure 18. Mean implicit adaptation behaviour during an isometric task (n = 21). ..................... 56 Figure 19. Mean angle of force direction during baseline, late adaptation, and aftereffect measures. ....................................................................................................................................... 57 Figure 20. Reaction times over experimental phases. .................................................................. 59 Figure 21. Movement times over experimental phases. ............................................................... 59 xii Figure 22. Baseline angular variability did not correlate with early rate or asymptote of adaptation. ..................................................................................................................................... 60 Figure 23. Force Movements. ....................................................................................................... 61 Figure 24. Mean implicit adaptation behaviour across participants. ............................................ 63 Figure 25. Implicit adaptation differs between isometric and reaching tasks. ............................. 65 Figure 26. Angular endpoint error during baseline, adaptation, and aftereffect measures. ......... 66 Figure 27. Perceived hand position and proprioceptive error during an isometric task. .............. 71 Figure 28. Predicted adaptation behaviour based on PReMo during reaching and isometric aiming tasks. .................................................................................................................................. 72 Figure 29. Sensory contributions to percieved hand position. ..................................................... 74 Figure 30. Implicit adaptation and perceptual re-alignment during an isometric task when considering the efferent contribution towards perceived movement direction. ............................ 76 Figure 31. Schematic of adaptation generalization when a shift is induced at the home position. ....................................................................................................................................................... 78 Figure 32. Predicted extent of adaptation behaviour related to the perturbation size. ................. 82 xiii List of Abbreviations ANOVA: Analysis of Variance CW: Clockwise CCW: Counter-clockwise LCD: liquid crystal display ODAU: Optotrak Data Acquisition Unit PEA: Perceptual Error Adaptation PReMo: Proprioceptive Re-Alignment Model Prop: Proprioceptive SPE: Sensory Prediction Error TE: Target Error UBC: University of British Columbia xiv Acknowledgements I would first like to acknowledge my team at the University of British Columbia. Romeo, thank you for teaching me everything I know, and having more confidence in me than I have in myself. I am beyond lucky to call you my mentor. Tim, thank you for your never-ending insights and curiosities which inspired me to pursue research in the first place. Hyosub, thank you for broadening my scope of knowledge within the field and bringing contagious enthusiasm into our lab space. I would also like to acknowledge NSERC, the Faculty of Education, and the School of Kinesiology for their funding support which allowed me to pursue my research goals. I would next like to acknowledge everyone in the Osborne lab family. I would like to thank Jeff and Heather for their welcoming energy and mentorship, as well as Kyle and Keara for their valuable wisdom and advice. I would like to especially thank Solenne, Ben, and Josh for being the best cohort of masterâs students I could have ever asked for. Our friendship and support for each other extends far beyond the lab, and I will be forever grateful for that. Lastly, I would like to extend my biggest thanks my PMD Lab members. Gregg, thank you for putting up with my silly questions, teaching me countless new research skills, and being confident in me when I needed it most. Nick, thank you for making me laugh every single day, showing me more support than I couldâve ever asked for, and being the person I got to grow with most. Finally, I would like to thank all of my friends and family. Dad, thank you for demonstrating endless support in my abilities throughout my entire degree. Mom, thank you for always knowing exactly what I needed to hear in stressful moments. Julia, thank you for being the person I could count on most over the past two years. I would like to thank every single one of my friends for actively supporting me and having unwavering confidence in my ability to succeed. You are all so special, and I couldnât have done it without you. xv Dedication I dedicate this thesis to my parents, Sheila Bowling and Peter Szarka. The people who made me who I am today. Thank you. 1 Chapter 1: Introduction 1.1 Introduction Motor adaptation plays a crucial role in our ability to move through the world. Although we learn most rudimentary movements at a young age, our ability to continuously alter these movements depending on the sensory and motor context determines how successful we are when navigating through the environment. The motor system is constantly adjusting and correcting our movement to achieve our intended goal; however, we are seldom aware of how frequently this is occurring. Motor corrections happen seamlessly and often effortlessly to account for perturbations or unforeseen circumstances. Recall how you may have picked up a cup of water with unexpected condensation on it and had to quickly increase your grip strength to avoid dropping the cup. Perhaps youâve experienced the uncomfortable feeling of supposedly reaching the bottom of a staircase, when in reality there was one step left. Maybe youâve nearly fallen over while standing on the bus if it stopped abruptly and unexpectedly. Each of these situations would result in a poor outcome if your motor system were not able to adjust to the changing environment to avoid injury. Furthermore, recognize what might happen the second time you attempt to grasp the condensated glass, or the second time the bus comes to a stop. Due to your prior experience, you might grip the cup with more force right away, or anticipate the shift in bodyweight you experience as the bus decelerates. This ability to learn from previous motor experience is a hallmark characteristic of motor adaptation. In addition to external perturbations, the motor system also functions to alter movement patterns depending on the bodyâs internal state. For instance, the body often experiences fatigue, which affects how the muscles execute a given motor command. The motor system is constantly evaluating the bodyâs physiological state to account for these small but relevant changes. In contrast to these short-term changes, your 2 body segments are also changing length and size from childhood to adulthood. Envision the distinct mechanics of a fundamental motor skill like walking, ranging from a toddler, through an adult, to an elderly individual. The complex control of multi-segmented joints must be carefully executed depending on the size and strength of those segments. Nonetheless, once we acquire the motor skill of walking, we are able to perform it automatically and relatively error-free until the end stages of life. This flexibility within the motor system plays a key role in human fulfillment and survival. Motor adaptation research attempts to examine some of the key mechanisms behind these adaptation processes, and what sensory contributions may be at play. How is the motor system able to correct for sensory perturbations in a seamless and unconscious manner? What aspects of motor adaptation are available to conscious awareness? What type of sensory information is needed to induce adaptation, and which sensations are more important than others? Moreover, what are the error signals used to drive adaptation? The motor system must encounter some form of error to induce subsequent adaptation. However, there is ongoing debate regarding which error signals and accompanying sensory information sources are responsible for driving this adaptation. Nonetheless, these are some of the broad questions which guide motor adaptation studies. The proposed research attempts to isolate certain information sources in the absence of others, to assess which are necessary and/or sufficient for adaptation. This question will be examined behaviourally through a novel isometric aiming task. The research aims to expand our knowledge of implicit sensorimotor adaptation mechanisms through the lens of existing models. The results of this study will serve to elaborate upon various key assumptions of these models, but more importantly, deepen our understanding of implicit motor adaptation more broadly. 3 1.2 Adaptation: Defining Terms Motor adaptation has been defined as changes to an already well-known movement in response to environmental or physical changes (Krakauer et al., 2019). Under this definition, motor adaptation can be categorized as a subset of motor learning, which refers more broadly to any experience-dependent improvement in performance (Krakauer et al., 2019). Whereas motor learning also refers to the acquisition of new motor skills, motor adaptation pertains specifically to the modification of previously learned movements. Although we acquire most basic motor skills at a young age through motor learning, we are continuously adapting those skills to suit the given environment throughout the entire lifespan. 1.2.1 Types of Errors It is well established that motor adaptation is driven by error signals (Miall & Wolpert, 1996; Shadmehr et al., 2010; Taylor & Ivry, 2012). When a motor command is executed correctly and the goal of the movement is fulfilled, there is no need to adjust the movement. If a movement does not achieve the intended action, an error will result, signaling the motor system to adapt by changing the subsequent motor plan. Although it is agreed upon that error signals drive adaptation, there is still debate as to the relative contributions of, and interactions between, these error signals (Shadmehr et al., 2010). Questions remain about the origin of these error signals, whether they stem from incoming sensory feedback, outgoing sensory predictions, or a combination of both. To better understand these different error sources, consider this example. Imagine an archer aiming for the bullseye on a target board. As they pull back and release the arrow, they see that theyâve missed their target and hit an outer ring instead. The first and most obvious error is the difference between the goal (the bullseye) and the actual outcome (the outer ring). This error is referred to as target error (TE). The archer failed to achieve their task goal 4 based on the outcome observed. Another possibly more subtle error signal may also be present in this situation. Perhaps there was some leftward wind while the archer was preparing their shot. To compensate for this, the archer adjusted their shot to aim slightly rightward of the target. By implementing this strategy, the archer created a discrepancy between the expected and actual feedback of the movement. Here, it is crucial to establish the specific context in which we are addressing the notion of expectation. At the explicit level, the archer is still expecting to hit the target. They are implementing a strategy to deal with the external perturbation and expect to hit the target as a result. Therefore, it may be assumed that hitting the target in this situation would result in no error. However, on a lower subconscious level, the motor system is now executing a movement directed to the right of the bullseye. Therefore, at an implicit level, there is an expectation of sensory consequences which match the given action. If the shot were to land on the bullseye due to the wind, there would be a difference between the actual sensory feedback and the implicit sensory expectations based on the motor command. This difference is referred to as a sensory prediction error (SPE). It is worth noting that in this example the SPE refers to the difference between the expected and actual movement outcome, whereas SPE can also describe discrepancies about the movement itself. For example, the archer may have an expectation of how the arrow release should feel, and due to inherent motor noise, the expected sensory feedback may not match exactly to what they expected. In contrast to TE, this error signal acts independently of the actual movement goal. In this example, even if the archer did in fact hit a perfect bullseye, there would still be an SPE present at the level of the motor system due to the rightward aiming strategy to compensate for the wind. 5 1.2.2 Types of Adaptation The various types of errors which may drive adaptation can be attributed to two main processes: explicit and implicit. To understand these different processes, consider an example of skill adaptation where a dancer may be practicing a pirouette. Perhaps the dancerâs teacher is watching her and providing verbal feedback on her performance. The dancer would have to use cognitive processes to implement the given corrections. Motor adaptation resulting from voluntary strategy use is referred to as explicit learning (Krakauer et al., 2019). It is thought to be largely cognitive, as the adaptation is a result of a conscious effort to change the movement. Explicit adaptation also occurs relatively rapidly (McDougle et al., 2015). This learning type is often associated with TE, since TE feedback influences conscious planning processes (McDougle et al., 2016). If the dance teacher encouraged the dancer to raise her arms higher during the pirouette, this would highlight a TE where the dancerâs movement outcome did not achieve the movement goal. Thus, the dancer would need to consciously correct her movement to lift her arms higher and eliminate the error. In contrast, SPE is thought to induce adaptation through a different process: implicit learning. Implicit learning is a much slower component of adaptation and is thought to occur without conscious awareness (Krakauer et al., 2019). Applying this concept to our example of the dancer, imagine she practices her pirouette for an hour. Over time, her muscles will begin to fatigue, and the motor output she used for her very first pirouette of practice will no longer achieve the same movement outcome. Thus, at the same time as she is implementing conscious corrections given to her by her teacher, she is also implicitly updating her motor plan to overcome the effects of muscle fatigue. Motor adaptation research seeks to better understand the relative contributions of these distinct learning processes and their affiliated error signals. 6 1.2.3 The Efference Copy The motor systemâs ability to generate this prediction of the movement outcome is essential to successful movement execution. This research specifically examined the potential role this prediction may play towards position awareness and accompanying adaptation. Therefore, the foundational research which underpins the role of prediction in motor adaptation will now be described in detail. One of the largest challenges the motor system faces is the delay in sensory feedback. If the motor system was required to await sensory feedback before evaluating the success of a given movement, we would find ourselves constantly overcorrecting for errors. By the time that feedback indicating one correction reached the central nervous system, there may already be a need to make a different correction. To account for these delays in biological feedback loops, a prediction of the sensory feedback which would result from a given motor command is generated. When the motor command is sent to the muscles to carry out a given movement, a copy of the motor command is sent to the cerebellum (Desmurget & Grafton, 2000). A prediction of the sensory consequences of the given movement can be generated from this copy of the motor command. The predicted sensory consequences can thus be compared to the desired outcome, and corrections can be made to the motor command if the two information sources do not equal each other. Control via this feedforward mechanism is much faster than relying on afferent sensory feedback since there is no need to wait for subsequent input from the periphery (Cooke & Diggles, 1984; Gerdes & Happee, 1994; Guthrie et al., 1983; Higgins & Angle, 1970; van Sonderen et al., 1989; Wolpert et al., 1995). This copy of the motor command was defined as an âefference copyâ by Von Holst and Mittelstaedt in 1950. Around the same time, Roger Sperry coined the term âcorollary dischargeâ 7 while studying the optokinetic reaction in fish (1950). Although they reached their respective conclusions through different experimental methods, both terms effectively convey they same idea â and are therefore used interchangeably in the literature. The work of Von Holst and Mittelstaedt was based off of earlier findings by Helmholtz (1867) who suggested that the brain uses information about the motor command sent to the extraocular muscles to predict the resulting visual consequences. This work relates to the idea of perceptual constancy, which states that objects tend to give rise to similar perceptual experiences despite variations in the sensory context (Boring, 1942). For example, snow appears white when observed in broad daylight as well as in the dim evening. In the context of movement, imagine two related scenarios: one where you are walking past a stationary car, and one where you are standing still watching a car slowly drive by. Both of these situations may create the same visual flow across the retina. If this is the case, how are we so easily able to tell whether we are moving, or the object is moving? Helmholtz hypothesized that when a motor command is sent to the eyes, a prediction of the expected sensory feedback is generated and compared to the actual sensory feedback experienced (1867). This would allow the brain to differentiate between visual signals resulting from self-generated eye movements compared to those generated by external perturbations. This work served as the foundation for the re-afference principle. Von Holst and Mittelstaedt expanded on this work both experimentally and theoretically. They applied the terms âefferenceâ and âafferenceâ which refer to the motor output and incoming sensory information respectively (von Holst & Mittelstaedt, 1950). They further broke down afference into two terms: âex-afferenceâ and âre-afferenceâ. Ex-afference refers to sensory feedback originating from the external world, and re-afference refers to sensory information resulting from self-produced movement. Whenever the motor system sends a motor command to 8 the muscles, a copy of that efferent information (hence the term âefference copyâ) is sent to the brain to predict the sensory consequences of that movement. This allows the brain to distinguish between re-afferent and ex-afferent information. Von Holst and Mittelstaedt supported these ideas through experimental testing on insects. They demonstrated how the motor behaviour of a fly was modulated not simply by the generation of active movement, but by the sensory expectations of the given situation based on the efference copy (1950). This work demonstrated how the efference copy is used in an internal model control system to produce motor commands which mitigate movement errors. Since this classic work was demonstrated in a fly model, various human studies have demonstrated the predictive ability of the motor system. For example, unloading tasks have been a prime example of this phenomenon (Diedrichsen et al., 2003; Dufossé et al., 1985). Imagine you are holding a heavy book out in front of you. If someone was to come and remove it, your arms would likely move upwards due to the delay in reacting to the mechanical consequences of the removal. In contrast, if you were to remove the book yourself, the supporting hand would remain stationary. This demonstrates that you are only able to anticipate the dynamic and sensory consequences of a movement and adjust accordingly in a situation where a motor command is generated. This idea has also been clearly shown in the context of object manipulation, where grip force has been shown to scale with load force with no lag, demonstrating that the adjustment in grip force was made based on an anticipatory mechanism rather than sensory feedback (Flanagan & Wing, 1993). Feedforward control has remained an important foundational concept in modern motor control research (McNamee & Wolpert, 2019). 