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Improving manual interception accuracy through eye and hand training Lalonde, Kathryn Mackenzie 2015

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    IMPROVING MANUAL INTERCEPTION ACCURARY THROUGH  EYE AND HAND TRAINING   by   Kathryn Mackenzie Lalonde  B.Sc., The University of British Columbia, 2013     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE   in   The Faculty of Graduate and Postdoctoral Studies   (Neuroscience)     THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)    July 2015      © Kathryn Mackenzie Lalonde, 2015   ii ABSTRACT   Accurate hand movements are important for many daily activities and we frequently use vision to help guide our interactions with our environment. Here we investigated whether smooth pursuit training transfers to hand movements by examining manual interception accuracy.    We conducted three series of five-day perceptual-motor learning experiments. In a track-intercept task, observers were instructed to track a moving target on a screen and to hit it with their index finger as soon as it entered a “hit zone”. In each trial, only the first part (100-300 ms) of the trajectory was shown and observers had to extrapolate and intercept the target at its assumed position. In all three experiments, subjects were tested on an eye-hand coordination task on the first day (day 1, pre-test) and last day (day 5, post-test); the three experiments differed with regard to training on days 2-4. Further, subjects were invited to complete the eye-hand coordination task during a one-week follow-up session after the post-test (day 6). Experiment 1 (n=9) involved no hand movements during training; subjects only tracked the target with their eyes and received no visual feedback. Subjects in Experiment 2 (n=9) tracked and intercepted the target during training. Experiment 3 (n=9) served as a control and involved no training. Subjects in all groups were invited to come back one week after the post-test for a follow-up testing session.    Results show that manual interception performance (finger position error) improves in all groups, but improves most following combined eye-hand training. Interestingly, this group also resulted in the greatest improvement in eye movements. This finding is particularly noteworthy because both training groups involved the same degree of eye-movement training, but eye movements improved only if combined with engaging the hand. Analysis of performance in the one week follow-up after the post-test revealed that training effects in the eye-hand group were particularly long-lasting and stable, whereas eye movements continued to improve through to the week follow-up.    I will discuss implications of these results for our understanding of the brain pathways underlying eye and hand movement control, as well as practical applications in sports and clinical rehabilitation.                    iii PREFACE   Experiments are based on work conducted in UBC’s Neuroscience of Vision and Action (NOVA) Laboratory (ICORD, Vancouver General Hospital and ICICS, UBC Vancouver Campus), supervised by Dr. Miriam Spering.    Dr. Miriam Spering, Dr. Sang-Hoon Yeo, and fellow graduate student Jolande Fooken were responsible for all aspects of computer programming of this experiment. I was responsible for aiding in designing the experiment, recruiting subjects, and running the experiment. I worked on data processing and data analysis under the guidance of Dr. Miriam Spering. I was responsible for writing the drafts that were associated with this project. This work has been presented in poster form (Lalonde, Fooken & Spering, 2015).   The UBC Behavioural Research Ethics Board approved all procedures related to this work (title: “Eye-hand coordination”). Ethics board certificate: # H13-01645. This work has been funded by NSERC (CGS-M Award to KML, 2014-2015; NSERC DG to MS, 2013-2017) and CFI (John R. Evans Leaders Fund to MS, 2013-2016).                 iv TABLE OF CONTENTS  Abstract ..................................................................................................................................... ii  Preface ...................................................................................................................................... iii  Table of Contents ..................................................................................................................... iv  List of Tables ............................................................................................................................ vi  List of Figures ......................................................................................................................... vii  Acknowledgements ............................................................................................................... viii  Dedication ................................................................................................................................. ix  Chapter 1 – Introduction ......................................................................................................... 1             1.1 Overview of the Different Types of Eye Movements ............................................... 1                         1.1.1 Smooth Pursuit Eye Movements ................................................................ 2             1.2 Pursuit Brain Pathways and Associated Neuroanatomical Structures ...................... 6                         1.2.1 Retina and Lateral Geniculate Nucleus (LGN) .......................................... 7                         1.2.2 Striate or Primary Visual Cortex (V1) ....................................................... 8                         1.2.3 Middle Temporal Visual Area (MT/V5).................................................... 8                         1.2.4 Middle Superior Temporal Visual Area (MST)......................................... 9                         1.2.5 Frontal Eye Fields (FEF) and Supplemental Eye Fields (SEF) ................. 9                         1.2.6 Lateral Intrapariental Areas (LIP) ............................................................ 11                         1.2.7 Superior Colliculus .................................................................................. 11                         1.2.8 Pontine Nuclei .......................................................................................... 11                         1.2.9 Cerebellum ............................................................................................... 12                         1.2.10 Oculomotor Neurons .............................................................................. 12             1.3 Visual Information Processing for Action and Perception ..................................... 13                         1.3.1 Vision for Action and Vision for Perception ........................................... 13                         1.3.2 Relationship of Smooth Pursuit and Perception ...................................... 13             1.4 Perceptual Learning ................................................................................................ 14             1.5 Motor Training and Smooth Pursuit ....................................................................... 16             1.6 Manual Interception ................................................................................................ 17             1.7 Current Experiment ................................................................................................. 18  Chapter 2 – Methods .............................................................................................................. 19             2.1 Observers ................................................................................................................ 19             2.2 Visual Stimuli and Set-Up ...................................................................................... 19             2.3 Experimental Procedure and Design ....................................................................... 21                         2.3.1 Procedure during Pre, Post and Week Sessions ....................................... 22                         2.3.2 Procedure during Training Sessions ........................................................ 23             2.4 Data Preprocessing and Analysis ............................................................................ 24                         2.4.1 Eye Movement Recording and Analysis.................................................. 24   v                         2.4.2 Hand Movement Recording and Analysis ............................................... 27             2.5 Statistical Analysis .................................................................................................. 30             2.6 Experimental Hypotheses ....................................................................................... 30  Chapter 3 – Results ................................................................................................................. 32             3.1 Analysis of Eye and Hand Movement Performance during the Pre-Test ............... 32                         3.1.1 Eye Movement Pre-Test Performance ..................................................... 32                         3.1.2 Finger Movement Pre-Test Performance ................................................. 34             3.2 Analysis of Pre-Test and Post-Test Results ............................................................ 35                         3.2.1 Baseline Trial Comparisons ..................................................................... 35                         3.2.2 Training Effects on Eye Movements ....................................................... 36                         3.2.3 Training Effects on Hand Movements ..................................................... 39              3.3 Analysis of Training Days and Week Follow-Up Comparisons ........................... 40  Chapter 4 – Discussion ........................................................................................................... 44              4.1 Major Findings ....................................................................................................... 44                         4.1.1 Combined Eye-Hand Training is the Most Beneficial for the Hand ....... 44                         4.1.2 Combined Eye-Hand Training is the Most Beneficial for the Eye .......... 45                         4.1.3 Time-Course of Training Effects ............................................................. 48             4.2 Limitations and Future Experiments ....................................................................... 50             4.3 Practical Implications.............................................................................................. 51                         4.3.1 Sports Training......................................................................................... 51                         4.3.2 Clinical Rehabilitation ............................................................................. 52             4.4 Conclusions ............................................................................................................. 53  References ................................................................................................................................ 54                       vi LIST OF TABLES  Table 1. Details of the Training Protocols for Each Group ...................................................... 22  Table 2. Definitions of Eye Movement Measures .................................................................... 26 Table 3. Repeated-Measures ANOVAs for Eye Measures with Factors Speed and  Presentation Duration ................................................................................................. 