9 1.2.3 Forward and Inverse Models Internal models are systems which can mimic the behaviour of a natural process (Wolpert & Kawato, 1998). In the context of motor control, the internal model is a neural mechanism which can model the input and output properties of the nervous system (Kawato, 1999). Two types of internal models, forward and inverse, work to simulate different but complementary mechanisms (figure 1). The inverse model determines what motor command needs to be sent to achieve the given movement goal. The forward model predicts the dynamic and sensory consequences of a given motor command based on the efference copy. Consider a scenario where you are making a right hand turn while driving. The goal of this maneuver is to execute a smooth vehicle turn, with the corresponding motor command dispatched to your upper limbs for precise steering wheel rotation and the necessary force application. In this situation, the inverse model would first determine the exact motor command needed to achieve this goal. Once the motor command is generated, it would be sent to the upper limbs for execution. Simultaneously, a copy of the motor command (the efference copy) would be used in the forward model to predict the sensory consequences resulting from the movement. If there were any discrepancies between the sensory expectations and the actual feedback, a new motor command would be sent to correct for the discrepancy. The forward and inverse models work closely together to control our movement and achieve the task goal (figure 2). The concept of the internal model highlights how error signals are crucial toward producing motor adaptation (Imamizu, 2010). 10 Figure 1. Schema of the internal forward and inverse models. While the inverse model creates the motor command based on the desired movement trajectory, the forward model predicts the expected next state based on the efference copy of the motor command, then predicts the sensory information which would arise from that state. Figure 2. An example of internal and forward models. The movement goal is a successful right turn, while the motor command is a complex upper limb movement executed on the steering wheel. Whether error in our movement results from discrepancies between the movement outcome and the goal, or movement outcome and the expected outcome, or a combination of the two â error undoubtedly induces motor adaptation. By inducing these errors in a controlled setting, we can begin to examine how different error signals contribute to motor adaptation. 1.3 Adaptation Paradigms Adaptation can be be brought about experimentally by manipulating the sensory feedback available to the participant. This allows the experimenter to introduce error into the participantâs movement or the resulting feedback. The participant will then have to correct for the error by Forward ModelDesired State Inverse ModelMotor commandEfference copyMovement outcomeAfferent feedbackPredicted re-afferenceCurrent stateComparatorForward ModelInverse ModelMovement GoalMotor Command 11 adapting their movement. By systematically presenting the participant with erroneous feedback, the experimenter can observe how they adapt accordingly and what mechanisms may be driving the adaptation. 1.3.1 Force Field Adaptation One way we can experimentally induce error is by altering the dynamics of a movement. Picture the last time you reached your hand out of a car window while driving on the highway. While you may have intended to reach your arm in a forward direction, it was likely pushed backward due to the wind. The external force created a discrepancy between your expected and actual proprioceptive feedback based on the given motor command. In an experimental set up, a comparable situation can be simulated by having the participant perform a reaching movement while holding onto a robot arm. The robot can then create a âforce fieldâ which will alter the intended movement (Shadmehr & Mussa-Ivaldi, 1994). The force is typically exerted perpendicular to the movement direction, causing an error to the right or left of a given target. This manipulation specifically targets proprioception, because the visual feedback is veridical with respect to hand position. By introducing a force perturbation, we can alter the sensory information received from a given motor output, which will differ from the expected sensory input, causing an error signal. This proprioceptive-proprioceptive mismatch drives adaptation such that a motor command is sent to counteract the externally imposed force. Once the participant learns how much force is needed to properly execute the movement, they will be able to fully compensate for the perturbation and successfully hit the target. Most adaptation experiments follow a standard trial sequence: baseline, exposure, and post-test. Forcefield adaptation is no exception. During baseline trials, the reaching task is executed without any force perturbation. This experimental block serves to orient the participant 12 to the task, as well as assess their baseline performance when no perturbation is present, which allows for the evaluation of individual bias or variability. During this block the participant will be able to reach to the targets with relatively little angular error during baseline (figure 3a). The exposure block is when the perturbation is introduced. On the first exposure trial, when the perturbation is introduced, they will likely experience large errors in the direction of the imposed force (figure 3b). This mismatch between the expected and actual proprioceptive feedback during the movement will induce motor adaptation such that the motor plan will be updated to account for the perturbation. After repeated trials, the participant will learn how to fully compensate for the perturbation, eventually reaching performance that is similar to baseline (figure 3c). The exposure block allows for quantification of the learning process as the participant adapts to the manipulation. In the post-test block, the perturbation is removed, and the participant is once again asked to perform the same task but with no experimental manipulation of the movement or feedback. This block serves as an opportunity to examine what type of residual aftereffects the perturbation had on the participantâs behaviour. When the forcefield is removed during the post-test, they will experience aftereffects in the opposite direction of the imposed force (figure 3d). Since they have learned to adapt their reaching movement to compensate for the force, they will exhibit that learning in the post-test, even though the perturbation is no longer present. 13 Figure 3. Forcefield adaptation. The following figure is a schematic of adaptation behaviour representing a typical participant response during forefield adaptation. a) baseline trials. b) early exposure trials. c) late exposure trials. d) post-test trials. Adapted from Krakauer et al., 2019. Although this paradigm targets proprioception since the perturbation perturbs the actual hand movement, it has been demonstrated that visual feedback may be enough to adapt to a force field perturbation (Sarlegna et al., 2010). In this study, it was demonstrated that a deafferented participant was able to adapt to the novel force environment as well as control participants. Although visual feedback is kept veridical in relation to actual hand movement, the visual feedback still does not match the expectation based on the outgoing motor command, since hand position is perturbed. It was therefore hypothesized that the SPE created between the expected vs actual visual feedback was enough to drive adaptation in the deafferented participant. a) b)c) d) 14 1.3.2 Prism Adaptation Another way to induce a sensory discrepancy through experimentation is through the manipulation of vision. Prism glasses were among the first experimental devices used to create this type of error (Helmholtz, 1867). Prism lenses create an overall visual shift to the right or left. In doing so, a visual-proprioceptive discrepancy is created when making movements. Imagine a scenario where you reach for a water bottle on a table straight in front of you. As you reach for the bottle, the visual information you receive about your hand position is aligned with both the expected visual information, as well as the proprioceptive information from the limb. Once you put the glasses on, this is no longer the case. If the visual field is shifted to the right, the water bottle will seem to be positioned to the right instead of directly in front. When you attempt to reach for the bottle, you will send a motor command toward the rightward-appearing location of the object, and thus miss. Furthermore, the visual information of your limb reaching for the bottle will also be shifted to the right â creating a discrepancy between the actual visual feedback of the limb, and the expected visual feedback generated from the efference copy. This error signal is an SPE, meaning it would be present even in the absence of the TE (missing the bottle). There is also one more error signal at play â since the visual information is perturbed but the proprioceptive information is not, a visuo-proprioceptive mismatch is created. Most prism experiments follow the standard adaptation paradigm trial sequence (Redding et al., 2005; Redding & Wallace, 2006). In a baseline block, the task is executed without any induced visual perturbation. Typically, the participant would be asked to reach to a target somewhere in the periphery without visual feedback of their limb. In the exposure block, participants would wear prism lenses which shift the visual field either to the right or left. Participants would then be asked to perform the same task they were doing during the baseline 15 block, but now with perturbed vision of their limb. If the participant was wearing prism glasses which shifted their visual field to the right, they will miss the target on the first trial in the rightward direction. This mismatch between the desired and actual outcome of the reach will drive adaptation on subsequent trials. After repeated exposure to the perturbation, the participant will learn to adapt to the visual distortion by shifting their reach direction leftward. Eventually they will be able to hit the target with little error. In the post-test block, the glasses are removed, and the participant repeats the task they performed at baseline. After adapting to the prism goggles, the exposure to a sensory mismatch accompanied by motor adaptation will prompt sensory recalibration, leading to errors in reach trajectory in the opposite direction (to the left) during the aftereffect block. Adaptation may then be quantified by the difference in endpoint error between the pre- and post-test (figure 4). Figure 4. Prism Adaptation Paradigm. Adaptation during the prism adaptation task is quantified by the difference between endpoint error during the pre- and post-test. The dotted target and grey limb represent the visually shifted feedback. (A) During baseline, the participant is able to successfully hit the target. (B) When the prism goggles are first introduced, the participant misses the target in the rightward direction. (C) After multiple trials, the participant learns to adapt to the perturbed visual feedback. (D) When the glasses are removed, the participant demonstrates adaptation by missing in the leftward direction. Adapted from Pisella et al., 2007. Although prism-induced visual errors seem to be successful in inducing robust adaptation, they have some experimental limitations. Firstly, each set of prism glasses can only induce a a. b. c. d. 16 certain degree of error. Although one can use prisms of different diopters to vary the degree of visual displacement, the experimenter is limited in their ability to explore the effect of different error sizes due to the methodological challenges of changing the lenses between trials. More importantly, prisms do not allow for selective visual displacement, as they shift the entire visual field. Instead, computerized tasks have since been used to create visual perturbations because any degree of error can be induced, and visual perturbations can be restricted to the feedback of the effector only. 1.4 Visuomotor Rotation The visuomotor rotation task is one of the most-used experimental paradigms for studying sensorimotor adaptation. Although there are many variations of this task, the basic premise remains constant throughout studies. Participants are asked to reach for targets presented on a visual display, while their actual hand is occluded. Visual feedback is typically provided in the form of a cursor, and can be manipulated in various ways to target the given research question. 1.4.1 Contingent Visuomotor Rotation The classic visuomotor rotation task allows for the examination of adaptation in response to varying degrees of visual perturbation (Krakauer et al., 2000). The protocol entails displaying a cursor with a certain degree of rotation in relation to the participants' true hand position. In this task, subjects are provided visual feedback which appears either directly on an electronic display, or on a mirror reflecting the display onto a table surface. The participantâs hand is located such that it is occluded from their field of vision. Participants are asked to make quick movements out to a target location. The feedback they are given varies depending on the trial block. Veridical feedback shows a trace of movement which matches the path of the participantâs movement 17 (figure 5a). No-feedback trials provide participants with no information regarding their hand position (Figure 5b). Rotation trials provide feedback, which is rotated to a certain degree, either clockwise or counterclockwise, from the participantâs actual movement trajectory (figure 5c). Figure 5. Visuomotor rotation task trial types. a) veridical feedback trials. b) no feedback trials. c) rotated feedback trials. During veridical feedback trials, participants will be relatively accurate with any movement error being attributable to motor noise (figure 6a). On the first trial of rotated feedback, the participant will observe a large visual error between the target and the cursor (figure 6b). As the participant continues to perform rotated feedback trials, they will likely learn to adapt to the rotated feedback and reduce the visual error by aiming away from the target in the opposite direction of rotation (figure 6c). No-feedback trials may be used after a block of rotated feedback trials to observe the aftereffects of adaptation to the rotated cursor. When visual feedback is removed and participants are instructed to aim straight back to the target, they tend to keep missing in the opposite direction of the rotated feedback (figure 6d). This aftereffect reflects the implicit recalibration of the sensorimotor system. This recalibration is temporary and decays over time (figure 6e). Home positionTargetTargetHome positionReach DirectionFeedbackTargetHome positiona. b. c.FeedbackReach DirectionReach Direction 18 Figure 6. Adaptation during a visuomotor rotation paradigm. a) baseline feedback. b) early rotation exposure. c) late rotation exposure. d) post-test block. e) timecourse of adaptation throughout the experimental protocol. In the visuomotor rotation task, both SPE and TE are present. Since rotated visual feedback is provided, a visual SPE is created by the difference between the cursor location and hand location. In contrast, the TE is derived from the difference between the feedback and target location. During the first few trials with rotated feedback, the SPE and TE will be considerably large, and the degree of error will be dependent on the degree of rotation (Mazzoni, 2006) (figure 7a). Over time, the participant will begin to adapt to the visual perturbation, adjusting their intended movement so that cursor feedback lands on the target (figure 7b). Once this occurs, and the cursor feedback matches the target location, TE will be eliminated. Despite this error Trial numberAiming angle0°30°Baseline Exposure Post-Teste.TargetHome positionReach DirectionFeedbacka. b.FeedbackReach DirectionTargetHome positionc.FeedbackReach DirectionTargetHome positiond.Reach DirectionTargetHome position 19 correction, SPE will remain, because the expected sensory and actual feedback will remain misaligned since the cursor is still rotated (figure 7c). In order to understand the persistence of the SPE, it is important to recognize the difference between the expectations of the conscious brain compared to that of the motor system. Although at an explicit level the participant should expect a rotated cursor, and therefore no error would be percieved, there is still an implicit mismatch between the expected feedback based on the outgoing motor command and the actual outcome of the rotated cursor feedback. Figure 7. TE and SPE throughout visuomotor adaptation. a) When rotated feedback is first introduced, TE and SPE are roughly the same size. b) As adaptation progresses, TE begins to decrease. c) Once the cursor reaches the target, TE is eliminated, yet the SPE persists. As the participant begins to correct for error during a visuomotor rotation task, their adaptations can be attributed to two types of learning which were previously defined: explicit and implicit. TE is usually the driver for explicit learning because TE feedback influences planning processes (McDougle et al., 2016). In the case of visuomotor rotation, when the participant sees the TE created between the cursor and the target, they will implement a conscious strategy to aim away from the target in attempt to eliminate the cursor-target error. In SPETETargetReach directionHome positionSPETETargetReach directionHome positionSPETE = 0TargetReach directionHome positiona. b. c. 20 contrast, SPE is thought to induce adaptation through implicit learning. The aftereffects of implicit adaptation are minimally affected by aiming strategies (Morehead et al., 2017). Error measured during an aftereffect block is therefore often used as an indicator of implicit adaptation since any explicit strategy used in the exposure block will not be present. The interplay between these two learning types can be analyzed through a visuomotor rotation paradigm. The relationship between explicit and implicit learning during a visuomotor rotation task was first examined by Mazzoni and Krakauer (2006). In their task, the cursor was rotated 45° counterclockwise to the actual reaching direction. Participants were informed of the rotation and were instructed to employ a strategy to ensure the cursor hit the target. The target was presented straight ahead (12 oâclock), but participants were instructed to aim at a neighboring target (2 oâclock) due to the counterclockwise nature of the rotated cursor. During early adaptation when rotation was first introduced, there was a large adjustment in aiming direction to reduce TE. Although this finding may not be surprising, it was not the only adaptation observed. Over time, a slower, more subtle adaptation occurred across trials, wherein the aiming direction continued to drift away from the target. This meant that the cursor began drifting toward the neighboring target which they were instructed to aim towards. These observations can be attributed to different error signals: although explicit adjustment of the pointing direction reduced TE, the discrepancy between the expected and actual feedback created an SPE which induced further adaptation (Mazzoni & Krakauer, 2006). This slow drift, in contrast to the initial shift in aiming position, was unconscious â demonstrating the involuntary nature of implicit adaptation. This finding has since been reproduced in multiple experiments, even when participants were not instructed to employ re-aiming strategies (Benson et al., 2011; Taylor et al., 2014; Taylor & Ivry, 2011). 21 There are a few experimental methods which have been used during the visuomotor rotation task to parse apart explicit and implicit learning processes. One method used by Taylor et al (2014) used probing questions during the experimental blocks to quantify each component of learning. Participants were not explicitly informed of the rotation, but were simply instructed to make their cursor land on the target. Before participants made their reaching movement out to the target, they were asked to report where they were aiming. Their reported aiming direction was used as a direct measure of explicit adaptation, since this reveals a conscious strategy the participant was using to align the cursor with the target. On most trials, the participantâs actual reach direction was not fully aligned with their reported aim direction. Thus, the difference between their actual reach direction and reported aim was taken as a measure of implicit adaptation (figure 8a). During baseline trials where no rotation was imposed, their aim and reach direction were relatively aligned with the target. During the exposure phase, the actual hand direction continuously drifted away from the target compensating for the cursor rotation. Surprisingly, although participants consistently reported aiming away from the target in the opposite direction of the cursor, the degree to which they were aiming away decreased throughout the exposure period (figure 8b). The reason for this reduction in aiming strategy directly reflects the gradually developing implicit adaptation. The persistent SPE between the cursor and hand induces implicit adaptation. Thus, to achieve zero target error through the combination of both explicit and implicit adaptation, explicit aiming must be reduced to compensate for the accumulating implicit adaptation. If explicit aiming was maintained at a constant level, continuous development of the implicit process would lead to an increase in TE (Mazzoni, 2006). 22 Figure 8. Explicit vs. Implicit learning. a) Quantification of learning types. Participants were probed where they were aiming during a visuomotor rotation task. The difference between their aim direction and the target was taken as the explicit component of learning, and the remainder was attributed to implicit learning. b) Time course of adaptation. Although the actual hand movement continues to steadily drift away from the target over time, the explicit learning occurs rapidly and tapers off, whereas the implicit learning slowly increases throughout the experiment. Adapted from Taylor et al., 2014. Since both SPE and TE are elicited through the visuomotor rotation protocol, it is an unsuitable method for analyzing a single learning process. With task-dependent rotated feedback, both TE and SPE would be present, causing fast explicit and slower implicit adaptations to occur in parallel. Although methods have been created to separate the two processes, these methods rely on the assumption that they are operating independently. The subtraction method between aiming direction and hand direction assumes that the explicit and implicit components of adaptation are additive rather than integrative. Thus, when both processes are occurring simultaneously it is difficult to make definitive claims about either one. HomeTargetHand movementImplicit recalibrationExplicit aimingAima. b.Explicit aimingImplicit recalibrationHand movementMovement CycleAngle (degrees)0401020300 20 8040 60 23 1.4.2 Clamped Visuomotor Rotation Recently, a new paradigm using clamped visual feedback has been developed to directly probe the implicit component of adaptation without contamination of explicit strategies during the exposure phase (Morehead et al., 2017). In this task, participants are asked to make slicing movements through targets presented on a visual display. In most cases, vision of the actual hand is occluded. As the participant reaches toward the target, they receive visual feedback in the form of a cursor. On exposure trials, the cursor is rotated relative to the target, invariant with actual reach direction. In other words, no matter what the actual reach trajectory is, the cursor is always displayed at the same angular error relative to the target (figure 9). While the cursor remains temporally coupled to the reach, it is uncoupled from reach direction. Since the cursor feedback is not directionally related to the participantâs movement, they are instructed to ignore the cursor and continue aiming for the target. Considering the explicit component of the task never changes Figure 9. Clamped Visual Feedback. The cursor is always rotated relative to the target, invariant with reach direction. While reach direction eventually drifts away from the target in the opposite direction of the cursor, the aim direction remains toward the target. (aim for the target), any observed adaptation is thought to be of implicit nature. This can also be described through the error sources present during the clamped visual feedback task. A visual SPE is continuously present, as the actual visual feedback will not match the expected visual TargetFeedbackEarly reachLate reachHomeposition 24 feedback based on the intended reach trajectory. Although it may appear that TE is also present, due to the difference between the cursor feedback and the goal, it is crucial to remember that TE relates to the task goal itself. Since participants are explicitly informed that the cursor feedback is not contingent on their movement, it is no longer an indicator of task performance, and therefore signals conveying TE are removed. This contrasts with the classic visuomotor rotation task, where the rotated cursor feedback is tied to the actual hand movement, and therefore is indicative of task performance. In this classic task, participants are instructed to get the cursor to the target, rather than their hand, and thus the participant must explicitly aim away from the target to successfully complete the task. Here, it becomes more difficult to examine only the implicit component of adaptation, as both explicit and implicit processes are acting to alter the reach direction. Despite explicit instructions to ignore the cursor feedback in the clamped visual feedback task, participants begin to adapt their reach trajectories in the opposite direction of the visual cursor (Morehead et al., 2017). Angular error continues to increase as reach direction drifts away from the target until a plateau of adaptation is eventually reached (usually around 20°) (figure 10). Although initial adaptation rate has been shown to scale with clamp sizes smaller than 6°, similar asymptotic behaviour appears to be observed across a variety of clamp sizes when enough trials are performed (H. E. Kim et al., 2018). Furthermore, participants seem to be unaware of this drift in hand angle, confirming the implicit nature of the adaptation. When probed during an exposure period of adaptation, participants report that their hand remains near the target even when actual reach direction drifts up to 40° away from the target (Tsay et al., 2020). 25 Figure 10. Schematic of implicit adaptation during the clamped feedback task. During baseline, mean hand angle varies around zero. Once the clamped feedback is introduced, participantsâ hand angle begins to drift away from the cursor until eventually reaching a plateau. Despite the potentially surprising findings, this task produces robust results which have been replicated numerous times (Kim et al., 2019; Tsay et al., 2022; Tsay et al., 2021). One of the biggest questions regarding this protocol is why participants adapt at all, since they are explicitly told to ignore the visual feedback. A related point of interest is why adaptation eventually saturates, despite the persistent visual error of a constant size. Another puzzling finding is that participants are unaware of their motor error, reporting an aim direction that is up to 40° away from their actual reach direction. In attempt to better understand and explain these phenomena occurring during implicit adaptation, various models focusing on different aspects of sensory feedback and their roles have been developed. Mean Hand Angle (°)Movement Cycle0 40 80 120 1403020100Baseline Exposure 26 1.5 Models of Implicit Adaptation 1.5.1 Visuocentric Model The visuocentric view of implicit adaptation hypothesizes that adaptation is driven by a visual SPE, which in this case is the difference between the expected and actual cursor feedback (Donchin et al., 2003; Kim et al., 2021; Krakauer et al., 2019; Mazzoni and Krakauer, 2006; Shadmehr et al., 2010; Thoroughman & Shadmehr, 2000). In the clamped visual feedback task, the expected feedback would be aligned with the target, whereas the actual visual feedback is the rotated cursor. The discrepancy between these two sources of visual information drives adaptation in the opposite direction of the cursor. This visuocentric model hypothesizes that adaptation saturates when an equilibrium between learning and forgetting is reached. As the sensorimotor system is learning to adapt to the visual SPE, there is simultaneous âforgettingâ as learning decays back to baseline. The plateau of adaptation is explained as the point where the rate of learning and forgetting are equal and opposite to each other. The visuocentric model can be formalized mathematically through a standard state-space model. In its simplest version, the trial-by-trial adaptation can be represented through the following equation: ð¥!"# = ð´ð¥! + ð¾ð! Equation 1. State-space model for trial-by-trial adaptation during a visuomotor rotation task. Where x represents the behaviour on a given trial (n). In a visuomotor reaching task, the direction of the reaching movement (x) on any given trial will be based on two parameters. The first is the learning rate (K), which corresponds to the proportion of error (e) which will be corrected for on the next trial. K typically falls within the range of 0.1-0.3 (Gonzalez Castro et al., 2014; Huang et al., 2011; Jiménez, 2008; Smith et al., 2006). This means that for any given 27 trial, the motor system adjusts for 10-30% of the error. The other parameter determining reach direction is A, which represents the retention factor. This variable accounts for the systemâs tendency to decay back to baseline levels. Setting A = 1 would imply perfect retention, which is not usually the case in a visuomotor paradigm. Instead, this value falls slightly below 1 (typically greater than 0.95), indicating that there is a partial decay of the adapted response from one trial to the next (H. E. Kim et al., 2021). This also accounts for the well-established finding that reach adaptation never fully compensates for the given perturbation (Kooij et al., 2015; Vaswani et al., 2015). For example, in a visuomotor rotation task, the participant typically reaches their saturation point at a slightly smaller angle compared to the imposed rotation. Visuocentric models have the capacity to describe behaviour in both a standard visuomotor rotation and a clamped visual feedback task. 1.5.2 Multisensory Integration Hypothesis Alternative yet closely related explanations for the plateau of implicit adaptation have also been proposed. It has been suggested that instead of being offset by learning decay, the visual SPE inducing adaptation may be counteracted by a proprioceptive SPE which gradually increases during adaptation (H. E. Kim et al., 2018; Morehead et al., 2017). The proprioceptive SPE describes the difference between the actual and expected proprioceptive feedback. As the hand drifts further and further from the target, the difference between the actual and expected proprioceptive feedback increases, inducing adaptation in the opposite direction to reduce this error. Thus, adaptation plateaus when an equilibrium between the visual and proprioceptive SPEs are reached. Although this model explains the saturation point of adaptation to be caused by a slightly different mechanism than a purely visuocentric view, it still supports the notion that the visual error is the main driver for adaptation. 28 Limitations of Visuocentric Models At first glance, visually driven models of implicit adaptation are appealing. Visual information is the most prominent source of sensory information guiding our daily movement. Thus, it makes intuitive sense that the visual error in a typical reaching task would drive the observed adaptation. Some of the strongest evidence supporting a visuocentric view arises from studies of deafferented monkeys and humans. These participants have lost all or most of their proprioceptive sensation, leaving vision as their only guiding sense. Remarkably, these participants are able to adapt in a similar fashion to control participants, suggesting visual information alone is enough to drive adaptation (Bernier et al., 2006; Sarlegna et al., 2010). Additionally, the state-space equations are capable of modeling behavioural patterns extremely closely. They are able to account for the learning phase of adaptation, as well as the natural decay of learning in an aftereffect block. Despite their initial appeal, visuocentric models ignore all other important sensory information sources. It is well established that although adaptation may occur in the absence of certain senses, the motor system integrates all available sensory information when planning and controlling movement (Sober & Sabes, 2005). The loss of any given sensation typically results in some form of motor deficit. The most prominent evidence for this in the literature pertains to loss of proprioception. Early work involving surgical deafferentation of the upper limbs of monkeys resulted in inaccurate and poorly coordinated movement (Mott & Sherrington, 1997). Humans who suffer from deafferentation can perform various movements with practice but cannot perform them without the use of vision. For example, when performing a task where they must repetitively move their finger between their nose and the experimenterâs hand, although the movement is relatively error-free when their eyes are open, the patient loses track of their hand 29 position almost immediately upon closing their eyes. This outlines the importance of proprioception in guiding movement when vision is not available. The following evidence demonstrates that although vision might play an important role in adaptation, it certainly does not tell the whole story. 1.5.3 Proprioceptive Re-Alignment Model Recently, a new model of implicit adaptation has been proposed which highlights proprioception as an important contributor to adaptation. The Proprioceptive Re-alignment Model (PReMo) states that implicit adaptation is driven by a proprioceptive error between the perceived hand position and the movement goal (Tsay, Kim, et al., 2022). The perceived hand position (or in other words, where the participant feels their hand to be) is made up of a variety of sensory inputs. The model states that the perceived hand position is derived from the integrated hand position (an optimal estimate of hand position based on proprioceptive signals from actual hand position as well as the expected hand position based on the movement goal) as well as vision (the cursor)1. 1 PReMo utilizes Bayesian concepts to describe how this perceived hand position is formalized. From a Bayesian perspective, when an observer is performing a task or making a perceptual judgement, information sources are optimally combined in a manner that minimizes error. When there are multiple information sources contributing to a single percept, these sources are combined to create the most accurate estimate to achieve task goals. For example, when determining hand position, one would combine all available sources of sensory information (e.g., vision, proprioception) to generate the most accurate estimate of hand position. Each information source is weighted according to its uncertainty, where greater uncertainty would result in decreased weighting. In our hand localization example, proprioception likely has greater variability than vision. Thus, when optimally integrating these two information sources, vision will be weighted higher, and the final estimate of hand position will be closer to the visual cue than the proprioceptive cue. Another fundamental assumption from a Bayesian perspective is that additional cues will always decrease the uncertainty of a final estimate. For example, when localizing our hand, it is possible we also use other sensory cues such as tactile feedback to help localize. Although this cue may have high uncertainty on its own, adding this information source will improve our final estimate of hand position. This logic applies in the opposite direction: if an information source is removed, then the final estimate will be weighted heavier by all other available cues, and the estimate itself will be less certain. 30 To understand how the proprioceptive error is created in the clamped visual feedback task, consider the sensory information available on the first exposure block trial. The model assumes that before any crossmodal interactions between vision and proprioception occur, the nervous system will create optimal intramodal estimates of cursor and hand positions based on visual and proprioceptive information respectively (figure 11a + b). In this context, the terms crossmodal and intramodal are refering to sensory integration across different senses versus within the same sense, respectively. The integrated hand position will be an intramodal weighted estimate between proprioceptive feedback of actual hand position and expected hand position based on the outgoing motor command toward the target location. In other words, the nervous system will assume the hand lies somewhere between its true position and the target. In the same manner, the integrated cursor position is derived from the actual and expected cursor position (which would align with the target). Once these integrated hand and cursor positions are determined, they are crossmodally recalibrated to produce perceived hand and cursor positions (figure 11c). The perceived hand position arises from the integrated hand position being crossmodally recalibrated by vision, and the perceived cursor position results from the integrated cursor position being crossmodally recalibrated by proprioception. This results in the nervous system having a perceived hand position which lies between the target and cursor. In other words, the participant feels their hand to be pulled toward the cursor. Adaptation is thus driven by the participantâs attempt to re-align their felt hand position with the target (figure 11d). As adaptation accumulates and the actual hand drifts further away from the target, the perceived hand position eventually aligns itself to the target. At this point, adaptation saturates and reaches a plateau. 31 Although PReMo utilizes the term âperceived hand positionâ, it may be more accurate to use âperceived movement outcome.â Although the difference between terminology is subtle, the latter emphasizes the multimodal nature of the phenomenon, since various sensory modalities (vision, proprioception, prior expectations of movement outcome) are contributing to the percept. Therefore, it also may be more suitable to state that adaptation is driven by a perceptual error, rather than a proprioceptive error. As much as proprioception is a contributing sensory information source to the error, the error itself is still largely perceptual, in that participants feel their hand to be perturbed from the target. The terms âproprioceptive errorâ and âperceived hand positionâ are used interchangeably with âperceptual errorâ and âperceived movement outcomeâ respectively throughout this document, indicating the understanding of their perceptual nature with the recognition of the nuances of these terminologies. 32 Figure 11. Proprioceptive Re-alignment Model of implicit motor adaptation. (A) The actual hand and cursor positions during the clamped visual feedback task. (B) the actual and expected hand and cursor feedback combine intramodally to create the integrated hand and cursor positions respectively. (C) the integrated hand and cursor positions combine crossmodally to determine the perceived hand and cursor positions. (D) the proporioceptive error created between the perceived hand position and the goal (the target) drives adaptation. Adapted from Figure 1 in Tsay et al., 2022. The perceived hand position can be determined experimentally throughout the course of adaptation by probing the participant during an experimental protocol. In order to do this, the a.Hand actualTargetGt = 0Cursor actualHome positionActual hand/cursorHand actualCursor actualCursor integratedb.TargetGt = 0Home positionIntegrated hand/cursorHand integratedHand perceivedCursor perceivedCursor integratedc.TargetGt = 0Home positionPerceived hand/cursorHand integratedHand perceivedd.TargetGt = 0Home positionImplicit AdaptationProp Error 33 experimenter asks the participant to report where they think their hand is after performing a reaching movement to the target. This is most commonly done by presenting the participant with a number array appearing around the target after they perform their reach (Tsay et al., 2020). They are then asked to report which number they believe to be closest to where their hand passed the target perimeter. During baseline, when the cursor feedback is aligned with the actual reach direction, the perceived hand position hovers closely around the actual hand position with a small amount of variability. As the task-irrelevant cursor feedback is first introduced, the integrated estimate of hand position is cross-modally recalibrated by vision of the cursor, and the perceived hand position is pulled toward the visual feedback (figure 12a). Thus, the participant reports that their hand is somewhere between their actual hand position and the cursor. The difference between the perceived hand position and the movement goal with clamped feedback is hypothesized as the driving factor for adaptation. The participantâs reaching direction begins to drift in the opposite direction of the cursor to re-align the perceived hand position with the target. As adaptation persists and the actual hand drifts further away from the target, the perceived hand shifts to re-align with the target (figure 12b). Thus, although the actual hand position may be up to 30° away from the target, the participant believes their hand is near or at the target. This characteristic initial shift and eventual re-alignment of the perceived hand position during the clamped feedback task has been demonstrated empirically in various studies (Tsay et al., 2020; Tsay, Kim, et al., 2021). 