33  Table 4. Repeated Measures ANOVAs for Finger Measures with Factors Speed and   Presentation Duration ................................................................................................. 34  Table 5. ANCOVAs for Eye Measures with Group as a Factor and Controlling for               Pre-Test Means ........................................................................................................... 39  Table 6. ANCOVAs for Finger Measures with Group as a Factor and Controlling for               Pre-Test Means ........................................................................................................... 40                               vii LIST OF FIGURES  Figure 1. Corollary discharge ..................................................................................................... 5 Figure 2. Main areas in pursuit pathway depicted in the monkey brain (lateral view) ............... 7 Figure 3. Experimental set-up ................................................................................................... 20 Figure 4. Experimental trial sequence ...................................................................................... 23 Figure 5. Example eye position trace to indicate time intervals and eye movement                 analysis features ........................................................................................................ 25  Figure 6. Matlab analysis screen with representative trace ...................................................... 27 Figure 7. trakSTAR™ finger magnet set-up ............................................................................. 28 Figure 8. Participant making a manual interception on the translucent screen ........................ 29 Figure 9. Pre-test results for eye measures averaged across n=27 subjects .............................. 33 Figure 10. Pre-test results for finger measures averaged across n=27 subjects ........................ 34 Figure 11. Pre-test and post-test baseline trial comparison by group ....................................... 36 Figure 12. Pre-test and post-test experimental results for eye measure comparison                    by group .................................................................................................................. 37  Figure 13. Averaged eye velocity traces for pre-test and post-test of two subjects .................. 38  Figure 14. Pre-test and post-test experimental results for finger measure comparison                    by group .................................................................................................................. 39  Figure 15. Training results for the eye training group .............................................................. 41  Figure 16. Training results for the eye-hand training group ..................................................... 41            viii ACKNOWLEDGEMENTS   First, thank you to Dr. Miriam Spering for providing guidance and support through this research endeavour. I have learned an incredible amount about science, writing, research and hard work. This experience has provided immense value to my educational background.    Thank you to my committee members, Dr. Lara Boyd, Dr. Romeo Chua and Dr. Deborah Giaschi for providing scientific guidance and assistance with the protocols in order to complete this thesis. Also, to Dr. Nicola Hodges for taking time to act as my external examiner.   Jolande Fooken, I need to thank you for everything over these past two years that you have helped with in relation to this project and my sanity. You have been a source of laughter, insight, inspiration and friendship. I cannot thank you enough for your companionship throughout this process. I wish you nothing but success.   And, of course, thank you to my wonderful family – especially my parents. Without you two, I am not sure I would be where I am today. Your endless support, love, admiration, care and pride have allowed me to see through the difficult times and always strive for success. Sometimes I do not think I can express my appreciation enough.                   ix                     This one is for you, Grandpa   1 CHAPTER 1 – INTRODUCTION  Humans use different types of eye movements to see and interact with the surrounding environment during daily activities. The goal of these eye movements is to bring an object of interest close to the fovea, the area of highest density of photoreceptors on the retina, and thus the area of highest visual acuity. Many aspects of our environment are moving and we often need to keep track of their timing and position, whether that is a car moving towards us or we are trying to catch a ball. In order to bring a moving object close to the fovea, we engage in an eye movement called smooth pursuit. Smooth pursuit is a continuous rotation of the eye that allows us to track moving objects. Also, smooth pursuit enables us to tackle the opposite situation in which the object is stationary, but we are moving; smooth pursuit allows us to keep a stationary object close to the fovea during self-motion. We use vision as one tool to help direct interactions with objects in our surroundings. This thesis addresses the question whether training smooth pursuit eye movements can impact hand movements, and the extent to which there is possible transfer between the two modalities. An overview of the literature on smooth pursuit and neurological correlates will be presented, followed by visual motion processing for action and perception. Finally, the topics of perceptual learning, motor training and relevant aspects of manual interception will be explored in the context of vision and action before addressing the experimental rationale and methodology.  1.1 Overview of the Different Types of Eye Movements  Different types of eye movements contribute to our ability to function in our visual environment. Eye movements are impaired in many neurological, psychiatric and neurodegenerative disorders, and examining eye movements can be used as a tool for detection   2 and diagnosis due to the systematic changes observed (Anderson & MacAskill, 2013; Bittencourt et al., 2013).   Different eye movements vary on their physiological properties, neural correlates, function and outcomes (Carpenter, 1988; Leigh & Zee, 2006). From a functional perspective, we can divide eye movements into two categories: gaze-stabilizing and gaze-shifting (Leigh & Zee, 2006). Fixational eye movements (microsaccades, drift, tremor), optokinetic reflex (OKR) and vestibulo-ocular reflex (VOR) are examples of gaze-stabilizing eye movements, but they will not be discussed further in this thesis. Instead, more emphasis will be placed on gaze-shifting eye movements. These include vergence, saccades and smooth pursuit; the latter being the primary focus of this research.   1.1.1 Smooth Pursuit Eye Movements  Smooth pursuit eye movements are slow rotations of the eyes that allow tracking of a moving object, as mentioned previously. These smooth eye rotations need to be accurate and precise in order to avoid motion blur (Bedell, Moulder, & Qian, 2012). Interestingly, there does not need to be physical/retinal motion of a target for smooth pursuit to occur; the perception of motion can be sufficient to trigger a pursuit response. In his seminal study, Steinbach (1976) demonstrated this by having two light sources illuminated on a wheel that was moving horizontally in a dark room while observers watched on. Observers perceived a rolling wheel with these two light sources and tracked the centre of the wheel despite not having any other visual information or knowledge of the wheel itself. Observers were able to pursue the moving centre of the wheel despite not being able to see it. Many other studies have since been completed that have confirmed these initial findings indicating that when an observer perceives   3 motion, they will be able to track this motion with smooth pursuit (for a review, see Schütz, Braun & Gegenfurtner, 2011).   Smooth pursuit in humans usually has a latency of 80-120 ms due to delays in visual motion processing and target selection (Leigh & Zee, 2006). Observers are generally able to track a moving target in the range of 1-100 deg/s (Ilg, 1997). Despite this, the smooth pursuit system is generally not successful at keeping up with target motion that exceeds 30 deg/s (Robinson, 1965). When this system begins to fail and position and velocity errors increase, smooth pursuit eye movements are supplemented by catch-up saccades (de Brouwer, Yuksel, Blohm, Missal, & Lefèvre, 2002). In general, saccadic eye movements are fast, jerky movements with high velocities and allow the observer to shift their gaze to stationary objects of interest (Becker, 1991). Catch-up saccades allow the eye to shift position quickly while trying to keep the target on the fovea. Thus, it is the combination of these two types of eye movements that allow a human observer to track a moving object along its trajectory. Under ideal circumstances (small, high-contrast, visible target) pursuit gain – that is, the ratio of the velocity of the eye movement relative to target velocity – will be close to one. However, when the visual target disappears, observers are able to continue tracking it for some time (up to 4 sec after an initial degradation at 0.45 sec to 60% of the initial velocity after target disappearance) albeit at a lower pursuit gain (Becker & Fuchs, 1985; for a review, see Barnes, 2008).   Smooth pursuit can be divided into two different phases: open-loop and closed-loop or steady-state (Tychsen & Lisberger, 1986). The open-loop phase is the initiation phase and is usually defined as the first 140 ms from onset of eye movement. During this time, internal feedback about the retinal error between the eye and target motion is not available to the oculomotor system. Therefore, open-loop pursuit is driven solely by the retinal image velocity of   4 the moving target. After the first 140 ms, during closed-loop tracking, the visual system receives feedback from the oculomotor system (either efference-copy feedback or proprioceptive feedback). Due to this negative feedback, the error between the eye and target motion is minimized compared to the open-loop phase. Retinal slip is slip of the visual image across the retina due a difference in velocity of the eye and target that induces optokinetic eye movements; pursuit and saccades. A goal of the pursuit and saccadic systems is to cooperatively minimize retinal slip (Leigh & Zee, 2006). By examining the two different phases, we can understand different aspects about the mechanisms underlying smooth pursuit control.   As mentioned above, feedback is used to enable appropriate motor performance through the use of either an efference copy of the motor command or proprioceptive feedback. An efference copy signal is an internal copy of the efferent motor command that is concurrently sent to the sensory areas in the brain. This procedure of relaying an internal copy of the motor command to the sensory structures is also known as corollary discharge (see Figure 1; Sommer & Wurtz, 2008). Crapse and Sommer (2008) have highlighted two roles of corollary discharge: facilitating suitable motor performance and resolving uncertainty in sensory inputs. The information gained by corollary discharge allows our movements to be steady and meticulous by permitting updating sensory and motor signals online. Alternatively, proprioception is the ability to determine the orientation and placement of one’s limbs in space and relative to other limbs.  Proprioceptive feedback is afferent feedback from the proprioceptors in the muscles. It is this feedback that allows for corrective movements and readjustments for future motor commands. For eye movements, the stretch receptors in the ocular muscles provide feedback to the brainstem and oculomotor system (Weber & Daroff, 1972), and they discuss this particular feedback loop as a possible corrective mechanism for refixation saccadic eye movements.   