34 Figure 12. Early vs. late adaptation explained through PReMo. (A) when clamped visual feedback is first introduced, the perceived hand position is based on the actual hand position as well as visual feedback from the cursor. (B) implicit adaptation saturates when the percieved proprioceptive error is removed (the perceived hand position is re-aligned with the target). Adapted from Figure 1 in Tsay et al., 2022. While PReMo is the first computational model to propose proprioception as playing a significant role in driving adaptation, previous research has suggested proprioceptive recalibration as a mechanism to account for implicit aftereffects in the classic rotation paradigm (Henriques & Cressman, 2012). Cressman and Henriques (2009) demonstrated that performing a visuomotor rotation task resulted in partial proprioceptive recalibration (i.e., a shift in perceived hand position). They tested this recalibration by assessing where participants felt their hand to be in relation to a reference marker before and after reaching to targets with rotated cursor feedback in the clockwise (CW) direction. They found that participantsâ judged the visual reference markers to be counterclockwise (CCW) with respect to their perceived hand position after performing reaches with a CW rotated cursor compared to following reaches with a veridical cursor. When participants were exposed to a CW rotation, their sense of hand position also shifted in the CW direction due to the proprioceptive recalibration resulting from the visual error. As a consequence, participantâs hands had to be rotated CCW more extensively before they felt Hand perceivedCursora.TargetHome positionEarly AdaptationHand actualHand perceivedCursorb.TargetHome positionLate AdaptationHand actualLearningImplicit adaptation 35 that their hand aligned with the reference marker's position. The shift in responses was approximately a quarter of the magnitude of the visual perturbation introduced, suggesting that subjects partially recalibrated proprioception with the perturbed visual information. This shift in perceived hand position has been described as a mechanism for aftereffects in the classic visuomotor rotation paradigm (Henriques & Cressman, 2012), which aligns with the predictions of PreMo (Tsay et al., 2020). Beyond explaining the mechanism for implicit adaptation, PReMo makes various other proprioception-related predictions regarding adaptation behaviour. For example, the model predicts that an increase in proprioceptive variability would lead to an increase in implicit adaptation. This is clearly shown through the following equation: ð¥$% = â ð½& *1 + ð&'ð(' - Equation 2. The upper bound of adaptation (ð¥!"), which is determined by the size of the proprioceptive shift ( ð½&), is influenced by both proprioceptive (ð&') and motor (ð(') variability (Tsay et al., 2022). Recall that the upperbound of adaptation (ð¥$%) is reached when the proprioceptive error is eliminated. In other words, adaptation reaches its saturation point when the perceived hand position re-aligns with the target. This equation highlights that the upperbound of adaptation is directly related to the size of the proprioceptive shift, which is weighted by proprioceptive uncertainty (ð&') and the noise associated with the sensory expectations of the motor command (ð('). Increased proprioceptive uncertainty will therefore lead to an increase in the upperbound of adaptation. This prediction is supported by empirical evidence which demonstrated a correlation between participantsâ variability in a hand localization task (indicating proprioceptive variability) with their upper bound of adaptation (Tsay, Kim, et al., 2021). Interestingly, although an increase in proprioceptive shift will result in increased adaptation, the shift has not been shown to 36 correlate with proprioceptive variability. This suggests that the proprioceptive shift and proprioceptive variability may have independent contributions to the upper bound of adaptation (Tsay, Kim, et al., 2021). Given the importance that PReMo places on the role of proprioception, an interesting test case could be a consideration of a condition in which proprioceptive information was severely impaired. A recent study tested six individuals who had either congenital or acquired deafferentation and compared their performance in the clamped feedback task to that of age-matched controls (Tsay et al., 2023). The hypothesis based on PReMo was that deafferented patients would adapt more than control participants, because their perceived hand position would be more heavily biased by the visual cursor given that they have virtually no proprioceptive signals contributing to actual hand position. Additionally, it may be argued that deafferented individuals have higher proprioceptive variability, which would also support the hypothesis of heightened implicit adaptation. Contrary to this prediction, the deafferented patients adapted to the same extent as the controls. Furthermore, they also displayed a bias in perceived movement outcome toward the visual perturbation. Considering that PReMo attributes adaptation largely to proprioceptive feedback information, the similar behaviour between deafferented individuals and control participants could not be properly explained by the model. These unexpected results prompted further consideration of the role of efferent-based information about hand position in implicit adaptation. 1.6 Afferent vs Efferent Contributions to Adaptation PReMo places a large focus on the sensory information available, such as vision of the cursor and proprioception from the actual hand position. Although the model considered some non-sensory signals, it only considered the motor command in relation to the movement goal (the 37 target). It did not consider the sensory prediction based on the motor command sent to the limb musculature. This efferent information, generally referred to as the efference copy or corollary discharge, may serve as another contributing factor towards perceived hand position. As previously discussed, this efferent information can be used to generate a sensory prediction of the feedback resulting from the given motor command. This sensory prediction had not previously been considered as an information source contributing to perceived hand position. If it were, the perceived hand position would then be composed of both afferent and efferent sources which could be weighted differently depending on the context. While control subjects may have the option to flexibly weight afferent and efferent signals based on the task context, deafferented individuals may need to rely more heavily on the sensorimotor prediction based on the efference copy to compensate for their lack of afferent proprioceptive information (Tsay et al., 2023). Although the consideration of this additional information source would account for new evidence provided from this study, it has yet to be empirically tested. Although PReMo had not previously considered efferent-related contributions to adaptation, many other studies on sensorimotor adaptation have. One common approach which has been used to parse apart the afferent and efferent contributions is to perform a visuomotor rotation task with passive movements. This involves the use of a robot which passively moves the participantâs arm out to a given location. The idea with this experimental manipulation is that while the afferent information of a reaching movement is left intact, there is no efferent contributions since no motor command was ever sent to the limb. Mostafa et al (2019) used this type of manipulation to compare the effects of passive versus active training on both hand localization and reaching aftereffects. The active training group performed a typical visuomotor rotation task where participants reached to targets while observing rotated cursor feedback. The 38 passive training group was exposed to a visuomotor rotation display while a robot passively moved their arm through a typical adaptation curve, where their hand slowly drifted away from the target. Both groups performed no-cursor reaches after their respective training types to assess if any reach adaptation (aftereffects) had occurred. In the hand localization task, participants either passively or actively moved their hand from home position to a certain location on a displayed arc, then used their other hand to indicate where they landed. Both training groups performed active and passive localizations to compare the effect of training type on both types of hand localization. Whereas there was no difference in passive versus active hand localization for the passive training group, the active training group showed a larger shift for active localization compared to passive. This indicates that the updated prediction of sensory consequences (i.e., the efferent contribution) achieved during active training contributed to hand localization. Interestingly, they also found that active reaching aftereffects between both training groups were comparable. This suggested that proprioception alone still plays a large role in movement planning and limb position estimation. Although using passive movements may begin to reveal how the nervous system uses afferent and efferent information differently when undergoing motor adaptation, it has some caveats. The biggest problem with passively performed movements is the quality of afferent information resulting from the perturbation. It has been established that muscle spindle afferents may fire differently during an active compared to a passive movement due to muscle thixotropy (Proske & Gandevia, 2012). Active movements may result in the removal of thixotropic effects that are not accounted for in a passive movement. Thus, even if the exact same movement is performed, the afferent information coming from the muscle spindles may be different 39 depending on if the movement was actively or passively performed. It is therefore difficult to isolate the same afferent feedback which would be present in an active movement without actually sending a motor command. The alternative approach to this question would be to isolate the efferent information in the absence of afferent feedback during a reaching task. Theoretically, this was the case in the clamp adaptation study with deafferented individuals (Tsay et al., 2023). Since the participants had little to no incoming proprioceptive information, it was postulated that their sensory feedback was almost entirely composed of the prediction based on the efference copy. Although this experiment started to address this question, the results may not be generalizable to a typical nervous system. A person who experiences deafferentation cannot necessarily be equated to a characteristic nervous system simply void of proprioception. These deafferented individuals have been living with their respective conditions for years, and thus have likely developed many neural adaptations and strategies to deal with the problems they face on a daily basis. Additionally, it is difficult to conclude what the sensory prediction of a deafferented individual would be composed of. Although they do not have proprioception, do they still have a proprioceptive prediction? Or is it a purely visual prediction? We are unable to determine the differences, if any, between the sensory predictions of a deafferented individual compared to control participants. Thus, the results of this study must be considered carefully in terms of their generalizability to individuals with intact proprioceptive sensory systems. Considering the limitations and constraints of the study by Tsay et al (2023), it may be advantageous to create an experimental paradigm which could effectively minimize proprioceptive contributions in a healthy control participant. An isometric force aiming task where the participant would send a motor command to the limb musculature without actually 40 moving the limb would be one potential method to isolate the efferent sensory contributions. This type of experimental manipulation has been used in a classic visuomotor rotation paradigm (Baek & Okamura, 2015; Berger et al., 2013; De Rugy et al., 2009; Gentner et al., 2013; Rotella et al., 2013, 2015). Various handles requiring the use of different upper limb muscles have been used in the different studies. Regardless of the specific experimental set up, participants clearly have the ability to make accurate force exertions towards different targets and are able to successfully adapt to an imposed visuomotor rotation. Furthermore, participants also show aftereffects of the perturbation, suggesting that efferent contributions alone are enough to elicit adaptation, even in the absence of dynamic reach-related feedback. Although these studies were successful in demonstrating adaptation, they were addressing slightly different questions. Many of these studies were focusing on determining different muscle synergy patterns and how they affected adaptation, rather than the underlying neural mechanisms for the observed adaptation. Additionally, since the classic visuomotor rotation paradigm was used, we are unable to distinguish between the explicit and implicit components of learning. To the best of our knowledge, the clamped visual feedback task has not been examined through the use of an isometric aiming task. 1.7 Summary Sensorimotor adaptation research largely aims to examine the mechanisms through which humans learn to adapt their movement to suit the given environment. Various experimental paradigms have been used to evoke different error signals and evaluate their contributions toward adaptation. While the visuomotor rotation task has been extensively used in the literature, the clamped visual feedback task has been used more recently to examine the purely implicit components of adaptation. Currently, most computational models used to explain adaptation 41 behaviour are visually driven, meaning they hypothesize visual error signals as the driver for adaptation. Although these models can accurately represent behavioural responses during adaptation tasks, they do not consider the importance of proprioception. Additionally, they do not account for various phenomena during adaptation, such as the shift in perceived hand position observed in both classic visuomotor rotation and the clamped visual feedback task (Cressman & Henriques, 2009; Henriques & Cressman, 2012; Tsay et al., 2021). Thus, newer models emphasizing the role of proprioception have been proposed to better explain adaptation performance. The Proprioceptive Re-alignment Model hypothesizes that adaptation is an attempt to re-align the felt hand position with the target. The perceived hand position was originally proposed to be mainly composed of visual and proprioceptive afferent signals but has recently been thought to also contain efferent signals such as the sensory prediction derived from the motor command. Although this idea is supported by previous literature stating that the efference copy is likely used in a forward model to contribute towards adaptation, it has not been empirically tested in a clamp paradigm. By isolating this signal in the absence of afferent feedback, the role of the forward prediction based on the efference copy on adaptation can be further examined. 1.8 Purpose The purpose of this study was to examine the effect of dissociating the afferent and efferent information available during implicit sensorimotor adaptation. I investigated whether the misaligned visual feedback during the visual-motor rotation task could alter our perceived hand position by biasing our efferent, motor-related prediction of where our hand should be. The clamped visual feedback task was used to address this question. Although the hypothesis that the sensorimotor prediction based on the efference copy may be contributing to perceived hand 42 position is appealing, it has not been directly examined. In order to assess its contribution to perceived hand position, it may be useful to dissociate this information from the proprioceptive signals present during the clamped feedback task. Classically, when a participant reaches out to the target during this experimental protocol, both their afferent and efferent kinesthetic information will be aligned. Predicted sensory feedback of the hand reaching out to the target will be generated based on the efference copy, while the actual proprioceptive sensory information will confirm that prediction. Since these two sources of information are closely tied (and typically in agreement) when making reaching movements, it is difficult to quantify the extent of contribution each signal is making towards the perceived hand position. Thus, in the present study, these two information sources were separated to better understand how each is contributing to overall implicit adaptation. Instead of performing actual reaching movements towards targets, participants used an isometric handle to complete the task. They were instructed to âreachâ towards an array of different targets simply by applying sheer force to a static handle in the goal direction. During baseline trials a visual cursor moved in the direction of the imposed force. During exposure trials, the cursor followed an invariant path rotated in relation to the target such that the cursor direction always remained the same no matter what direction the force was applied to the isometric handle. Participants were instructed to ignore this task-irrelevant cursor feedback and continued to âreachâ towards the target. By completing the task without actually moving the hand toward the target, the proprioceptive information is less relevant to the task. In other words, while proprioception was not actually being removed, it was no longer coupled to the actual reach trajectory, and was presumably not contributing to perceived hand position as described in PReMo. The motor-related sensory prediction on the other hand remained related to the task, since participants attempted to generate a âmovementâ towards the 43 target. By removing the relevance of afferent proprioceptive information while maintaining a motor command intended towards the target, our objective was to better understand how each of these signals contribute to adaptation. 1.9 Hypothesis The PReMo model proposed by Tsay et al (2022) did not fully consider the efferent contributions towards perceived hand position. In this regard, would PReMo predict implicit adaptation during an isometric force task? When the hand remains at the home location, afferent proprioceptive feedback of the static hand no longer contributes to the perceived hand position at the target location. If afferent information regarding hand position is still relevant even when the hand remains stationary, one might question whether a proprioceptive shift could still be induced, thus creating a proprioceptive error as anticipated by PReMo. PReMo predicts that there is a shift of the hand position at home after training with perturbed visual feedback, and that this shift is responsible for generalization of adaptation to new targets in the workspace. The vector planned to untrained targets would be based off of the shifted perceived hand position rather than the actual hand location, resulting in generalization. Despite the logic behind this hypothesis, it is still a postulation, as it has not been empirically tested. Additionally, given that PReMo assumes the afferent feedback contributing to hand position is with respect to actual reaching movements, it is difficult to articulate what the prediction might be in the isometric task. Intuitively, it might be assumed that since there is no afferent feedback contributing to perceived hand position at the end of the reach, there is no longer anything to shift, and therefore there is no proprioceptive error to drive adaptation. In other words, isometric force movements may remain directed toward the target, even when clamped visual cursor feedback is present. On the other hand, if a shift is still induced at the home position, we may expect some adaptation of 44 force movements as the participant would be planning force vectors based on their perceived hand position rather than their actual hand position at the home location. If we now consider the potential efferent contributions to hand position, we can consider an alternative hypothesis. Although there is no specific afferent information with respect to reach position near the target contributing to perceived hand position in an isometric task, there may still be efferent contributions, such as the prediction of the sensory consequences for a given movement. If this were the case, the efferent-based percept of the generated movement may be influenced by the misaligned visual information, inducing a shift which could eventually drive adaptation. Based on previous literature suggesting the important role of efferent contributions toward adaptation, I hypothesized that participants would exhibit implicit adaptation during the isometric aiming task by slowly drifting the direction of their force exertion away from the target when exposed to clamped visual feedback. Further, I hypothesized that adaptation would reach similar asymptotic levels compared to that found in previous clamped visual feedback reaching studies (H. E. Kim et al., 2018; Morehead et al., 2017; Tsay et al., 2023). The data from Tsay et al (2023) suggest that the efferent-based sensory prediction was recalibrated by the misaligned visual information, leading to subsequent adaptation in individuals with deafferentation. Thus, it is expected that healthy participants would be able to reweight the sensory information contributing to perceived hand position to place greater importance on the motor-related sensory prediction. The perceived hand position (mostly composed of signals derived from the efferent- 45 related predictions of the movement outcome) would then be crossmodally recalibrated by the visual cursor to create an error signal which would drive subsequent adaptation2. 2 It is worth noting that a visuocentric perspective would hypothesize similar adaptation in an isometric task compared to a reaching task. According to this view, the visual sensory prediction error between the actual and expected location of the target would drive adaptation of the force movement angle away from the target in the opposite direction of the cursor. The plateau of adaptation would be reached at the equilibrium point between the rate of learning and rate of forgetting. While this study was not specifically designed to examine adaptation from a visuocentric perspective, this hypothesis will be explored in greater detail in the discussion. 46 Chapter 2. Methods 2.1 Participants Thirty-three students (9 male, 25 female; ages 18-27) from the University of British Columbia were recruited to participate in this study. Twenty-one individuals participated in the main experiment while the remaining twelve completed a follow-up study. Participants were free of neurological or musculoskeletal injury preventing them from performing movements with their dominant limb, were naïve to sensorimotor research, and had normal or corrected-to-normal vision. All participants were asked to provide informed consent prior to participation and were compensated financially for their time. All study protocols were approved by the UBC Behavioural Research Ethics Board. 2.2 Experimental Setup Participants performed the isometric reaching task while seated in a comfortable position. Targets were displayed on a 27-inch LCD monitor (600 x 339 mm display, 240 Hz refresh rate, 1920 x 1080-pixel resolution) and reflected onto a one-way mirror located under the screen to provide an illusion of the targets being located on the table surface (figure 13). The position of the mirror and dark environment of the experimental room occluded peripheral vision of the arm. The isometric handle used to carry out the experimental task was placed approximately 36cm from the edge of the table surface below the mirror. The handle used was custom built using a small sized 3-axis force sensor (USL06-H5-50N-E; Tec Gihan). The sensor was 20x20mm and had a range of 25N in the Fx and Fy direction (although the sensor also detects force in the Fz direction (vertical), we were less concerned with this direction as the participant exerted forces in the horizontal plane). A custom 3D-printed handle (3.2 cm diameter) was 47 mounted onto the sensor such that sheer force applied to the handle was encoded by the sensor to determine force direction. Figure 13. Experimental apparatus and visual display. Left: the participant was seated comfortably in front of the mirror display set up. They grasped the isometric handle in a pinch grip between their thumb and index finger in a comfortable orientation. Right: one of four possible targets appeared on each trial 8cm away from the home location. The forces exerted onto the handle were ultimately transformed into the display monitorâs pixel space and displayed on the screen as cursor motion. When a force was applied to the handle, analog signals from the force sensor were passed through the instrumentation amplifier (EI-1040; Electronic Innovations Corp.) at 1000x gain. Next, analog output signals from the instrumentation amplifier were passed through a custom-built analog amplifier at 10x gain and bandpass filtered DC to 10Hz. This amplifier also allowed the experimenter to manually adjust the voltage offset to deal with drift in the sensor. This amplified and filtered voltage signal was then recorded through a 16-bit ODAU unit of an Optotrak (Northern Digital) 3D motion capture system sampling at 500 Hz. This signal was then multiplied by 100 and the calibration matrix 8 cm30°60°120°150° 48 (see appendix: calibration matrix) to return the force values for Fx, Fy, and Fz in Newtons. This was done through MATLAB (MathWorks) custom software. The force values were then converted into pixel space, such that 1N translated to 12mm (approximately 36 pixels; 1mm ~ 3 pixels) of cursor movement on the screen. The ODAU was polled continuously (effective sampling rate ~250Hz) to provide the visual cursor feedback. 2.3 Experimental Task Participants were asked to apply force to the handle to move a cursor towards targets (green circles; 6 mm diameter) presented on the visual display. The cursor (white circle; 3.5 mm diameter) always began in the home position, indicated by an empty circle (10 mm diameter). The home circle appeared yellow while the cursor was inside, and red once it left. After a given 500ms foreperiod, a target was presented in one of four locations in front of the home position (30°, 60°, 120°, 150°) at a radial distance of 8cm (figure 13). Participants were instructed to prioritize both speed and accuracy when completing the task. Movement time, defined from the time the cursor left the home circle until it crossed the target perimeter, was required to be between 100-400ms. This short movement time limited the participants from making online corrections during the movement. Movement times outside of this window triggered an auditory tone signalling an error, and the experimenter provided verbal feedback on whether the movement was too slow or too fast. After the movement was complete, participants relaxed, and the cursor reappeared in the home circle to begin the next trial. 2.4 Feedback Types All feedback types are described in figure 14. On veridical feedback trials, cursor feedback was given to the participant such that the cursor direction was directly coupled with the direction of force applied to the handle. On no feedback trials, participants did not see a cursor 49 while making force efforts towards targets. On clamped feedback trials, cursor feedback was rotated 15° clockwise or counterclockwise (counterbalanced between participants) relative to the target. Thus, no matter what direction the participant applied force to the handle, the cursor always followed the same path. It is worth noting that the amplitude and speed of the cursor movement was still tied to the participantâs force, such that a larger force resulted in a faster-moving cursor. Since the direction of the cursor was uncoupled to the participantâs force exertion, they were instructed to ignore this cursor feedback and continue generating forces that were directed towards the target. On trials where visual feedback was present, the cursor remained visible for 200 ms after the participant crossed the target perimeter. Figure 14. Feedback types. (A) Veridical feedback. The direction of the cursor was related to the direction of force applied to the joystick. (B) No feedback. No cursor feedback was displayed during the force movement. (C) Clamped cursor feedback. The cursor was displayed 15° relative to the target no matter what direction of force was generated. For the first 35 mm of the radial distance between home to the target, position-based mapping was used for this portion of the cursor display, where the forces recorded were directly mapped to the position and movement of the cursor (e.g., 1N ~ 12 mm). For the last two thirds of Veridical feedbackJoystickNo feedback Clamped feedbackTargetCursor 50 the radial distance, the cursor entered an âautomatic zone.â The last 5 samples collected from the non-automatic zone were averaged and used to calculate the angular direction of the resultant force, which determined the cursor trajectory for the remainder of the cursor movement out towards the target. This calculation was done online such that the cursor appeared to seamlessly move from one zone to the next. The automatic zone helped to ensure that the cursor movement was smooth out toward the target, and that online corrections were mitigated. In other words, whatever force direction was exerted during the first third of the movement determined the direction of the remainder of the movement, regardless of a change in force direction during the latter part of the trial. Therefore, even if online corrections did occur despite the fast movement time, they did not manifest through the cursor display. The speed of the cursor movement however was still tied to the force exertion such that a stronger push moved the cursor in a faster manner (see appendix: force mapping and cursor display). This cursor mapping also helped reduce the exertion effort needed from the participant. In attempt to make the pushing movement as similar to a regular reach as possible, the participant did not have to over-exert themselves when pushing on the handle, just as they would not have to use much force to move their hand to a target in a typical reaching paradigm. This cursor mapping addressed this issue by seamlessly advancing the cursor with a relatively small force (~4N) applied to the handle (figure 15). 51 Figure 15. Example display of cursor movement. The first third of the display was directly related to the resultant force exerted onto the handle. The last two thirds of the display (automatic zone) was based on the trajectory of the cursor from the last 5 samples taken during the non-automatic zone. 2.6 Experimental Blocks and Conditions The experiment consisted of 12 blocks (figure 16) . Each block contained a designated number of cycles. One cycle was defined as four âreachingâ movements: one to each target position presented in a randomized order. Participants had the opportunity to explore the nature of the experimental apparatus in a calibration block prior to testing. This block was 60 seconds long, allowing the participant to become familiar with the required force movement as well as the cursor sensitivity. A home position and an arc were drawn on the screen for the entire block. The arc was a white semicircle covering the upper-right and upper-left quadrants and had a radius of 10 cm from home. The participant was instructed to move the cursor in any direction they chose around the arc. They were encouraged to move in various directions so that they could explore the entire workspace. The calibration block was run more than once if the participant felt they needed more time to familiarize themselves with the apparatus. Once comfortable with the task, they participated in a practice block of 60 targeted reach trials with Non-automatic zoneAutomatic zone 52 veridical feedback. The first 40 practice trials were divided into 10 trials per target, where all 10 trials to each target were presented sequentially. This gave the participant an opportunity to repetitively aim to the same location and develop familiarity with exerting force towards the given target. The remaining 20 practice trials were pseudorandomized such that each target was presented once before they were repeated. Practice blocks were repeated if the standard deviation of their cursor angle over the whole block was greater than 20 degrees (indicating large variability in performance), or they did not feel comfortable with the task. Once they confirmed they could confidently aim to the different targets, the experimental trials commenced. Participants began the experimental trials with two baseline blocks. Baseline block 1 consisted of 80 no-feedback trials, and baseline block 2 consisted of 80 veridical feedback trials. These blocks served as an assessment of individual baseline bias and variability. Next, participants performed a practice block of clamped feedback to familiarize them with the nature of the cursor movement. On the first trial, participants were shown a target straight in front of them and were asked to execute a force exertion towards that target. After seeing the rotated cursor feedback, the experimenter explained that the cursor feedback would remain rotated relative to the target for the next series of trials. To demonstrate this to the participant, they were presented with the same target on the next trial but were instructed to exert a force in the rightward direction. Next, they were asked to exert a force in the leftward direction. On the final practice trial, they will be asked to exert a force in any direction of their choosing. After observing the same cursor feedback on all four practice trials no matter what force was exerted on the handle, the experimenter confirmed with the participant that they understood the nature of the cursor feedback before advancing to the exposure blocks. 53 The next five blocks (80 trials each) consisted of clamped cursor feedback. Participants were instructed to ignore the feedback and continue making force exertions towards the target. Participants were given the opportunity for a short break between blocks to prevent mental fatigue. The final block consisted of 40 no-feedback trials towards the targets to assess aftereffects. The participants were told to keep aiming towards the targets as they were in the exposure blocks, but there would no longer be any feedback displayed on the screen. Figure 16. Experimental blocks and conditions. Dark grey = veridical feedback trials, light grey = no feedback trials, white = clamped feedback trials. Numbers correspond to number of cycles in the block (1 cycle = 4 trials). 2.5 Data Collection and Measures The main dependent measure in this experiment was the mean force vector angle, which was used to index whether there was adaptation. Mean force vector angle was calculated as the average force vector angle across all four trials in one cycle. Force vector angle on each trial was computed as the angular difference between the target and the cursor endpoint (figure 17). In order to identify outliers, a 5-point moving average was calculated for each participant and target location. Trials were removed from analysis if they were greater than 2.5 standard deviations from the moving average, which resulted in 1.92% of trials being removed. An additional 2.83% Practice/Baseline Exposure Post-test20150 1661020Cycle # (4 trials)20 20 20 201 20 54 of trials were removed due to movement time violations or other trial errors. Average angular error during baseline trials for each individual participant was subtracted from all trials to account for inherent biases in aiming angle. Early adaptation rate was measured as the average hand angle from the first five cycles of clamped feedback trials. Late adaptation was measured as the average angular error over the last 10 cycles of clamped feedback. The aftereffect was measured as the average hand angle over the first cycle of no-feedback trials. In addition to mean force angle, baseline force angle variability was calculated as the standard deviation of participantsâ force angles during the no-feedback baseline trials. Figure 17. Measure of implicit adaptation. The primary measure was force angle, described as the angle between the direction of force and the target. Other performance measures were also taken to analyze performance. Reaction time was measured as the time between target presentation and when the cursor left the home region. Movement time was taken as the time between when the cursor left home and when it crossed the target perimeter. Although movement time was restricted to 100-400ms, reaction time was not restricted. Participants were simply instructed to push towards the target as soon as it appeared. Median movement and reaction times were taken for each participant for all no-TargetCursorJoystick Force directionForce angle 55 feedback baseline trials, all veridical feedback baseline trials, the first and last 10 cycles of clamped feedback trials (early and late adaptation), and all no-feedback post-test trials. To determine if adaptation had occurred, a one-way repeated measured analysis of variance (rm-ANOVA) was used to compare angular error between baseline, adaptation, and aftereffect phases. Significant effects were followed up with Holm corrected t-tests to determine how angular error differed in each phase. Separate rm-ANOVAs were used to determine if there were any differences in movement or reaction time across experimental phases (baseline no-feedback, baseline veridical feedback, early adaptation, late adaptation, aftereffect). Significant effects were followed up with Holm corrected t-tests to determine how movement and reaction time differed in each of these five phases. Statistical significance was set to ð < .05 for all measurements and a greenhouse-geisser correction was applied if the assumption of sphericity was violated. Uncorrected degrees of freedom are reported in the text. 56 Chapter 3: Results 3.1 Implicit Adaptation Participants applied force on an isometric handle to move a cursor towards different targets on a visual display. After baseline trials, participants completed 5 blocks of clamped-feedback trials, followed by a short no-feedback block to test for aftereffects. Force angle was used as the main dependent measure to indicate whether adaptation had occurred. Overall adaptation behavior is shown in figure 18. Figure 18. Mean implicit adaptation behaviour during an isometric task (n = 21). Data points represent angular endpoint error averaged across epicycles for all participants across all experimental phases. Vertical dotted lines indicate transitions between blocks. Shaded area = 95% confidence interval. Early adaptation occurred at a rate of ~3.64° [2.44, 4.85] per cycle. Since there appeared to be adaptation observed on the very first cycle of clamped feedback, we performed a secondary 57 analysis of early adaptation rate which included the last baseline cycle. When this cycle was included, the early adaptation rate was 4.3° [3.39, 5.29] per cycle. Adaptation in force direction occurred in the opposite direction of the clamped cursor, eventually plateauing at a mean of 40.76° [33.09, 48.42]. There was a significant effect of phase on angular error [F(2,40) = 116.225, p < .001, ð&' = .853]. Post-hoc comparisons revealed a statistically significant difference between force angle at baseline and late adaptation (p < .001), confirming that adaptation had occurred. There was also a statistically significant difference between baseline and the first cycle of the post-test (p < .001), demonstrating adaptation aftereffects. Furthermore, there was no significant difference between late adaptation and the aftereffect (p = 0.131), providing additional evidence of the implicit nature of the adaptation (figure 19). Figure 19. Mean angle of force direction during baseline, late adaptation, and aftereffect measures. Points represent average angular error for each individual participant during each experimental phase. Error bars represent multi-level bootstrapped within-subject 95% confidence intervals. 