5 Feedback information, such as the information arising from an efference copy signal and proprioceptive feedback, is critical in a model of smooth pursuit eye movements.  Figure 1 - Corollary discharge. As a motor command is sent to the muscles, a copy of that motor command is sent to the appropriate sensory areas for feedback to close the sensorimotor loop. Modified from Crapse and Sommer (2008).   Although smooth pursuit is largely visually driven and affected by stimulus features such as velocity, luminance and colour (for a review, see Spering & Montagnini, 2011), it can also be influenced by a variety of cognitive factors, such as expectation (for reviews, see Barnes, 2008; Kowler, 2011). For instance, some studies have shown the presence of anticipatory pursuit for future target motion (Kowler, 1989). Expectations of future target motion have an impact on oculomotor control as the observer’s eyes will begin to move in the anticipated target direction.   6 The oculomotor control response will be impacted by these expectations, whether or not the target motion is predictable or recurring (Kowler & Steinmann, 1979a;b). This is important because it implies that there is a predictive component to the execution of smooth pursuit eye movements.  1.2 Pursuit Brain Pathways and Associated Neuroanatomical Structures  The pursuit pathway in the brain has been uncovered via different experimental procedures – lesion studies in primates, behavioural studies in monkeys and humans, clinical studies in patients, and imaging studies of the healthy or pathological brain – to determine the role of a given anatomical structure and in visual motion processing and in the generation of smooth pursuit. Most studies presented in this section are completed with monkeys; however, those that are conducted with humans are identified as such. A brief overview of the smooth pursuit pathway will be given below before looking at each neuroanatomical component in more detail separately.  Visual motion information is processed in the retinal ganglion cells and then sent to early visual cortical areas, such as V1, via the magnocellular pathways in the lateral geniculate nucleus (LGN). Motion signals are then sent to the middle temporal visual area (MT) and middle superior temporal visual area (MST), the brain’s motion processing centres. Then, motion information is transferred to the frontal areas for initiation and maintenance of pursuit (such as frontal eye fields, or FEF). The brainstem nuclei will receive information from these frontal areas and pass them along to the cerebellum, which will enable the motor command for the cranial nerves (III, IV and VI) to innervate the extraocular muscles to move the eye. Many of these connections are reciprocal. Pursuit pathways and main anatomical substrates will be discussed in more detail below (see Figure 2).   7  Figure 2 – Main areas in pursuit pathway depicted in the monkey brain (lateral view). Not all of the areas involved in the pursuit pathway are identified in this figure, but will be discussed in the text. V1: primary striate cortex; MT/MST: middle temporal/middle superior temporal visual area; FEF: frontal eye fields; PON: pontine nuclei; Verm: Vermis; VPF: ventral paraflocculus. Adapted from Krauzlis (2004).   1.2.1 Retina and Lateral Geniculate Nucleus (LGN)  The retina is neural tissue that is located at the back of the eye. It processes light signals and subsequently advances these signals to brain regions and pathways that enable us to see. The outer layer of the retina is covered by rod and cone photoreceptors, with the highest density of cones being at the fovea (for a graphical representation, see: Land & Tatler, 2009). These photoreceptors interact with other cells located throughout the deeper levels of the retina to reach the retinal ganglion cells in the terminal layer. The retina houses the optic disk where the axons of the retinal ganglion cells converge to become the optic nerve (Kandel, Schwartz, Jessell,   8 Siegelbaum, & Hudspeth, 2013). The optic nerve connects to the LGN in the thalamus, where it receives sensory input from the retina. The LGN has two different types of neurons: magnocellular and parvocellular. The first two layers of the LGN are magnocellular and contain neurons with wide receptive fields and quick conduction responses that are necessary for the perception of motion and depth. The remaining four layers are parvocellular and contain neurons with smaller receptive fields with sustained responses necessary for the perception of colour, form and objects (Livingstone & Hubel, 1988). 1.2.2 Striate or Primary Visual Cortex (V1)  The primary visual cortex is the first level of cortical processing along the visual pathway. The primary visual cortex receives inputs from the LGN; more specifically for smooth pursuit, from the magnocellular layers of the LGN (Maunsell & Newsome, 1987). V1 has a retinotopic map that represents the entire visual field and thus receives all the visual information collected by the retinal cells. Neurons in V1 are selective for motion direction, spatial frequency, and orientation (Hubel & Wiesel, 1959; Livingston & Hubel, 1988). Once the visual information has been processed in the primary visual cortex, its output is sent to other cortical areas for further processing.  1.2.3 Middle Temporal Visual Area (MT)  MT receives input from the primary visual cortex, largely from the direction-selective and speed-tuned neurons and is known to facilitate in the processing of motion signals (Movshon & Newsome, 1996). MT is a major processing stage for visual-motion information that is used to guide pursuit, and most MT neurons code for acceleration, speed and direction of the moving target (Lisberger & Movshon, 1999). Newsome, Wurtz, Dürsteler, and Mikami (1985) used ibotenic acid to lesion MT, resulting in a decrease in eye velocity during the pursuit initiation.   9 Thus, it has been concluded that MT is primarily involved in the initiation of smooth pursuit eye movements (Krauzlis, 2004). In humans, imaging studies have confirmed activation of MT/MST (often referred to as MT+ or V5) in motion perception tasks (e.g., Huk & Heeger, 2001) and identified visual field maps in human MT (Amano, Wandell & Dumoulin, 2009) as well as detailed motion processing pathways (Lanyon et al., 2009). Furthermore, the MT+ complex is also involved in human smooth pursuit (e.g., Hartmann, Bremmer, Albright, & Krekelberg, 2011; Konen, Kleiser, Seitz, & Bremmer, 2005).  1.2.4 Middle Superior Temporal Visual Area (MST)  MST and MT are often studied together, but are distinct cortical regions. MST sits adjacent to MT at the occipital-temporal-parietal junction (Leigh & Zee, 2006). MST receives direct input from MT (Maunsell & Van Essen, 1983). Unlike MT neurons, some MST neurons respond to object motion even in the absence of a retinal motion signal; they can respond to imaginary target motion (Ilg, 2008). MST has been shown to be more important in the maintenance of smooth pursuit motion, as indicated by chemical lesion studies, which produced directional deficits during the maintenance of pursuit (Dürsteler & Wurtz, 1988). MST neurons also appear to receive vestibular input, which is the sensory system involved in balance and spatial orientation taking into account eye and head movements (Leigh & Zee, 2006).  1.2.5 Frontal Eye Fields (FEF) and Supplemental Eye Fields (SEF)  The FEF are located in the frontal cortex and have been shown to be involved in the directional selectivity of pursuit responses through direct input from MT/MST. The FEF has been largely known for its role in gaze-shifting saccadic eye movements, but a subarea of FEF, FEFsem, seems to be involved in pursuit (for a review, see Krauzlis, 2004). Tanaka and Lisberger (2002) applied microstimulation to FEFsem producing an increase in gain and induction of   10 smooth pursuit. Further, injections of muscimol used to deactivate the FEFsem affected directionality (ipsilateral more affected than contralateral), impaired pursuit acceleration and impaired eye velocity causing an increase in catch-up saccades (Shi, Friedman & Bruce, 1998).   The SEF is located in the supplemental motor area (SMA) and is involved in the control of pursuit. Due to SEF being located in the SMA, it has been thought that it would likely exhibit the same planning characteristics the SMA does for other motor movements, implying that SEF would be involved in the preparing of pursuit eye movements (Tanji, 1996; Krauzlis, 2004). Missal and Heinen (2001) used electrical microstimulation in macaque monkeys in SEF during fixation, smooth pursuit initiation and steady-state pursuit. They showed that when current was applied to this area during fixation, smooth pursuit was not elicited. There was also no effect during steady-state pursuit. Despite this, microstimulation during pursuit initiation did facilitate smooth pursuit by increasing eye velocity and initial acceleration. In addition to its role in pursuit initiation, SEF plays a key role in the control of predictive pursuit. Kim, Badler and Heinen (2005) examined the relationship between SEF activation and predictive eye movements. In this study, awake behaving monkeys played “ocular baseball”, a paradigm in which a small target moves towards a strike zone and either hits or misses. Monkeys had to identify trials as “strike” or “ball” trials by either moving their eyes to “hit” the ball and then track it or by continuing fixation if they predict a “ball”. In a waiting period after the initial cue trajectory, SEF neurons were differentially activated depending on whether monkeys were going to track or fixate. This pattern of SEF activation was thus indicative of monkeys’ subsequent behavior. Accordingly, SEF neurons have been shown to reflect anticipatory pursuit in response to predictable events (Heinen & Liu, 1997).     11 1.2.6 Lateral Intraparietal Areas (LIP)  LIP receives projections from the FEF, and is also known to be part of the saccadic eye movement system (O’Leary & Lisberger, 2012). Bremmer, Distler, and Hoffman (1997) combined a pursuit paradigm with single-cell recordings in macaque monkeys and found that 39% of LIP neurons were directionally-selective and modified by eye position.  1.2.7 Superior Colliculus (SC)  The SC has a well-established role in the saccadic eye movement system, but recent evidence has shown that it is involved in smooth pursuit as well. Basso, Krauzlis, and Wurtz (2000) activated (electrical stimulation) and inactivated (reversible chemical injection) the rostral SC and showed that pursuit initiation and maintenance were affected by both experimental manipulations. The authors concluded that the SC provides a position signal; moreover, this area is involved in target selection for a variety of movements (for a review, see Krauzlis, Liston & Carello, 2004), including saccades (Kim & Basso, 2008; Shen & Paré, 2014), pursuit (Gardner & Lisberger, 2002) and reaching (Song & McPeek, 2015). 1.2.8 Pontine Nuclei  The pontine nuclei are a group of grey matter cells that are part of the pons in the brainstem. The pontine nuclei receive input from the FEF and MST/MT, and send projections to the cerebellum, mainly the flocculus and dorsal vermis (Leigh & Zee, 2006; Krauzlis, 2004). The pontine nuclei are involved in the control of eye movements. Gaymard, Pierrot-Deseilligny, Rivaud and Velut (1993) completed a case study on four patients who all had basal pontine lesions. Their eye movements were recorded and the most noticeable abnormality was ipsilateral hindrance of both smooth pursuit and optokinetic nystagmus.      