58 3.2 Movement and Reaction Times Median movement times averaged across participants over the course of the experiment was 167.32 ms [152.87, 181.78]. Although there was no restriction placed on reaction time through task instruction, participants initiated their movement within 369.99 ms [334.61, 405.37] on average. This relatively short reaction time suggests participants were not implementing any explicit strategies, but instead were simply reaching towards the target as per task instruction. A one-way rm-ANOVA comparing average reaction times across experimental phases (baseline no-feedback, baseline veridical feedback, early adaptation, late adaptation, aftereffect) revealed there was no effect of phase on reaction time [F(4,80) = 1.588, p = .215, ð&' = .074], revealing that reaction time did not change across experimental phases (figure 20). Interestingly, there was a significant effect of phase on movement time [F(4,80) = 14.161, p < .001, ð&' = .415] (figure 21). Post-hoc comparisons revealed that movement times during the veridical feedback baseline trials were slower than all other experimental blocks (p < .001), likely due to the effect of meaningful visual feedback on online movement control. There was no statistically significant difference between movement times among any other experimental blocks. 59 Figure 20. Reaction times over experimental phases. The central box represents the interquartile range (IQR) (and spans the 25th to 75th percentile of the data). The central line within the box represents the median, while the whiskers extend to 1.5 * IQR. Diamonds represent outlier values. Figure 21. Movement times over experimental phases. The central box represents the interquartile range (IQR) (and spans the 25th to 75th percentile of the data). The central line within the box represents the median, while the whiskers extend to 1.5 * IQR. Diamonds represent outlier values. 3.3 Angular Error Variability and Implicit Adaptation Previous studies examining motor adaptation have shown a correlation between baseline motor variability and learning rate, where an increase in variability of angular error was associated with an increased learning rate (Dhawale et al., 2017; Wu et al., 2014). Although the regressions 60 showed a trend in the opposite direction as to what was expected, we did not find a statistically significant correlation between baseline variability and either early (R = -0.34, p = 0.146) or late adaptation (R = -0.35, p = 0.121) in our data set (figure 22). Figure 22. Baseline angular variability did not correlate with early rate or asymptote of adaptation. Data points represent individual subjects. Left: relationship between baseline variability of angular error and early rate of adaptation. Right: relationship between baseline variability and asymptote of adaptation. 3.4 Force Movements As previously described in the methods, the cursor display followed an invariant trajectory for the final two-thirds of the movement. The average angle over the last five trials of the non-automatic zone was taken as the angle the cursor followed for the remainder of the trajectory. The purpose of this manipulation was to reduce the force effort required from the participant and prevent the visual manifestation of potential online corrections or force fluctuations associated with the latter portions of the trajectory when participants began to ease their exertion on the handle. To confirm that the force angle taken at the start of the automatic zone accurately reflected the intended endpoint force direction, we correlated the angular R = -0.35, p = 0.121 R = -0.33, p = 0.146 61 endpoint error (angular error shown during the automatic zone) with the force-mapped angular error during peak resultant force. Pearson r statistics and p-values for each participant are reported in Appendix B. There was a significant positive correlation between angular endpoint error and force-mapped angular error at peak force for all participants, indicating that the angular error shown during the automatic zone strongly corresponded with the angular error derived at peak force (figure 23). Figure 23. Force Movements. Scatterplot between angular error derived from the terminal portion of the first 1/3rd of the force trajectory (and displayed as feedback) and the angular error derived at peak resultant force. Data points represent individual participantsâ mean angular error measures for each epicycle. Dotted line = unity line. To further demonstrate that the force angle calculated at the start of the automatic zone was an appropriate measure of intended force direction, we repeated our analysis with the angular error derived at peak resultant force. Angular error at the time of peak force may be 62 another method of capturing the intended force direction while minimizing the effects of potential online corrections. Additionally, reaching studies frequently examine the angular error at peak velocity, which is analogous to peak force in our isometric set up (H. E. Kim et al., 2019). Upon visual inspection, it is clear that the patterns in our results remain unchanged when angular error was calculated at peak force (figure 24). A rapid adaptation of force direction is observed when the clamped feedback is introduced, and an eventual plateau of adaptation was reached. The early rate of adaptation over the first five cycles of the exposure phase was found to be ~3.4° [2.37, 4.43] per cycle, and the plateau of adaptation occurred at 35.4° [28.41, 42.39]. Angular error was found to be 29.9° [24.76, 35.03] on the first trial of the post-test. Analyzing angular error at the time of peak force confirmed the effect of phase ([F(2,40) = 99.727, p < .001, ð&' = .833], and revealed that there was a difference between angular error at baseline compared to late adaptation (p < .001), and baseline compared to the post-test (p < .001). There was marginal significance between late adaptation and the post-test (p = .049). These findings were similar to our original analysis, demonstrating that the primary results did not change depending on the measure of angular error used. 63 Figure 24. Mean implicit adaptation behaviour across participants. Data points represent angular endpoint error averaged across epicycles for all participants in the given experimental group. Pink = angular error at the automatic zone; Purple = angular error calculated at peak force. Vertical dotted lines indicate transitions between blocks. Shaded area = 95% confidence interval. 3.5 Reaching Movements Our results demonstrated enhanced adaptation in an isometric task compared to previous findings in a clamped feedback reaching paradigm. Although these studies usually report an asymptote of adaptation around 20°, they typically use a full array of targets spread throughout the entire 360° workspace around the home position (H. E. Kim et al., 2018; Morehead et al., 2017; Tsay et al., 2020). In order to eliminate any potential confounds when comparing our data to typical reach adaptation, such as the number or location of targets, we tested an additional twelve participants in a follow-up experiment. All experimental parameters were the same as the main experiment, except participants performed actual reaching movements instead of isometric force exertions. This allowed us to directly compare the adaptation between an isometric 64 paradigm and a reaching paradigm. Instead of using the isometric handle, participants made actual slicing movements through targets on the visual display. Participants wore a splint on their right index finger which was mounted with an infrared marker. An Optotrak (Northern Digital) 3D motion capture system was used to track reaching movements at a sampling rate of 500Hz. Participants were instructed to move their hand to the home position to begin each trial. Once they placed their finger/cursor within 30 mm of the home position, an elliptical ring appeared to help guide participants toward the home position without indicating the actual finger position. Once their finger was within 20 mm of home, the cursor appeared. Once the finger was at the home position for 500 ms, the target (6 mm diameter) appeared. All other experimental parameters (target location, experimental blocks, trial numbers) were identical to the main experiment. Similar to previous research, adaptation was observed when participants reached towards a target with clamped rotated visual feedback (figure 25). Early adaptation occurred at a rate of 1.7° [0.45, 2.94] per cycle, and plateaued at around 11.9° [8.66, 15.32]. Although this asymptote of adaptation occurred at a smaller angle than previous studies, it confirmed that the enhanced adaptation observed in the isometric task was not simply due to the target set-up and protocol. Angular endpoint error remained similar on the first cycle of the post-test at approximately 11.3° [8.35, 14.17], indicating the presence of an aftereffect. A one-way rm-ANOVA revealed a significant effect of phase (baseline, adaptation, aftereffect) on angular error [F(2,22) = 23.292, p < .001, ð&' = .679]. Post-hoc comparisons confirmed a significant difference between angular error in the baseline phase compared to the end of adaptation (p < .001) as well as the first trial of the aftereffect phase (p < .001). There was 65 no significant difference between angular error during adaptation compared to aftereffect (p = .701), suggesting that the adaptation observed was implicit. Figure 25. Implicit adaptation differs between isometric and reaching tasks. Average endpoint error over epicycles for all participants in the isometric (pink) and reaching (blue) group. Shaded areas represent 95% confidence intervals. Figure 25 shows a comparison of mean angular endpoint error for both the isometric and reaching groups. Both groups adapted to the clamped visual feedback as their endpoint errors drifted from zero in the opposite direction of the visual feedback. The isometric group clearly shows a much larger adaptation than the reaching group. To compare adaptation between the isometric group and the reaching group, we performed a group (isometric or reaching) vs phase (baseline, adaptation, aftereffect) mixed-design ANOVA with a greenhouse-geisser correction applied if the assumption of sphericity was violated. There was a significant Group * Condition interaction [F(2,62) = 22.709, p < .001, ð&' = .493]. There were no significant differences between groups during baseline (p = 1.000), 66 indicating that both groups performed similarly during baseline. There was a statistically significant difference between groups during both the adaptation (p < .001) and aftereffect conditions (p < .001), demonstrating that the isometric group had greater adaptation and aftereffects compared to the reaching group (figure 26). Figure 26. Angular endpoint error during baseline, adaptation, and aftereffect measures. Pink = isometric group, blue = reaching group. Points represent each individual participant. Error bars represent multi-level within-subject bootstrapped 95% confidence intervals. 67 Chapter 4: Discussion The purpose of this project was to examine the effect of dissociating the afferent and efferent contributions towards implicit sensorimotor adaptation. Previous research has demonstrated that performing a reaching task with perturbed visual feedback results in implicit adaptation. While it has been hypothesized that the visual error is responsible for driving adaptation, this view ignores the potential contribution of proprioception. The Proprioceptive Re-alignment Model was proposed as an alternative explanation for adaptation behaviour which places emphasis on the role of proprioception. It hypothesizes that the perturbed visual feedback causes a shift in the perceived hand position, which drives adaptation. While the perceived hand position was originally thought to be composed of mostly visual and proprioceptive signals, the role of the efferent prediction has been recently postulated as another contributing information source. Current studies using reaching paradigms elicit both proprioceptive afferent feedback and motor-related efferent predictions which could both contribute to adaptation, making it difficult to parse their respective contributions. Thus, the current study utilized a unique isometric aiming task where participants exerted force onto an immovable handle to advance a cursor towards targets on a visual display. By making force movements without actually moving the limb, we were able to directly examine the potential contribution of the movement-related efferent prediction without the confounding influence of proprioception tied to the action (cf. O. A. Kim et al., 2022; Mostafa et al., 2019). Although we did not directly remove proprioception, we were able to dissociate the proprioceptive feedback of hand location from the efferent prediction of a typical reaching movement. We found that participants implicitly adapted in a task where they were required to generate isometric forces to move an error-clamped rotated cursor on a visual display instead of 68 performing actual reaches to the target. Furthermore, we observed faster learning rates and greater overall adaptation than in a typical clamped reaching paradigm (Kim et al 2018). This finding was confirmed in our secondary experiment where participants performed actual reaching movements and demonstrated significantly less adaptation. Although the isometric task did not remove proprioception itself, it decoupled this feedback source from the task. Thus, these findings suggest that while afferent proprioceptive feedback of hand position at the target location most likely plays a role in adaptation behavior, it may not be necessary in and of itself to induce adaptation. 4.1 Models of Implicit Adaptation 4.1.1 Visuocentric view Considering that adaptation was induced during a task where proprioceptive feedback was not necessarily contributing to task-relevant dynamic position sense, it may seem appealing to assume a visuocentric model best explains these results. This view would hypothesize that there is a pertinent visual error between the clamped cursor and the target, which drives force direction away from the target, producing adaptation. Although the participant is not actually making a reaching movement, they have an expectation that the cursor will move in accordance to the direction of their exerted force. Thus, when the cursor feedback is clamped with respect to the target, there will be a visual error between the actual and expected cursor position which can drive adaptation. According to this view, adaptation reaches asymptotic levels once there is a balance between the adaptive response to the visual error and the decay in the retention of the response. At first glance, it might seem that our results also provide evidence for this view. There is a clear initial rate of adaptation, followed by an eventual plateau. Despite this, the visuocentric 69 perspective alone cannot explain why we observed a greater initial rate and asymptote of adaptation in our isometric task compared to the reaching task (figure 27). In both tasks, the visual feedback is identical, meaning that the error signal driving adaptation remains constant across the tasks. If vision was the only contributing source towards adaptation, we would have predicted similar levels of adaptation in both tasks. This however was not the case in our results, since we saw adaptation plateau around 40° in our isometric task compared to approximately 10° in the reaching task. Additionally, this view cannot account for the enhanced learning rate which we observed. Typically a state-space model is used to formalize adaptation behaviour from a visuocentric perspective. Although a state-space model could be fit to our data simply by adjusting the learning rate parameter (considering we also observed rapid adaptation decay), the visuocentric view does not provide a theoretical basis for this increased adaptation. In other words, while this model could formally represent our data, there is no theoretical explanation (from a visuocentric perspective) for why an isometric task would induce a faster learning rate compared to a reaching task when the visual stimuli was held constant. Thus, we must consider other possible explanations for our findings. 4.1.2 PReMo If the visual error alone cannot sufficiently explain the increased adaptation, we must consider what other sensory information sources may be contributing. Our original question for this study developed from the PReMo model, where we were interested in testing the notion that proprioception was a key component of implicit adaptation. More specifically, we intended to examine what information sources contribute to the perceived hand position. PReMo hypothesizes that adaptation is driven by a proprioceptive error created between the perceived hand position and the target. In the clamped visual feedback reaching task, the 70 misaligned cursor induces a shift of the perceived hand position towards the perturbed visual feedback. In other words, while the participant may have successfully reached the target, they perceive their hand to have missed in the direction of the clamped cursor. Adaptation in reaching direction away from the cursor is thus the result of realigning the perceived hand with the target. This study primarily addressed what sensory information sources are necessary to create a proprioceptive shift, and therefore induce subsequent adaptation. The behavioural findings from this study can be examined through the various predictions PReMo may make regarding adaptation in an isometric task. PReMo postulates that the perceived hand position is largely composed of three information sources: proprioception, vision (the clamped cursor), and the movement goal (the target). While vision and the movement goal contribute to final perceived hand position sense, proprioception is hypothesized as the main information source tied to the actual hand position. Upon first inspection, one might assume that PReMo would predict attenuated adaptation in the isometric task because there would no longer be any proprioceptive information contributing to perceived hand position, and therefore a shift should not be induced. This hypothesis was not supported based on our results, as adaptation prevailed in the absence of reaching. Alternatively, if we take PReMo in its current form, we can consider what the model would predict in an isometric situation by removing the contribution of afferent proprioceptive information. Since the model hypothesizes that the system first generates an intramodal estimate of hand position using the weighted average of the actual position of the hand and the expected position based on the motor command to achieve the proprioceptive goal, this integrated hand estimate would only be composed of the latter (figure 27a & b). When this integrated hand position is crossmodally recalibrated by vision (the cursor), it will be biased towards the clamped 71 feedback (figure 27c). The key distinction between formulating the perceived hand position in this manner compared to including afferent proprioceptive feedback is that neither of the remaining information sources change over the course of an experiment. The proprioceptive goal will always remain at the target, and the perturbed visual information remains unchanged. Therefore, the error introduced between the perceived hand position and the target will remain constant for the entire experiment, thus predicting an unbounded linear increase in adaptation (figure 28). This prediction is clearly not supported by our data, and thus other hypotheses must be considered. Figure 27. Perceived hand position and proprioceptive error during an isometric task. (A) The actual âhandâ (or âmovement outcomeâ in an isometric task) would be composed of the motor goal directed towards the target. (B) The integrated hand would be the same as the motor goal, since this is the only contributing information source when proprioception is not