12 1.2.9 Cerebellum  The cerebellum is generally involved in motor control, but has also been shown to have a more complex role contributing to cognitive tasks (Kim, Ugurbil, & Strick, 1994; Middleton & Strick, 1994). For instance, patients with cerebellar damage suffer severe deficits related to various visual and motor functions, including motor learning (e.g., Therrien & Bastian, 2015). The cerebellar flocculus, paraflocculus and vermis are involved in smooth pursuit. Microstimulation of the ventral paraflocculus (VPF) neurons can induce smooth pursuit eye movements within 10 ms while monkeys attempt to fixate (Belknap & Noda, 1987). The flocculus and paraflocculus appear to be more imperative in pursuit maintenance (Krauzlis, 2004). Lastly, the dorsal vermis seems to be more important in the onset of pursuit. Ablation of the dorsal vermis in monkeys impacted the open-loop phase of the eye movements by decreasing peak acceleration and eye velocity (Takagi, Zee, & Tamargo, 2000).  It appears likely that an efference copy of the eye movement motor command is created in the brainstem and/or cerebellum and sent to the sensory-motor areas such as LIP/FEF for integration with visual information (Sommer & Wurtz, 2004a;b).  1.2.10 Oculomotor Neurons  Three pairs of extraocular muscles move the eyes in the horizontal, vertical and torsional directions (about the line of sight). These six muscles are controlled by three cranial nerves: the Oculomotor nerve (III), the Trochlear nerve (IV) and the Abducens nerve (VI). The Oculomotor and Trochlear nerves originate from the midbrain. The Abducens nerve originates from the pons. These nerves receive motor input from the cerebellum and brain stem nuclei that cause the muscles to contract (Leigh & Zee, 2006).    13 1.3 Visual Information Processing for Action and Perception 1.3.1 Vision for Action and Vision for Perception  Visual information is processed to guide perception – the active process of extracting information from our environment and interpreting images to form coherent and meaningful information – as well as motor actions. Two visual processing streams are often associated with “vision-for-action” and “vision-for-perception”: The dorsal stream projects to the parietal lobe and is associated with tasks such as object localization and visuomotor control (“vision-for-action”). The ventral stream goes down into the temporal lobe and is more involved in object recognition and perception (“vision-for-perception”; Milner & Goodale, 1992; Goodale, 2011). According to this view, visual signal processing in the brain is determined by the purpose for which we are using these signals. For example, if we are going to grasp a cup, it is imperative that we use the vision-for-action stream to acquire information about the exact size of the cup and make the appropriate hand aperture. If we were just looking to observe some feature about the cup, then we would need to elicit vision-for-perception as it would be more important to process information about the cup’s colour, orientation, and so forth.  1.3.2 Relationship of Smooth Pursuit and Perception   Despite the assumption of partly separate processing streams for vision-for-perception and vision-for-action, the link between visual perception and eye movements is very close. In fact, eye movements are often used as a way to understand some aspect of visual perception or visual cognition – in areas such as developmental psychology, computer science, marketing and sports/exercise science. Although dissociations have been reported (Spering & Carrasco, 2015), the link between motion perception and smooth pursuit eye movements is particularly close, given shared neuronal substrates (pathways through area MT/MST) and similarities in sensitivity   14 (for a review, see Spering & Montagnini, 2011). Accordingly, a recent study has shown, for the first time, a transfer of perceptual learning of motion direction to untrained smooth pursuit eye movements (Szpiro, Spering, & Carrasco, 2014).   The close perception-pursuit link is also demonstrated by studies reporting that pursuit enhances perception (Bennett, Baures, Hecht, & Benguigui, 2010; Spering, Schütz, Braun, & Gegenfurtner, 2011). For example, Spering et al. (2011) conducted a study with a paradigm called “eye soccer” in which participants had to fixate or track a visual target (the “ball”) moving towards a vertical line segment (the “goal”). Critically, the target was presented only briefly and observers had to judge whether it would have missed or hit the goal if the ball had continued to move. They showed that motion prediction was significantly better in trials in which the ball was tracked (versus trials in observers fixated). This study highlights the importance of the interaction between visual perception and pursuit, and the potential to use this interaction to improve prediction and motion perception.  1.4 Perceptual Learning  Perceptual learning is improvement in a particular perceptual skill, such as the detection or discrimination of orientation, contrast, motion or spatial features, following extensive practice. Perceptual learning indicates plasticity in the mature human brain and has been shown to improve perceptual deficits in diseases such as amblyopia (for a review, see Levi, Knill, & Bavelier, 2015). In the perceptual learning literature, it is the general consensus that perceptual learning is highly specific to the task, direction, and visual field (Ahissar & Hochstein, 1997; Ball & Sekuler, 1987; Fahle & Edelman, 1993). For instance, Florentini and Berardi (1980) required participants to discriminate between gratings of a different waveform to see whether perceptual learning of one orientation and spatial frequency combination would transfer to   15 another orientation and spatial frequency. They found that training was specific to both the orientation and spatial frequency and there was no transfer of performance. This can also be shown for perceptual learning that occurs in a certain visual field; perceptual improvements usually do not transfer to different locations in the visual field other than what was trained (Sagi, 2011).  Though it has been shown numerous times that perceptual learning is highly task and stimulus specific and does not transfer across eyes, Szpiro, Spering, and Carrasco (2014) have recently shown transfer in a training study across modalities – from perception to eye movements. In their task, observers had to discriminate motion direction by following the motion with their eyes and then make a perceptual judgment on its direction during the pre-test and post-test. There were two training groups: a perceptual group which made perceptual judgments about motion direction during fixation and a smooth pursuit group which used their eye movements to follow the motion direction during training days without making a perceptual judgment. In the perceptual training group, there was consistent overestimation of direction differences. Interestingly, this overestimation also transferred to smooth pursuit, even though observers did not smoothly track the stimulus during training. The smooth pursuit-training group did not experience the same overestimation or transfer between modalities. From these novel findings, we can see that there can be transfer between modalities on certain types of perceptual learning tasks. Further, this opens the door for many research opportunities to look at other motor responses or eye movement responses in relation to perceptual learning and motor transfer. These findings are a strong motivation for the work completed in this thesis.                                              16           1.5 Motor Training and Smooth Pursuit  Motor training is the repetition of any motor tasks that leads to functional changes. Different studies have shown that the motor system is plastic and this type of training can have an impact on the organization and mapping in the brain (Taubert, Lohmann, Margulies, Villringer, & Ragert, 2011; Gartner et al., 2013; Villiger et al., 2015). Imaging studies have also revealed different activation patterns in various areas of the brain after motor training, such as a study by Walz et al. (2015) looking at the representation of movement in the brain through changes in activation patterns by fMRI with a two-week training study. Participants underwent unilateral hand motor training of their non-dominant hand, and were then tested using three different motor measures: comprised force modulation with the fist, sequential finger movements and a quick writing task. The training of the non-dominant hand showed motor improvements during testing and areas of the brain responded differently to this training, such as more activity seen in the striatum, but less in the motor cortex, after training.    The main goal of the series of studies presented in this thesis was to examine effects of smooth pursuit training on hand movements. Different studies with a variety of different subject populations have shown that smooth pursuit eye movements can be improved by training. One example, Fukushima, Tanaka, Suzuki, Fukushima, and Yashida (1996) evaluated nine healthy subjects’ ability to follow a horizontal pursuit target before and after two 30-minute training sessions. Two step-ramp training paradigms were designed – one where the target stimulus velocity increased stepwise throughout the trajectory and the other where the target stimulus velocity decreased after an initial step-up. There were training-dependent directional modifications to eye velocity in pursuit after the training paradigms and changes in the later phase of the eye movement due to adaptation.    17  Eibenberger, Ring, and Haslwanter (2012) used a short training protocol on three subsequent days to induce long-lasting changes in the open- and closed-loop phases of smooth pursuit. Healthy adults participated and were also tested five days after the initial training session. Results indicated that even simple training can induce significant long-lasting effects on the closed-loop pursuit. It should be noted that the effect sizes of these improvements, though significant, were small, presumably because pursuit eye movements are already relatively well-trained due to daily use. These results are consistent with another study by Guo and Raymond (2010) that demonstrated only marginal improvements in pursuit gain with training. Both of these studies demonstrate that smooth pursuit can be trained, despite improvements being small. Our study (see below) relies on these findings and used a more challenging pursuit task to increase the room for improvement.  1.6 Manual Interception Accurate hand movements are important for many daily activities. Manual interception of a moving target, such as catching a ball, requires the hand to reach the object at the certain time and certain place. This means that the observer needs to accurately determine the target motion in terms of acceleration, speed and position. Moreover, it also means that the observer must take into account the time for the hand to initiate movement and travel before it reaches the intended interception. Performance on manual interception tasks has been shown to be largely influenced by predictive mechanism, based on knowledge from previous trials and memory (Brouwer, Smeets, & Brenner, 2005; Brouwer & Knill, 2009). Bosco, Monache, and Lacquaniti (2012) compared the predictive mechanisms used to intercept a virtual baseball that was perturbed to zero gravity (0 g) or double gravity (2 g) on the descent by a button press, where the trajectory was either completely visible or occluded for 750, 1000 or 1250 ms. The   18 authors found that the predictive mechanisms used relied on each group’s previous experience. Those observers who had seen the full trajectories were more likely to use this information to inform them about the position of the occluded trajectories despite its disappearance, whereas those who were privy to the occluded trajectories used predictive strategies based on daily experience with gravity (1 g).   When intercepting a moving object, observers usually track the object with their eyes, even when there is no instruction to do so (Mrotek & Soechting, 2007). It is advantageous to track a moving stimulus well to ensure the highest visual resolution of the target, especially when the target trajectory is unpredictable (Brenner & Smeets, 2011). Interestingly, a number of the studies involving manual interception indicate interception by use of a button or mouse click, as seen in the Bosco et al. (2012) study summarized above. Though these studies are informative, it will be important to actively engage the hand in order to truly understand the influence of eye movement on the accuracy and precision of manual interception.  1.7 Current Experiment  In this study, we were interested to see if eye movement training can improve manual interception accuracy. To answer this question, a training experiment was conducted. The study was designed to address the impact that different training protocols – no training, eye-movement training only, or a combined training of eye and hand movements – had on the accuracy of manual interception of a disappearing visual target. Looking across these different groups will enable us to examine if eye movement training alone is sufficient to improve hand movements. We will explore the impact of these training protocols on different aspects of eye movements and hand movements over the training and testing days, and evaluate the duration or type of training needed.    19 CHAPTER 2 – METHODS   Observers participated in training variations of a manual interception task based on a novel paradigm developed in our lab (Fooken, Yeo, Pai & Spering, 2014). This paradigm requires observers to track a moving target that disappears after a brief presentation across a screen and use their pointer finger to intercept the target’s extrapolated trajectory.  2.1 Observers  Participants were 27 right-handed individuals (mean age = 24.1 years; SD = 3.5; age range = 19-33 years; 14 female). All had normal or corrected-to-normal visual acuity with soft contact lenses or glasses and all participants were right-handed (as assessed via self-report). Observers were screened to confirm normal binocular visual acuity using the ETDRS visual acuity chart at 4m-test distance (Original Series Chart “R”; Precision Vision, La Salle, IL). Most observers were undergraduate or graduate students at the University of British Columbia (UBC). Author KL participated in the study, but all other participants were unaware of the purpose of the experiment. Data from included author and untrained observers were not systematically different.  The UBC Behavioural Research Ethics Board approved all experimental procedures. Inclusion criteria for participation in the experiment were 19-60 years of age, normal or correct-to-normal visual acuity, and no history of psychiatric, neurological or eye disease. All observers participated with written informed consent prior to conducting the experiment. The study took up to six sessions and participants were compensated $10 per hour for their participation.         2.2 Visual Stimuli and Set-Up  Participants were seated comfortably with their head supported by a memory-foam chin and forehead rest in a dimly lit room. Visual stimuli were back-projected by a Vivid LX20 LCD   20 projector (Christie Digital Systems, USA) with a refresh rate of 60 Hz onto a translucent screen (see Figure 3). The screen consisted of a non-distorting white PVC film with a thickness of 7 mil (0.0254 mm) surrounded on both side by a 4 mm thick glass panel, a Twin White Rosco screen for front and rear projection that was 61.6 cm(H) by 46.4cm(V) in dimensions. This screen was clamped onto a solid glass plate and fixed in an aluminum frame (all profiles are 25 mm thick). The projected display window was 49.8 cm(H) by 39.8 cm(V). The display screen was 46.25 cm away from the seated participant who viewed the stimulus binocularly.  Figure 3 - Experimental set-up. Observers were seated with their head in a chin and forehead rest (A) facing the translucent screen (B), with the image being back-projected onto this screen for viewing by a projector (C).  Eye movements were recorded with an Eyelink 1000 tower mount (D) and hand movements were recorded with a trakSTAR™ magnetic tracker (E). (F) is the generator of the magnetic field.    21  The pursuit target was a small, black ball of luminance 5.44 cd/m2 multiplied by a Gaussian function, which made the edges of the ball less defined (Gaussian dot, SD = 0.38°). The background was divided vertically into two different halves; the left side (“tracking zone”) was set to a light grey (35.87 cd/m2) and the “hit zone” on the right side to a slightly darker grey (31.45 cd/m2). In order to initiate any of the trials in the experiment, the observer had to fixate on the target within a radius of 2.8 degrees or less (this is known as drift correction). The ball was launched from the same fixation point (-14° horizontal) and followed a curved trajectory from left to right. The trajectory was simulated to be the parabolic flight of a batted baseball.  The stimulus display and data collection were controlled by a networked PC and the eye tracker was controlled by an Eyelink® tower PC (graphics card: NVIDIA GeForce GT 430). The experiment was programmed in Matlab 7.1 using Psychtoolbox 3.0.8.  2.3 Experimental Procedure and Design  The experiment was set-up in a pretest-posttest design and comprised of three different experiments, which will be referred to as groups from here forward. This study was run as single-blind study, and observers were only told about the experimental conditions upon completion of the final day of testing during debriefing. Participants were assigned to one of three different groups of n = 9 each prior to coming into the lab for the pre-test: eye training group (mean age = 26.1 years; SD = 4.1; age range = 22-33; 5 female), eye-hand training group (mean age = 23.8 years; SD: 2.9; age range: 21-29; 5 female), and a control group (mean age = 22.4, SD: 3.0; age range: 19-27; 4 female). The experiment consisted of five consecutive days of 30 to 45-minute sessions. Participants were also asked to come in a week later for a follow-up session. All groups performed the same task on day 1 (pre), day 5 (post), and day 6 (week). However, task and procedure on days 2-4 varied depending on assigned training group. Table 1   22 denotes the different groups and their respective training for days 1 through 5, which will be detailed in the following section. Table 1  Details of the Training Protocols for Each Group  Condition/Group (n=9 each) Pre-test (Day 1) Training (Days 2-4) Post-test (Day 5) Week  Follow-Up (Day 6)   Eye Hand   Eye training X X  X X  (n=8) Eye-hand training X X X X X (n=4) Control X   X X (n=6)  2.3.1 Procedure during Pre, Post and Week Sessions  At the beginning of each pre-, post- or week session, participants first completed 60 baseline trials during which participants tracked a stimulus moving along an entirely visible curved trajectory. During experimental trials, the visual stimulus would disappear after a brief presentation time (100, 200 or 300 ms), while being launched at different speeds (25°/s, 30°/s or 35°/s). This resulted in nine randomized conditions with 18 trials each, for a total of 162 experimental trials per subject and session. It should be noted that these conditions were selected to make the task harder and less predictable. We were not primarily interested in studying the effects of presentation duration and speed, as these have already been documented in the literature (e.g., Ke, Lam, Pai, & Spering 2013; Meyer, Lasker & Robinson, 1985; Schütz et al., 2010; Spering et al., 2011; Tychsen & Lisberger, 1986).  Observers were asked to track the ball and extrapolate its path after it had disappeared and to intercept its position after it had entered the designated “strike-zone”. The instruction was   23 to hit / catch the ball as fast and as accurately as possible after it had entered with the index finger of their right hand (see Figure 4). At the end of each trial, participants were given feedback on their interception error as indicated by a red dot relative to the visual stimulus position at time of interception. A “time out” feedback was given if the hand movement latency was too long (i.e. the subject did not intercept the screen until after the full trajectory would have been completed). Also, observers were reminded that they could take a break during the block if they needed by closing their eyes or looking away from the fixation point.   Figure 4 - Experimental trial sequence. Participants fixated on the ball and after successful fixation, the ball would begin to move. The ball would then disappear before crossing the midline and participants had to intercept the ball’s trajectory in the strike zone. Feedback of the ball’s actual position (black) and the finger position (red) was given at the end of each trial.   2.3.2 Procedure during Training Sessions  Whereas the control group did not come into the lab on training days, the three training sessions for the other two groups consisted of three blocks of 162 experimental trials each. The   24 procedure for training blocks was the same as for experimental trials before, but the instruction varied: In the eye training group, participants were instructed to track the ball with their eyes till the end of the trajectory – even after it disappeared – and not intercept with their finger. No feedback was given during these trials. In the eye-hand training group, participants were instructed in the same way as during pre- and post-test and feedback was given in the same way (see Figure 4).                                                   2.4 Data Preprocessing and Analysis 2.4.1 Eye Movement Recording and Analysis   Eye movements were recorded using a tower-mounted video-based eye tracker (Eyelink® 1000; SR Research Ltd., Osgoode, ON, Canada) set to a sampling frequency of 1000Hz. The Eyelink is a remote, video-based eye tracker that is unobtrusive. It requires use of a chin rest to stabilize head position for high-accuracy tracking (Figure 3). Seating position relative to the eye tracker was adjusted for each subject. The system reliably tracks observer’s infrared light reflections off the cornea, even with glasses or soft contact lenses. Data were stored offline for analysis and preprocessed using Matlab (Mathworks, Natick, MA). Eye velocity was obtained by differentiation of eye position signals over time and filtered using a low-pass, second-order Butterworth filter 15 Hz cut-off (position) and 30 Hz cut-off (velocity) in Matlab. In each trace, saccades were detected using customized criteria: 5 consecutive frames had to exceed a fixed velocity criterion of target speed ± 40°/s. Horizontal and vertical saccades were removed from the unfiltered traces and replaced by linear interpolation between saccade onset and offset. Precise on- and offsets (marked in green and magenta, respectively, in Figure 5) were then determined by finding the eye acceleration (digital differentiation of eye velocity) respective minima and maxima. Saccades were excluded from pursuit analysis. Pursuit onset (red cross in   25 Figure 5) was detected in individual traces using a piecewise linear function fit to the filtered position trace within a time window between 260 ms before stimulus motion onset and the first saccade or 80 ms after stimulus onset, whichever occurred earlier. An example eye trace is shown in Figure 5.  Figure 5 - Example eye position trace to indicate time intervals and eye movement analysis features. Note that pursuit onset was prior to target motion onset in this particular trial.  From these recordings, we assessed the open- and closed- loop phases of the pursuit response. The open-loop phase occurs during the first 140 ms after pursuit onset and we calculated pursuit latency and mean acceleration for this phase (see Table 2). Following the first 140 ms, we calculated closed-loop parameters gain and root-mean-square eye position error (Table 2). Saccade sum and peak eye velocity were calculated over the entirety of the trial from eye movement onset. All definitions for eye movement measures are found in Table 2.    