relevant to the task. The integrated cursor is derived from the actual cursor position and the visual target. (C) The perceived hand position arises from the crossmodal recalibration of the integrated hand position by the vision of the cursor. a. Actual hand/cursorTargetGt = 0Cursor actualHome positionGoal actualc. Perceived hand/cursorHand perceivedCursor perceivedCursor integratedTargetGt = 0Home positionHand integratedb. Integrated hand/cursorHome positionCursor integratedTargetGt = 0Hand integratedCursor actual 72 Figure 28. Predicted adaptation behaviour based on PReMo during reaching and isometric aiming tasks. (A) Implicit adaptation during a reaching task. When reaching, proprioceptive feedback contributes to the perceived hand position. During early adaptation, the perceived hand position is biased towards the visual cursor, creating a proprioceptive error. As adaptation progresses, and the actual hand drifts away from the target, the perceived hand position realigns with the target, nullifying the proprioceptive error. (B) Implicit adaptation during an isometric task. During early adaptation, the perceived hand position is biased towards the cursor. During late adaptation, although the movement direction drifts away from the target, the perceived hand position may remain unchanged, driving unbounded adaptation. a. Implicit Adaptation: Reaching TaskLateHand perceivedTargetGt = 0Home positionProp Error = 0Cursor perceivedHand actualEarlyHand perceivedTargetGt = 0Home positionProp Error > 0Hand actualCursor perceivedb. Implicit Adaptation: Isometric TaskEarlyHand perceivedTargetGt = 0Home positionProp Error > 0Movement actualCursor perceivedLateHand perceivedTargetGt = 0Home positionCursor perceivedProp Error > 0Movement actual 73 As stated in the introduction, PReMo may make a different prediction in the isometric task if we consider the motor-related efferent predictions as a contributing information source to the perceived movement outcome. While PReMo already considers vision, proprioception, and the prior (movement goal) to be contributing towards the perceived movement outcome, it may also be reasonable to consider the sensory predictions based on the outgoing motor command. We can first consider how the contribution of an additional information source relating to the actual hand position would affect the modelâs predictions in a typical reaching task. If this were the case, then the actual hand position may be composed of both afferent and efferent components, rather than just afferent sensory feedback (Simani et al., 2007). These two components may have different weights depending on the context. For example, if we have relevant proprioceptive feedback during the given task, such as in a typical reach, we may heavily rely on that information source to determine hand position. On the other hand, if we are in a situation where some form of sensory transformation between our movement and the outcome is required (e.g., moving a mouse on a computer screen), perhaps we would weight our efferent sensory prediction of the visual cursor higher than our actual proprioceptive feedback. The inclusion of the efferent contribution towards perceived hand position may not alter predicted adaptation behaviour in a typical reaching task, as both sources are combined to provide information about actual hand position. This would only hold true under the assumption that the efferent and afferent contributions were always present and contributing to the actual hand position, and they were just not explicitly defined as separate contributions. Otherwise, introducing another sensory information source towards hand position (the efferent prediction) would decrease uncertainty about actual hand position, which would likely result in decreased adaptation due to the decreased proprioceptive shift induced by the cursor (figure 29a). In our 74 discussion, it is assumed that this efferent contribution is not an additional sensory information source which increases certainty about the hand position, but rather an additional part of the âactual hand positionâ which had not yet been considered (figure 29b). Instead of the hand position being derived solely from afferent proprioceptive feedback, we propose it is composed of both afferent and efferent information sources which together compose the actual hand position. This assumption would allow for flexible re-weighting between the two sensory contributions to allow optimal performance in the given task and context. Figure 29. Sensory contributions to percieved hand position. A) If we consider the efferent prediction as an additional source towards actual hand position, then the percieved hand position would be a weighted average of four sensory cues. The additional cue relating to actual hand position (the efferent prediction) would increase the certainty of the hand position, resulting in decreased adaptation. B) If we consider the motor-related efferent prediction as a subcomponent of actual hand position, then the actual hand position would be a weighted average of both afferent and efferent components. The percieved hand position would then determined as PReMo originally postulated, where the actual hand position and motor goal are combined to form the integrated hand position, which is then biased by the visual cursor. The consideration of the efferent-prediction would make a key distinction for the predicted behaviour in an isometric task. Although the participant is no longer reaching in this paradigm, and therefore proprioceptive feedback is no longer tied to the task, they are still sending a motor command with related sensory predictions which should result from executing VisionPerceived Hand PositionMotor GoalProprioception Efferent Predictiona.VisionPerceived Hand PositionActual Hand PositionMotor GoalProprioception Efferent Predictionb. 75 the given action. In our isometric task, the motor command would generate a force vector in a given direction, which would be associated with an expected given cursor movement. In an isometric task, although proprioception may not directly contribute to position sense, the perceived movement outcome may then be based on vision, the movement goal, and the motor-related sensory prediction. This efferent-based percept could then be biased by the visual cursor, causing a perceptual error between the target and the end effector location which would drive adaptation (figure 30). This proposed model revision aligns with our current findings. We observed adaptation in the isometric paradigm, suggesting that a perceptual error may have been induced in the absence of proprioceptive feedback. If the hand position is composed of both afferent and efferent signals, we might expect heightened adaptation when afferent proprioception is removed â which was the case for our findings. Whereas in a typical reaching paradigm the motor system can rely on both afferent and efferent signals to determine hand position, there is only efferent information when producing isometric force movements. If the hand position (or in this context, the âeffectorâ position) is only composed of efferent signals, it would be less certain compared to when afferent proprioception is also contributing. If the hand position is less precise, then the perceived hand position would likely be biased by the visual signal of the cursor to a greater degree. This would effectively result in a greater perceptual shift, inducing a greater perceptual error, leading to an increased adaptive response to achieve realignment. Although this hypothesis would account for our findings, our current data set cannot confirm if a greater shift was induced since we did not directly measure the size of the perceptual shift. Previous studies have probed participants about their perceived hand position throughout the experiment to obtain a measure of the proprioceptive shift induced by the clamped cursor. Our protocol did not involve any measure of perceived movement outcome, 76 making it difficult to come to definitive conclusions about an increased shift in the isometric task. Future studies which include probing questions throughout the experimental protocol as to where participants believed they pushed after a force movement would help to determine if there was a shift of perceived position towards the visual cursor near the beginning of adaptation, and whether that shift was larger than in typical reaching studies. Figure 30. Implicit adaptation and perceptual re-alignment during an isometric task when considering the efferent contribution towards perceived movement direction. The perceived âhandâ location is composed of the motor goal, the motor-related efferent prediction, and the vision of the cursor. During early adaptation, the perceived hand is biased towards the cursor. As adaptation progresses, and the motor-related efferent prediction drifts away from the target, the perceived hand position re-aligns with the target. a. Actual hand/cursorGoal actualPerceived hand/cursorHand perceivedCursor perceivedCursor integratedTargetGt = 0Home positionHand integratedIntegrated hand/cursorHome positionCursor integratedTargetGt = 0Hand integratedCursor actualTargetGt = 0Cursor actualHome positionc.b.Hand efferentImplicit Adaptation: efferent contributionEarlyHand perceivedTargetGt = 0Home positionProp Error > 0Movement actualCursor perceivedLateHand perceivedTargetGt = 0Home positionCursor perceivedProp Error = 0Movement actuald. 77 We also considered the notion that a shift may be induced at the home location, as PReMo predicts this shift at home to be responsible for adaptation generalization that has been observed in previous studies. This shift at home would affect the direction of the force vectors required to reach the target. In other words, the participant would be exerting a force which would successfully hit the target if their hand was truly at the shifted location. Since it is not, this force vector would result in errors that would differ in size and direction depending on the generalized target location. For example, if there was a leftward shift in perceived hand position at the home location, this would result in clockwise errors to upwards targets (fig 31). Furthermore, this shift may result in slightly greater errors toward targets in the upper right quadrant compared to the upper left. The hypothesized resulting behaviour from this shift at home differs from the prediction of a shift induced at the target locations. If the perceived movement outcome is shifted at the target, then the shift would be expected to be in the same direction regardless of target location. In other words, if the clamped visual cursor caused a counterclockwise shift in perceived movement outcome, clockwise adaptation would be expected at all targets around the workspace. It is difficult to parse apart these two similar but unique hypotheses in our current data set, since we only utilized upward targets in our protocol. For example, if downwards targets were utilized, a leftward shift at the home location would result in counterclockwise errors, whereas the same shift would result in clockwise errors if it was induced at the target location. The current protocol does not allow for the comparison of these two contrasting predictions. It is also worth noting that PReMo makes a prediction about a shift near the home position when training is done with only one target, and the other targets act to test the generalization of adaptation. In our protocol the participants performed reaching movements to all four targets during the exposure phase, and therefore were able to adapt to each 78 target. Having the opportunity to train at each target as well as receiving veridical visual feedback at the home position may have attenuated any shift induced at home the position. Since our protocol used a limited target range and had participants execute movements to each one, it is difficult to make conclusions about a shift at the home position in our task. Future studies should consider this shift at home and its implications on overall observed adaptation. Figure 31. Schematic of adaptation generalization when a shift is induced at the home position. Grey dot = perceived hand position, black dot = actual home position, blue dots = targets. Left: task performance during baseline. The perceived hand position is aligned with the home position as no shift has been induced. Middle: the shift in perceived hand position once clamped cursor feedback is introduced. The perturbed feedback may create a shift in perceived hand position at home. This would alter the planned trajectory to hit each target. Right: task performance if there is a shift in perceived hand position at home. The trajectories towards each target would be planned from the perceived hand position, which has been shifted. This would result in a rightwards error towards all targets. Adapted from Tsay et al., 2022. 4.1.3 Bayesian Cue Combination: the PEA model The unique aspect of PReMo is that it places large importance on the idea of a proprioceptive error and its role related to adaptation. Whereas many models are governed by state-space equations that hypothesize that the asymptote of adaptation is reached when the rate of learning and forgetting are equal and opposite to each other, PReMo makes a distinct prediction that the adaptation plateau is reached once a proprioceptive error is nullified. Conceptually this is an appealing perspective, as our motor system is constantly tuning and adjusting to correct for errors in our movement. The idea of the perceived hand position is critical to the model, as the difference between the perceived hand position and the target is the driving error signal for adaptation. 79 Recently, another model which also incorporates the notion of a perceived hand position has been proposed to explain implicit adaptation. This model proposes that implicit adaptation is driven by a perceptual error determined from the Bayesian integration of movement-related cues (Zhang et al., 2024). The Perceptual Error Adaptation (PEA) model hypothesizes that when a participant reaches towards a target with error-clamp feedback, they form a perceptual estimate of their hand movement direction based on three sensory information sources: the visual information of the cursor, the proprioceptive information from the actual hand position, and the sensory prediction of the motor command towards the target. Although these are the same three sensory signals used by PReMo, their conceptual and computational representations are fundamentally different. Recall that PReMo hypothesizes that an integrated hand position is first derived from the actual hand position, as well as the motor goal (towards the target). This estimate is then recalibrated by vision, creating a perceived hand position which is shifted towards the visual feedback: ð¥&,*+ = Ï,'Ï,' + Ï&' ð¥&,* + Ï&'Ï,' + Ï&' ðº* Equation 3. The integrated hand position (ð¥#,%& ) is defined as a weighted average of the proprioceptive feedback from the actual hand position (ð¥#,%) and the proprioceptive goal centred at the target (ðº%) (Tsay et al, 2022). ð¥&,*&-. = ð¥&,*+ + ð½& Equation 4. ð¥#,%#'(= the perceived hand position, ð¥#,%& = the integrated hand position derived from proprioceptive feedback and the proprioceptive goal at the target, ð½# = the proprioceptive shift induced by vision of the cursor (Tsay et al., 2022). The PEA model hypothesizes a different mechanism for deriving the perceived hand position. According the this view, each of the three information sources is assumed to have normally 80 distributed noise, resulting in three independent gaussian distributions. Through a Bayesian cue combination framework, the perceived hand position is derived from the maximum likelihood estimation of the three noisy cues on each trial: ð¥8/0!1,! =9ð2ð¥2,!2 , ð¤ðð¡â ð2 = 1/Ï2'â 13 /Ï3' , ð, ð = ð¢, ð, ð£ Equation 5. The perceived hand location (ð¥8/0!1) on trial n is derived from the weighted average of three sensory cues: vision (v), proprioception (p), and the motor goal (u) (Zhang et al., 2024). On early adaptation trials, both the proprioceptive cue as well as the motor prediction will be centered around the target, while the visual cue will be offset according to the degree of clamp error. This perturbed visual feedback will bias the perceived hand position towards the direction of the cursor, creating a perceptual error between the estimated hand location and the target, which drives adaptation on subsequent trials. This trial-by-trial adaptation is modeled through the following state-space equation: ð¥&,!"# = ð´ð¥&,! + ðµFð â ð¥8/0!1,!H, Equation 6. State-space equation modeling trial-by-trial adaptation in the PEA model (Zhang et al., 2024). Where ð¥&,! is the actual hand position on trial n, A is the retention rate representing the inter-trial forgetting, B is the learning rate capturing the proportion of error corrected for on a given trial, T is the target position, and ð¥8/0!1,! is the perceived hand position on trial n. The asymptote of adaptation is therefore defined as the equilibrium point between learning and decay: 81 ð¥&0456 = â ðµ/Ï7'ðµ/Ï&' + (1 â ð´)â 13 /Ï3' θ, ð = ð£, ð, ð¢ Equation 7. The asymptote of adaptation (ð¥#)*+,) is influenced by the perturbation size, as well as visual uncertainty. (Zhang et al., 2024). Although the PEA model shares many similarities with PReMo, it has various key distinctions. One important difference is how they postulate the asymptote of adaptation is reached. PReMo hypothesizes that adaptation plateaus once the proprioceptive error (the difference between the perceived hand position and the target) is nullified. This upper bound is influenced by variables such as proprioceptive uncertainty, or the size of the proprioceptive shift. This contrasts with the PEA model, which describes adaptation behaviour through a state-space equation, therefore predicting the plateau of adaptation to occur when the learning rate is equal and opposite to the rate of forgetting. While the PEA model hypothesizes that adaptation is driven by a perceptual error, it does not formalize adaptation to reach asymoptic levels when this error is nullified. Interestingly enough, the model predicts that this error is never fully corrected for, as there is at least a small fraction of learning decay on each trial. Another key component of the PEA model is the prediction it makes regarding visual uncertainty. The PEA model incorporates the established phenomenon that visual uncertainty increases linearly with visual eccentricity, which is related to perturbation size (Klein & Levi, 1987; Levi et al., 1987). This is incorporated into the equation which quantifies the asymptote of adaptation (equation 7 above). Although adaptation extent will increase with perturbation size, this increase is counterbalanced by the coinciding increase in visual uncertainty. Thus, the PEA model predicts an increase in adaptation as perturbation size increases until approximately 15°, followed by a gradually decreasing adaptive response as the visual uncertainty continues to rise 82 (figure 32a). This is a direct contrast to PReMo, which predicts adaptation follows a ramp pattern as the perturbation size increases (figure 32b). This is because PReMo postulates that adaptation will depend on the size of the proprioceptive shift, which scales for small perturbations, but eventually saturates for visual errors approximately 5° and above. Figure 32. Predicted extent of adaptation behaviour related to the perturbation size. (A) The PEA model hypothesizes that adaptation extent will increase non-linearly as perturbation size increases, due to the negative effect of increasing visual uncertainty on adaptation. (B) PReMo hypothesizes that adaptation will increase linearly with perturbation size for small perturbations as the proprioceptive shift also increases. Once the shift saturates, the predicted plateau of adaptation remains constant, even as the perturbation size continues to increase. Adapted from Zhang et al., 2024. Given these key differences between the PEA model and PReMo, it may be of interest to postulate what the PEA model might predict in our isometric task. This model states that the perceived hand is composed of visual information, proprioceptive information, and the sensory prediction based on the motor goal. In our isometric task where the participant is not actually reaching to the target, there is no longer a contribution from afferent proprioception towards perceived hand position. In this sense, the perceived hand position would then only be composed of two cues: the visual cursor, as well as the sensory prediction centred around the target. Without the additional cue of proprioception, there would be a greater shift of the perceived hand position towards the target, since the hand position is an optimal integration of all available cues. Perturbation size (°)Asymptote of adaptation (°)0 20 40 60 80 1000102030Perturbation size (°)0 20 40 60 80 1000102030PEA model PReMo model 83 Although this logic up until this point is very similar to how PReMo might describe the isometric task, the prediction for adaptation is unique. Since the PEA model describes adaptation through a state-space equation that incorporates learning and forgetting factors, this model would predict heightened adaptation until a plateau is reached. According to the PEA model, this plateau occurs when the learning and forgetting rate are equal and opposite to each other, irrespective of whether the perceptual error is nullified. It is worth noting that the perceptual error would remain constant in an isometric task. When performing actual reaching movements, the proprioceptive feedback from the hand position can contribute to the perceived hand position. As the actual hand position begins to drift as a result of the implicit adaptation, the perceived hand position would also shift towards the target, reducing the perceptual error. This contrasts with the isometric task, where the perceived hand position is primarily composed of the visual cursor and predicted sensory consequences towards the target. Since these two information sources do not change throughout the course of the experiment, the perceptual error stays constant. Thus, although the perceptual error would remain constant throughout the experiment in an isometric task, an asymptote of adaptation would still be reached since the extent of adaptation is reached via learning and forgetting processes rather than the nullification of the perceptual error. This asymptote would be at a higher level compared to regular reaching, since the error driving adaptation would be larger. The removal of the proprioceptive contribution results in a perceived hand position which is based only on vision of the cursor and the motor goal towards the target. This would result in a greater perceptual shift towards the visual feedback, since the weighting of the visual feedback will increase as a consequence of having one less signal contributing to perceived hand position. This increased shift will directly result in an increased perceptual error, inducing greater adaptation. 84 Another way to conceptualize the isometric task through the lens of the PEA model is through increased proprioceptive uncertainty. Since the task does not actually remove proprioception itself, but rather decouples its movement-related contribution to the task, it is possible that proprioception still has some role towards the perceived hand position. However, since the hand is not actually moving, the proprioceptive uncertainty might be much higher. Additionally, if there is no actual reach performed, the expected movement outcome based on the motor command may also be less certain. This would increase the variability of the sensory cue derived from the motor goal around the target. Taken together, increased proprioceptive and motor uncertainty would result in a heavier weighting of the visual cue from the cursor. This would result in the same predicted behaviour as removing proprioception all together: a greater perceptual shift towards the visual feedback would lead to a greater perceptual error, inducing greater adaptation compared to reaching. Whether the increased adaptation is due to fewer sensory cues or more variability in those sensory cues, the predicted adaptation behaviour remains the same and aligns with our experimental observations. Although this model has the potential to describe our data, it is missing a similar potential information source as PReMo: the motor-related efferent prediction. Although it does incorporate a sensory prediction cue, this cue is always tied to the target. PReMo makes the same assumption when discussing the contribution of the motor command. It may seem logical to assume the sensory prediction always remains at the target, since the task instruction is to continue reaching towards the target even when the perturbed visual feedback is introduced. Despite this, there still may be an efferent component tied to the movement that is actually executed, meaning that the brain should still have a sensorimotor prediction associated with the actual motor command. A distinction must be made between the motor prediction based on the 85 conscious expectation that we are aiming towards the target, and the efferent prediction based on the actual motor command which was executed. In an adapted state, the latter would refer to the sensory prediction derived from the actual motor command directed away from the target in the opposite direction of the cursor. This may be a relevant information source towards the perceived hand position, thus playing an important role towards adaptation. As it currently stands, the cue pertaining to actual hand position in the PEA model (ð&) is composed of only one sensory information source: proprioception. We could alternatively consider ð& to contain both afferent proprioceptive feedback as well as the efferent contribution derived from the motor command. ð& would then be derived from the weighted average of these two cues. From there, the model would remain unchanged as there would still be three noisy cues to form the final perceived hand position. The sensory uncertainty for the efferent-based motor prediction would likely be greater than that of afferent proprioception, as the sensory prediction from the motor command may be more variable than proprioceptive feedback. It is important to note that similar to our hypothesis regarding adding an efferent contribution towards hand position in PReMo, this information source would not increase the certainty of the hand position. Rather than conceptualizing this as an additional information source towards hand position, we are proposing that the hand position itself is composed of two information sources: one being afferent-based, and the other being efferent-based. In other words, rather than simply adding a fourth information source which integrates towards the perceived hand position, ð& would simply be composed of two (more variable) signals, rather than only considering ð& as the afferent elements of hand position. The key difference between adding the contribution of the efferent-based motor prediction compared to simply removing the proprioceptive term from the model is that with the addition of an information source pertaining to the actual hand position, the perceived hand position would 86 change throughout the course of adaptation. As the hand (or in the isometric case, the force direction) begins to adapt away from the visual cursor, the predicted sensory feedback based on that adapted movement will also shift. This allows for a reduction of the perceptual error over the course of the experiment. This stands in contrast to removing the proprioceptive term, which results in a constant perceptual error throughout the course of adaptation, regardless of the adaptation observed. Future studies using an isometric task where the perceptual error is measured may be an effective way to determine if there is a reduction in perceptual error over time, thus suggesting there is some form of efferent contribution regarding actual hand (or effector) position. 4.2 Future Directions The findings of this study provide the basis for various future experiments. Firstly, given the prediction PReMo makes regarding a shift at the home position, future studies may be able to look into this as a potential explanation for adaptation in an isometric task. In this task, the hand remains at the home position for the entire experiment. While the participant is making isometric actions towards targets, they are experiencing perturbed visual feedback at the target regions, but are always given veridical feedback at the home position. It is possible that the association of the perturbed visual feedback with the exerted force movement could result in a shift of the perceived hand position. Under the same logic, it is possible that the veridical cursor feedback at the home location may mitigate any shift at home from developing. One potential way to determine if a shift was induced at the home location would be to intersperse perceptual probe trials among adaptation trials. Perceptual probe trials would serve as a measure of a proprioceptive shift. Participants would have their hand passively moved to a location close to home, and then would be required to use their other hand to place a visual marker (a cursor) 87 where they thought their hand was. These trials would provide insight into where the participant perceived their hand to be, and if this perception changes systematically over the course of adaptation. If their estimate of hand position following adaptation deviated towards the perturbed visual feedback compared to their estimates during baseline, this would suggest a shift at the home position was induced. Another way to infer if a shift at home was induced is to utilize targets in the entire workspace. PReMo hypothesizes that a shift at home would affect behaviour towards upwards and downwards targets in opposing directions. Since our protocol only used targets in the upwards direction, it is difficult to make conclusions about a shift at home using this assumption. Future isometric adaptation studies utilizing targets in all directions may help to determine if a shift at the home position is contributing to adaptation. For instance, if adaptation behaviour was observed in opposite directions for upwards compared to downwards targets (e.g., CW errors on upwards targets and CCW errors on downwards targets), we may conclude that a shift at the home position was induced. Additionally, if we asked participants to make point to point reaches towards targets in the horizontal plane, we may be able to deduce if there was a shift at home by comparing the amplitudes of the pointing movements between a left and right target. Another potential future study to build on the current findings would be to incorporate some form of hand report into the experimental protocol. While we may hypothesize that there is a shift of the perceived movement outcome (whether it be formed in the manner proposed by PReMo or the PEA model), we can only speculate this based on the current results. The only outcome measure used in our experiment was angular error, which measures the outcome behaviour during the task. This metric does not provide any direct evidence of a perceptual shift due to the perturbed visual feedback. Previous reaching studies using a clamped feedback 88 paradigm obtain a measure of perceived hand position by asking participants where they thought their hand crossed the target perimeter. This has typically been done by presenting a ring of numbers around the target once the participant completed their slicing movement. They were then asked to report their hand position by verbally indicating the number closest to their perceived slicing direction. In an isometric task, this becomes slightly more challenging, as the participant never actually moves their hand from the start position. Nevertheless, they are still executing a force movement towards the target, and therefore still have an associated sensory prediction of the feedback which should result from that movement. A future study could use a modified version of the hand report paradigm to obtain a measure of perceived movement outcome, and therefore quantify if a perceptual shift was induced. This may involve flashing the same number ring after the force movement is executed, and asking participants to verbally report where they think a veridical cursor would have landed if they had one. This would offer insight into whether a shift in their perceived force direction was induced by the perturbed visual feedback. The results of this follow up study would allow us to make conclusions regarding the driving signals of implicit adaptation in the isometric task. Specifically, it would allow us to refine current models of implicit adaptation to better explain the observed behaviour in this context. We may first consider the hypothetical finding of a shift in the perceived cursor direction. In early exposure trials, there would be a shift in the perceived cursor direction towards the perturbed visual feedback. If the participant were asked where their cursor would have landed if they were presented with one, they would report somewhere between the target and the clamped cursor. Over the exposure period, as the force direction drifted away from the target in the opposite direction of the cursor, the perceived cursor direction (in other words, 89 where the participant believed they aimed) would also slowly drift towards the target. If this were the case, these findings would support the notion of adding an efferent contribution towards perceived hand/cursor position. In the context of PReMo, the model in its current form cannot account for this re-alignment in an isometric task, since the removal of the proprioception contribution towards perceived hand position would result in a proprioceptive shift which would remain unchanged throughout the experiment. Thus, if we saw realignment in our experimental results, amendments to the model would need to be made to account for this finding. Similarly, the PEA model would need revisions to encompass this finding. Removing the proprioceptive contribution to the perceived hand position would result in a constant perceptual error driven by the visual cursor and motor goal. An efferent contribution towards the perceived hand position derived from the actual motor command which was sent would allow for the model to predict a dynamic perceptual error which reduced over the course of adaptation. By obtaining a âhandâ report during the isometric task, we would be able to determine which model best fits the observed adaptation by examining if the proprioceptive/perceptual error was ever nullified. PReMo predicts that the asymptote of adaptation is reached once the proprioceptive error is corrected for. On the other hand, the PEA model hypothesizes that the plateau of adaptation is reached at the equilibrium point between learning and forgetting, which occurs before the perceptual error is completely removed. Thus, by obtaining an experimental measure of the perceptual error, we may be able to make a distinction between which model best explains implicit adaptation in an isometric task. 4.3 Conclusions The purpose of this study was to examine the contributions of efferent compared to afferent signals towards implicit sensorimotor adaptation. Through the use of isometric 90 movements, we were able to parse apart the actual proprioceptive feedback provided when reaching from the sensory predictions derived from the efference copy of the given movement. When proprioceptive feedback was decoupled from the task, implicit adaptation still prevailed during the clamped visual feedback task. Furthermore, the adaptation observed was larger compared to a typical reaching task using identical visual feedback. These findings provide important insight into the sensory information sources which may be necessary for adaptation. The motivation for this study was rooted in PReMo. Since the model hypothesizes proprioception plays a critical role toward adaptation, we were interested in directly testing this notion. Although proprioception cannot be removed, our experimental set-up removes the relevance of the information source towards the task itself. Our results indicate that even when proprioception is irrelevant to the task goal, adaptation is not only induced, but heightened. This finding challenges the central notion of PReMo, and prompts the need for alternative theoretical explanations. The PEA model offers a similar but unique explanation for implicit adaptation. This model hypothesizes that we utilize three main sensory information sources to predict the location of our hand: proprioceptive feedback, the motor prediction at the target, and vision of the cursor. The integration of all three sources in the presence of perturbed visual information results in a perceptual error between the perceived hand position and the target, inducing adaptation. Theoretically, this model best explains our findings. The idea of a perceptual error fits well with the isometric task, as it highlights the high-level nature of the error. This contrasts with PReMo, which places emphasis on re-aligning the actual proprioceptive feedback. Bayesian cue combination models offer the intuitive explanation for behaviour in which we weight sensory signals differently depending on their relevance or certainty in the given context. This logic 91 allows for the explanation of heightened adaptation in the isometric task, where we may weight the visual cue of the cursor higher in a situation where the proprioceptive cue is essentially dissociated from the intended reach location. Alternatively, it may be the case that the proprioceptive cue is highly variable, rather than fully removed. This, potentially in combination with a more variable motor cue due to the isometric nature of the task, would also explain heightened adaptation. Finally, it is possible that there is an efferent contribution related to the motor command itself, which changes over the course of adaptation. In the end, whether this model needs mathematical or conceptual revisions to properly encapsulate the given data, the idea of cue combination is an appealing and reasonable approach to understanding the mechanisms for implicit motor adaptation. To the best of our knowledge, this was the first study to isolate implicit adaptation during an isometric task. The results offer insights into the potential sensory cues needed to induce adaptation, and allow us to critically examine the current models of implicit adaptation. While these findings are novel and important towards our greater understanding, various future studies are still needed to confirm the driving mechanisms for adaptation. 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Perceptual error based on Bayesian cue combination drives implicit motor adaptation [Preprint]. https://doi.org/10.7554/eLife.94608.1 102 Appendix A Calibration matrix Kð¹ð»8ð¹ð»5ð¹ð»5N = O0.05263 â0.00245 â0.002690.00198 0.05331 0.000990.00048 0.00067 0.05756 W Oð ð¥ð ð¦ð ð§ W Calibration matrix for the 3-axis force sensor (provided by Tec Gihan). FHx, FHy, FHz: Load value (N) after correction. e x, e y, e z: strain output (x 10-6 strain). Force mapping and cursor display When the cursor enters the automatic zone, the trajectory will no longer be in the participantâs control. The force angle will be determined by the last 5 samples of the non-automatic zone. The magnitude of the cursor will still be tied to the participantâs movement in the following manner: ð¥ = ðððððððð â ððð ððð(ðððððððððð) ð¦ = ðððððððð â ð ððð(ðððððððððð) Here, x and y are with respect to home centre, and forcemag is the resultant force in Fx and Fy * 1.25, where Fx and Fy have been scaled according to the set gain (1N ~ 12 mm). In other words: ðððððððð = 1.25 â ððð ð¢ðð¡ððð¡ ððððð In order to keep the cursor moving, forcemag grows on every poll iteration according to: ðððððððð = ðððððððð(ðððð£ððð¢ð ) + 1.25 â ððð ð¢ðð¡ððð¡ ððððð(ðð¢ððððð¡) This ensures that the resultant force vector increases with each poll iteration, and therefore the x and y coordinates change to advance the cursor trajectory. 103 Appendix B R2 p value 0.814 < .001 0.643 < .001 0.829 < .001 0.901 < .001 0.704 < .001 0.939 < .001 0.869 < .001 0.910 < .001 0.628 < .001 0.724 < .001 0.523 < .001 0.728 < .001 0.852 < .001 0.929 < .001 0.807 < .001 0.735 < .001 0.625 < .001 0.654 < .001 0.887 < .001 0.727 < .001 0.852 < .001 Table 1. Correlating angular error with peak resultant force. R-statistics and p-values for each participant when correlating angular error derived from the terminal portion of the first 1/3rd of the force trajectory (and displayed as feedback) and the angular error derived at peak resultant force. Each row corresponds to a separate participant.
Thesis/Dissertation
eng
Kinesiology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Chua, Romeo
The role of afferent vs efferent signals in implicit sensorimotor adaptation
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