26 Table 2 Definitions of Eye Movement Measures Measure Definition Latency Time it takes for the eye to begin to move after target stimulus onset (in ms).  Mean Acceleration Rate of change of velocity per unit of time. Measured in degrees per second^2 (deg/s2) and calculated across the open-loop phase (first 140 ms). Peak Velocity The highest rate of change of displacement per unit of time between eye movement onset and trial completion. Measured in degrees per second (deg/s). Gain Ratio of the observer’s eye velocity to the target velocity calculated across the closed-loop phase (140 ms after pursuit onset to end of trial). Position Error The absolute eye position error with respect to the target position at each point in time, averaged across the closed-loop in degrees (deg). Saccade Sum Number of saccades per trial multiplied by the total amplitude of all saccades (in deg).     Individual eye position traces were examined manually to check missed saccades, blinks or lost signals. The Matlab window created in the lab and used for this preliminary analysis with a representative trace is shown in Figure 6. From this window, decisions could be made about the quality of the data in terms of abnormalities or signal loss for both eye and hand movements. Traces that indicated that the participant blinked during the stimulus motion or the signal was lost during the trial were removed during manual inspection. Traces where pursuit onset was absent were not included in the analysis of the pursuit eye movement measures.      27  Figure 6 - Matlab analysis screen with representative trace. This window allows the experimenter to view the eye position trace (A), eye velocity trace (B), and finger interception (C).  A total of 6.5% of all trials across experiments and subjects were removed (8.5% for the eye training group, 4.9% for the eye-hand training group and 4.9% for the control group). 5.2% were removed from experimental trials (eye training group – 4.6%, eye-hand group – 6.1%, control group – 4.9%) and 7.3% were removed from training trials (eye training group – 10.1%, eye-hand training group – 4.5%).  2.4.2 Hand Movement Recording and Analysis  Hand movements were recorded using a 3D Guidance trakSTAR™ system (3D Guidance trakSTAR™, Ascension Technology Corp., Vermont, USA), which utilizes DC magnetic fields with a sampling rate of 240 Hz and has a root-mean-square accuracy of 1.4 mm. In order to track   28 the position of the index finger, a small, light-weight sensor was secured directly to the participant’s index finger via a Velcro® band and athletic glove sized to the participant’s hand (shown in Figure 7). We ensured that there were no cell phones or other electronic devices in the immediate proximity of the generator to avoid any potential interference (see Figure 3-F). Finger movements were then analyzed off-line using scripts in Matlab.   Figure 7 - trakSTAR™ finger magnet set-up (close-up of Figure 3-E). The small magnet is fastened onto the pointer finger of the right hand by a Velcro® strap and the wire is secured to the athletic glove to not interfere with ability of the participant to freely move their hand.     Participants were given instruction to intercept directly and quickly upon their conscious decision to intercept once they believed the ball was inside the strike zone. They were told to return their hand to neutral position after interception, which was defined as a stationary black mouse that was placed between the chin/forehead rest and translucent screen on the table. This was to ensure that each interception trial had the same starting and ending point, and thus the   29 measurements about hand movements could be subsequently compared. Figure 8 displays an observer making an interception.   Figure 8 - Participant making a manual interception on the translucent screen. Participants interacted with the screen in this manner when making their interceptive movements after target disappearance.    From these recordings, we were able to examine the finger position, finger movement time (the time it takes for the hand to reach the screen from the neutral position in ms) and latency of finger movement (time between target disappearance and finger movement onset in ms). 2D finger position was recorded in x- and y- screen centred coordinates. From this, we were able to determine the absolute finger position error measured in degrees relative to the trajectory once the participant had intercepted on the screen. Trials that did not have a recorded finger interception due to technical error or had a negative finger latency recorded, were removed through manual inspection of each trial (included in numbers listed in section 2.4.1).    30 2.5 Statistical Analysis  Prior to statistical analysis, outliers were identified by using standardized scores (z-scores) set to a standard deviation of 3σ in R; data points outside of the threshold were eliminated. The different statistical procedures were completed in SPSS (Version 22). A series of repeated-measures ANOVAs were used to determine main effects and interactions of experimental conditions (as within-subjects factors) and groups (as between-subjects factor). As well, ANCOVAs were completed controlling for the effects of pre-test mean performance on pre- and post-test differences with groups as a factor. Further, two-tailed t-tests were performed with an adjusted p value of 0.017 to correct for multiple comparisons, where appropriate. Lastly, Cohen’s d values were reported for effect size. Effect sizes are a robust reflection of the actual differences in results regardless of sample sizes. An effect size of 0.2 is considered small, 0.5 is medium, 0.8 and greater is large (Cohen, 1988). All graphs in the Results section have error bars representative of standard error. 2.6 Experimental Hypotheses Hypothesis One: The two training groups will have improvements in eye movements between the pre-test and post-test compared to the control group, which is expected to have no change in eye performance.   Comparisons for the eye measures (gain, peak velocity, eye position error and saccade              sum) will be completed for the pre-test and post-test results to determine differences in               performance by comparing effect sizes and post hoc t-tests. A series of ANCOVAs for  eye measures will be completed to control for the effects of pre-test mean performance  on pre- and post-test differences, using groups as a factor.    31 Hypothesis Two: Eye movement training transfers to hand movements and is sufficient to improve hand movements.   A pre-post comparison of interception performance between training groups will be              performed to assess whether the eye training group improves to the same extent as the              eye-hand training group by measuring effect sizes and post hoc t-tests comparisons. A  series of ANCOVAs for finger measures will be completed to control for the effects of  pre-test mean performance on pre- and post-test differences, using groups as a factor.  Hypothesis Three: Across days 2-4, both training groups show gradual improvements across eye measures. The eye-hand training group will also show gradual improvements with respect to hand movement measures.   By examining the eye and hand measures over training days 2-4, we can ascertain the              pattern of improvement.  Hypothesis Four: Training effects for both the eye and eye-hand training groups will persist or degrade slightly for both the eye and hand measures (considering their respective training) on the one-week follow-up. However, their performance will not decrease back to the level of pre-test performance.   A comparison of select eye and finger measures for the week follow-up to pre-test and              post-test performance will determine the sustainability of improvements over time by   post hoc t-tests comparisons between pre-test, post-test and week follow-up performance.      32 CHAPTER 3 - RESULTS  Testing the aforementioned hypotheses required different measures for eye and hand movements to be evaluated (refer to Table 2 and Section 2.4.2). Despite not being primarily interested in the effects of speed and presentation duration in this study, results for the pre-test across groups will first be reported to assess any main effects of speed and presentation to validate previously shown results using our paradigm. Next, averaged results across these conditions to conduct the main analyses to address the hypotheses will be reported.  3.1 Analysis of Eye and Hand Movement Performance during the Pre-Test  Mean eye and hand movement performance measures taken on day 1 (pre-test) were averaged across all 27 observers. Note that we averaged across observers because the pre-test procedures and instructions were identical across training groups.  3.1.1 Eye Movement Pre-Test Performance Figure 9 shows mean eye movement performance measures separately by speed and presentation duration. Whereas open-loop measures latency and mean acceleration were not significantly impacted by these target features (Table 3), closed-loop measures gain and position error as well as catch-up saccade sum and peak velocity were affected by speed and presentation duration as indicated by significant main effects in a speed X presentation duration repeated-measures ANOVA (Table 3).  Significant speed X presentation duration interactions were found for peak velocity, gain and position error, but not for latency, acceleration, and saccade sum.  Pursuit gain was best at the slowest speed and increased with presentation duration. Considering a mean pursuit latency of 96 ms across speeds for the shortest presentation duration the stimulus disappeared on average 4 ms after the eyes started moving, thus explaining the relatively low gain for short presentation durations. The saccade sum reflects this pattern of results: fewer   33 catch-up saccades of smaller amplitude were made at slower target speeds and with increasing presentation duration. Position error across the entire pursuit trace decreased with increasing presentation duration. The main effect of speed likely reflects a range effect (Poulton, 1975) with medium target speeds producing the lowest position error.   Figure 9 - Pre-test results for eye measures averaged across n=27. A: latency, B: open-loop mean eye acceleration, C: peak velocity, D: velocity gain, E: position error, F: saccade sum. Error bars in all panels depict S.E.M.  Table 3  Repeated-Measures ANOVAs for Eye Measures with Factors Speed and Presentation Duration  Speed Pres. Dur. Speed X Pres. Dur.  F(2,52) p value F(2,52) p value F(2,52) p value Latency 0.96 0.39 1.86 0.17 1.99 0.10 Mean Acceleration 0.43 0.65 1.09 0.34 0.19 0.94 Peak Velocity 0.91 0.41 16.24 <.001 3.69 .008 Velocity Gain 89.39 <.001 42.83 <.001 5.14 .001 Position Error 5.95 .005 214.14 <.001 9.99 <.001 Saccade Sum 28.20 <.001 45.26 <.001 1.72 0.15   34 3.1.2 Finger Movement Pre-Test Performance Hand movement performance measures were plotted across presentation durations and separated by speeds, as shown in Figure 10. Finger movement latency (reaction time) and movement time both increased significantly with decreasing speed (Table 4). This finding likely reflects the fact that decision time was shorter at faster stimulus speeds and participants thus needed to react faster in order to hit the target before a “time out”. Interestingly, pre-test means for finger position error show the same result pattern as eye position error, indicating a speed-range effect with best performance at medium speed. No speed X presentation duration interactions were found (Table 4).   Figure 10 - Pre-test results for finger measures averaged across n= 27 subjects. A: latency, B: movement, C: position error. Error bars in all panels are S.E.M.  Table 4  Repeated-Measures ANOVAs for Finger Measures with Factors Speed and Presentation Duration  Speed Pres. Dur. Speed X Pres. Dur.  F(2,52) p value F(2,52) p value F(2,52) p value Latency 11.55 <.001 0.08 0.92 0.71 0.59 Movement Time 9.66 <.001 4.51 0.02 0.81 0.52 Position Error 21.61 <.001 24.04 <.001 1.81 0.13   35 3.2 Analysis of Pre-Test & Post-Test Results  For all following analyses, data were averaged across speeds and presentation durations for two reasons: first, the main focus of our research was on effects of training, and not on effects of stimulus features (effects of speed and presentation duration are well known and have been reported in the literature; e.g., Ke, Lam, Pai, & Spering 2013; Meyer, Lasker & Robinson, 1985; Schütz et al., 2010; Spering et al., 2011; Tychsen & Lisberger, 1986). Second, we conducted an additional rmANOVA with within-subject factors speed and presentation duration and between-subjects factor group for differences between pre-test and post-test data. Most of the speed X group or presentation duration X group interactions were non-significant (results not shown; all p>.23) with the exception of significant speed X group interactions for saccade sum, p=.001, finger movement time, p=.03 and finger position error, p=.04. The following section compares eye and hand movement performance between the three different training groups (n=9 each).  3.2.1 Baseline Trial Comparisons  Sixty baseline trials were completed at the beginning of the pre-test and post-test sessions. As mentioned previously, baseline trials were those that the observer tracked the entire trajectory, without the stimulus disappearing. The data for four eye measures for pre-test and post-test are shown in Figure 11. In this figure, it is shown that subject’s are executing the task relatively well as evidenced by a low position error, a peak velocity that is close to target velocity and a decent velocity gain. Typically, velocity gain is close to one. However, due to the mentioned difficulty of this task, it is not surprising that the gain is below unity. Also from Figure 11, it is clear that performance during pre-test and post-test baseline trials are comparable.    36                   Figure 11 – Pre-test and post-test baseline trial comparison by group. A: peak eye velocity, B: velocity gain, C: eye position error, D: saccade sum. Error bars in all panels depict S.E.M.   3.2.2 Training Effects on Eye Movements In all measures, the improvements seen in the eye-hand training group are larger than those observed for the other two groups – which begins address the first hypothesis that the two training groups would improve more than the control group. Results (Figure 12) for four selected eye measures (peak velocity, velocity gain, position error and saccade sum) show the largest improvement for the eye-hand training group, a finding indicated by effect sizes. For example, as seen in Figure 12-C, though all groups improve on their position error as indicated by a lower value on the y-axis, the eye-hand training group improves with an effect size of d=0.562 compared to d=0.148 and d=0.290 of the control and training group, respectively. Interestingly, velocity gain shows the greatest improvement reflected in the largest effect size   37 (d=0.704). Accordingly, peak velocity improves for the eye-hand training group with a similar effect size (d=0.506). It can be concluded through this set of graphs, that the eye-hand training group improved the most on eye measures despite have the same exposure to the visual stimulus as the eye-training group. This would indicate the engagement of the hand is additive or helpful in training eye movements.   Figure 12 - Pre-test and post-test experimental results for eye measure comparisons by group. A: peak eye velocity, B: velocity gain, C: eye position error, D: saccade sum. Error bars in all panels depict S.E.M.     To further explore the pre- and post-test results, averaged eye velocity traces were drawn of one representative subject from the eye training and one from the eye-hand training group for illustration. The results are depicted in Figure 13 and show the subject’s eye velocity relative to the target velocity over time. Comparing the results from both testing days, it is clear that both   38 subjects have an increased eye velocity on the post-test. However, what is more striking is the improvement seen for Subject 37 (eye-hand training group). From Figure 12, it was clear that the eye-hand training group improved more both in peak velocity and in gain. Figure 13 is a pictorial representation to support this finding.   Figure 13 – Averaged eye velocity traces for pre-test and post-test of two subjects: Subject 6 – eye training group and Subject 37 – eye-hand training group.    A series of ANCOVAs were completed with groups as a factor, controlling for pre-test means on pre- and post-test performance differences for the eye measures (Table 5). Group as a factor did not afford any significant results after controlling for pre-test performance means for   39 any of the four eye measures examined. This is likely due to the close similarity in the results of the eye training and control groups, despite the eye-hand training group presenting differently.  Table 5 ANCOVAs for Eye Measures with Group as a Factor and Controlling for Pre-Test Means  Pre-Test Means Group  F(1,23) p value F(2,23) p value Peak Velocity 0.01 0.93 3.18 0.06 Velocity Gain 0.01 0.92 3.16 0.06 Position Error 1.60 0.22 1.08 0.36 Saccade Sum 0.94 0.342 1.58 0.23  3.2.3 Training Effects on Hand Movements  In the performance of the finger measures, we also see the most improvement in the eye-hand training group amongst the progress seen in all three groups (Figure 14).  Figure 14 - Pre-test and post-test experimental results for finger measure comparisons by group. A: latency, B: movement time, C: position error. Error bars in all panels depict S.E.M.  (***: p<0.001)  A series of ANCOVAs were completed with groups as a factor controlling for the effects of pre-test performance means on the pre-test and post-test differences for finger measures   40 (Table 6). A significant effect of group is shown with finger position error after controlling for the effects of pre-test means; F(2,23) = 5.23, p=0.01, meaning the differences in pre- and post-test performance observed between the groups was indeed due to the training intervention they were exposed to.  Table 6 ANCOVAs for Finger Measures with Group as a Factor and Controlling for Pre-Test Means  Pre-Test Means Group  F(1,23) p value F(2,23) p value Latency 0.58 0.46 1.12 0.35 Movement Time 0.01 0.91 2.63 0.09 Position Error 4.42 0.05 5.23 0.01  Much like the analysis of eye measures, effect sizes were calculated to consider the trends between the groups. The effect sizes seen for the eye-hand training group are the largest, with the most salient effect size coming from the improvement in finger position error of the eye-hand training group (d=0.741) being approximately three times larger than the eye-training group (d=0.256). There difference between the pre-test and post-test of the finger position error reached statistical significance (t = 2.68, p<0.001).  3.3 Analysis of Training Days and Week Follow-Up Comparisons   In this section, the last two hypotheses mentioned in Section 2.6 will be discussed. That is, addressing that gradual improvement in eye and hand measures over the training days, as well as the sustainability of results through to the week follow-up. The three parameters which had the greatest improvement in performance as indicated by effect size will be shown across the training days, as well as the results for the week follow-up for both the eye training group and the eye-hand training group. The results for the eye training group and eye-hand training group across all six sessions are depicted in Figure 15 and Figure 16, respectively. Two-tailed t-tests   41 were performed with multiple comparisons (with corrected alpha value of 0.017) between pre- and post-test, pre-test and week, and post-test and week for each group. Significant results are stated where applicable.   Figure 15 – Training results for the eye training group (n=9 for days 1-5; n=8 for week). A: velocity gain, B: eye position error, C: finger position error. Error bars in all panels depict S.E.M. (**: p< 0.01).             Figure 16 – Training results for the eye-hand training group (n=9 for days 1-5; n=4 for week). A: velocity gain, B: eye position error, C: finger position error. Error bars in all panels depict S.E.M. (**: p< 0.01; ***: p< 0.001).   42 The improvements seen in gain by the eye-hand training group are greater than those of the eye training group across all days (Figure 15-A and Figure 16-A). The eye training group experiences a reduction in gain quality throughout their training days due to being instructed to continue to track the stimulus after it had disappeared to the end of its trajectory. Despite this, when the hand is engaged in both post-test and the week follow-up, there are gradual improvements over these two days compared to the pre-test. This displays the propensity of eye improvement with hand participation. Third, the eye-hand training group demonstrates a generalized improvement over each day of training and the week follow-up. None of the comparisons reached statistical significance. Eye position error was examined across all six sessions (Figure 15-B and Figure 16-B). During the training days, the eye training group has a considerably larger position error. This is expected due to the need to track the entire trajectory despite the disappearance of the visual stimuli unlike the eye-hand training group which has the chance to intercept the trajectory before its completion, and therefore not needing to track for the same lengthy duration of time. However, for the eye training group, there is evidence of improvement in the post-test and week sessions. For the eye-hand training group, the improvements seen in eye position error begin to plateau during the training days and persist through the post-test. Nonetheless, we see further improvements in the week session.  For the eye-hand training group, results between pre-test and week test were significantly different (t = 2.86, p<0.01).   Finger position error is the only hand parameter discussed in this section. This measure is highly important in the analysis because it serves as the measure for interception accuracy improvement that was targeted in this study. The progression of improvement over time is depicted in Figure 15-C and Figure 16-C.    43 Both groups improve, but the eye-hand training group improves the most. The difference in experimental means by the eye-hand training group reaches significance between the pre-test and the post-test (t = 2.68, p<0.001). Both groups improve significantly between the pre-test and week follow up (t = 2.68, p<0.01 and t = 2.81, p<0.001 for eye and eye-hand training, respectively). The eye-hand training group improves gradually across the days and more than the eye training group at the post-test.                                     44 CHAPTER 4 – DISCUSSION  This thesis investigated whether eye movement training could improve manual interception accuracy and reports the following key findings: (1) manual interception performance improved most (by about twice as much) following a combined eye-hand training versus eye-movement training or no training. (2) Eye movements also improved more, when the hand was engaged during training. (3) Effects of eye-hand training on both eye and hand movement performance were more stable and longer lasting than effects of eye-movement training alone.  4.1 Main Findings 4.1.1 Combined Eye-Hand Training is the Most Beneficial for the Hand  This training study demonstrated that combined eye-hand training improved manual interception more than eye movement training or no training. The eye-hand training group improved in performance by roughly two times the amount of the other two groups. Namely, the eye-hand training group has up to a one-degree reduction in their finger position error. Further, looking at the pre-test and post-test results of all the finger parameters, the eye-hand training group improves the most on all three measures (finger movement time, finger latency, and finger position error) as we had anticipated. However, we did not expect that the control group would improve more than the eye training group on finger position error, let alone show any improvement at all. Though the differences in improvement are not large between the control group and the eye training group, we predicted that the eye training group would improve more than the control group due to the eye training and exposure to the visual element of the task on days 2-4. Consequently, not even exposure to the task on days 2-4 for the eye training group helped accelerate or solidify the training process. Given the results from this study, it can be seen   45 that providing training to the eye movements alone did not translate into more accurate manual interception.  Though there are differential effects of training amongst the three groups, a relationship between the three finger parameters materialized. As finger latency decreases with training, finger movement time increases. Participants are making the decision to intercept earlier by moving their hand sooner after stimulus onset, but they are increasing the time it takes their hand to reach the screen and intercept. It appears that this relationship between these two parameters facilitates more accurate manual interception, as evidenced by a globalized improvement in these measures for the eye-hand training group. Unpublished work in our lab has shown that participants who intercept earlier tend to have better interception accuracy (Fooken, Yeo, Pai & Spering, 2014). This is likely due to there being less spatial uncertainty since they intercept sooner after disappearance. On a very preliminary level, the results from this training experiment seem to be in line with that finding.  4.1.2 Combined Eye-Hand Training is Most Beneficial for the Eye  When comparing the eye data from both the eye training and control groups to the eye-hand training group, it is clear that engaging the hand helps activate improvements in the eye measures. This finding is particularly noteworthy because the eye training group and the eye-hand training group involved the same degree of eye-movement training, but eye movements improved only if combined with engaging the hand during training. The control group serves as a baseline for comparison and the eye training group looks very similar to the control group in their pre-test and post-test results; it becomes evident that the eye training did not have much impact on the eye measures. Furthermore, previous studies have shown that adding a hand-tracking component to a task can improve the accuracy of eye movements (Gauthier, Vercher,   46 Ivaldi, & Marchetti, 1988; Koken & Erkelens, 1992). This relationship will be detailed additionally below.    Eye movements (particularly gain and position error) improved most when the eye and hand were trained together, and it is this simultaneous engagement of the hand that enhances training effects on the eye movements. These results are in line with findings that simultaneous pointing movements enhance saccades (Lünenburger, Kutz, & Hoffman, 2000; Snyder, Calton, Dickinson, & Lawrence, 2002; Kattoulas et al. 2008; Lee, Poizner, Corcos, & Henriques, 2013; Scherberger, Goodale, & Andersen, 2003).   From this training task, certain eye parameters can be trained more than others and equally, certain eye parameters are more related to improvements in finger movements. Those eye measures that allow the participant to have a more accurate temporal and spatial representation of the disappearing target (such as gain and position error) are more likely to improve when the hand is engaged. If your eyes are keeping up with the target velocity (higher gain) and you are following the target’s trajectory with more spatial accuracy (lower position error), the likelihood of your hand reaching closer to the actual target stimulus would presumably increase. It is crucial in this task to not only discern the spatial components of the trajectory, but to account for the temporal components as well. If an observer only accounts for one of these two measures, there will be systematic errors in their judgments. With that being said, there may be a potential cost in the visuo-motor system to train all eye parameters that are not directly facilitating in improving hand movements. This could potentially explain why measures, such as saccade sum, do not evolve to the same extent with either training protocol. Thus, it may be important to design experiments that would selectively exploit the training of eye parameters that promote spatial and temporal accuracy.    47  The finding that smooth pursuit accuracy (particularly gain) improves most following combined eye-hand training might initially seem unexpected. While the relationship – eye movements affect hand movements – is relatively well established (e.g., Johansson, Westling, Bäckström & Flanagan, 2001), there is less information about effects in the opposite direction. However, previous behavioural studies in humans and monkeys and neurophysiological studies in monkeys have demonstrated similar effects for saccades and pointing (Lünenburger, Kutz, & Hoffman, 2000; Snyder, Calton, Dickinson, & Lawrence, 2002; Kattoulas et al. 2008; Lee, Poizner, Corcos, & Henriques, 2013; Scherberger, Goodale, & Andersen, 2003). For instance, Kattoulas and colleagues (2008) trained macaque monkeys to perform a simultaneous saccade and reaching task and found that mean eye position errors were significantly lower when the saccade was accompanied by a hand movement versus when it was carried out in isolation. Synder et al. (2002) required three macaque monkeys to perform saccadic eye movements to different targets in the periphery, with or without the involvement of an arm movement to the target. The authors found that mean saccade duration decreased and peak saccade velocity increased with the presence of an accompanied arm movement. In a recent study, Lee et al. (2013) assessed the relationship between saccades and reaching using a paradigm in which observers had to either move their eyes, their hand, or both their eyes and their hand to static or dynamic targets in 3D space. Even though the authors observed a close coupling between eye and hand, the results were different to those reported in the literature: in the wake of a reach movement, saccade peak velocity was reduced and saccade endpoint variability increased. Regardless of the direction of the effect, these studies together indicate that reaching or pointing movements can strongly influence saccades. The results of this thesis extend these findings to smooth pursuit eye movements.   48  The neuronal control for some types of eye movements (such as saccades, pursuit) and hand movements is tightly coupled (for a review see Prablanc, Desmurget & Gréa, 2003). For instance, target selection for eye and hand movements is coded in superior colliculus (for a review, see Krauzlis, Liston, & Carello, 2004; Kim & Basso, 2008; Shen & Paré, 2014; Gardner & Lisberger, 2010; Song & McPeek, 2015). Other regions that have been implicated in the coupling of eye and hand movements are the occipitotemporal region, e.g., lateral occipital cortex (LOC) and fronto-parietal cortex, e.g., frontal eye fields (FEF) (e.g., Tibber et al. 2010). 4.1.3 Time-Course of Training Effects  Due to the connection of gain and eye position error with manual interception accuracy, these two measures along with finger position error were chosen to be examined across all five training days and the week follow-up to address our hypothesis concerning the sustainability and maintenance of training effects. It is important to consider what is happening to the eye and finger measures over the training days, and not just relying on the information from the contrast between pre-test and post-test results if we want to understand the acquisition of training or skills. As was shown for the eye-hand training group, gain roughly improves continuously across all five days and the progression of improvement extends to the week follow-up for the eye-hand training group. The eye-training group does not increase their gain with only the visual stimulus training on days 2-4 and instead we see a slight decrement due to the challenging nature of the task on these days. This provides great evidence and support for the idea that enlisting the hand in the training protocol increases or enables the eye movements to progress; and not only to progress, but to progress more quickly. Also, this supports much evidence in the research literature that eye movements exhibit plasticity and can be manipulated with training, like many other motor skill or movements (Nelles et al., 2009; Adkins, Boychuck, Remple, & Kleim,   49 2006). Along the same lines, we see considerable improvement in the eye position error. Both the eye and eye-hand training groups improve, but characteristically, the eye-hand training group improves more. The eye-hand training improves on the first two days of training and then reaches a plateau that endures through the post-test. Nevertheless, there is further improvement in eye position accuracy on the week follow-up. Interestingly, despite the eye training group having a large position error on the training days because they need to track a disappearing stimulus, they continue to improve from their pre-test scores on the post-test and week follow-up even with this poor performance during the three days. Moreover, this provides added support that engaging the hand is key to see improvements in these eye parameters discussed above. The addition of the hand movement may provide an extra incentive for the producing more accurate and precise eye movements, allowing for synergistic and streamlined sensory and motor responses. This is different from our original hypothesis that improvements acquired across days 1-5 would persist or decay; to our delight, they actually continue to improve even after a week. Lastly, examining finger position error, the eye-hand training group shows improvement and stabilization through to the week follow-up. These long-lasting effects are critical in training experiments if there is to be any practical applicability or rehabilitative purpose  Throughout all the analyses of the data and literature review it is clear: when the hand is engaged, the potential to improve both eye and finger measures increases. Taken together, these results have implications for our understanding of the brain pathways underlying the control of eye and hand movements. The more accurate the eye movements, the more accurate the hand movements – and likely the opposite.      50 4.2 Limitations and Future Experiments   The results of this thesis indicate a difference between training the eye in isolation, and training the eye and hand combined. However, an important difference between these two training conditions is that feedback was given only in the eye-hand training group. An important additional study to perform would be an eye training study in which feedback is provided during training. This feedback could either be a standardized feedback, such as briefly showing an image of the entire trajectory at the end of each trial, or an individualized feedback indicating the final eye position of the subject (instructions could be adjusted, observers could be asked to saccade to “intercept”). This latter experiment would require real-time eye position analysis to provide instantaneous feedback, which would require more advanced computer programming than the current experimental set-up. Ideally, if both of these experiments could be run, we could determine if it is the training of eye movements (the first option) or the act of intercepting (the second option) that affects the eye and hand measures. Given that Goodale (2011) discusses that there could be differences between “vision-for-perception” and “vision-for-action”, these two experiments would also address that claim due to one experimental design being passive and the other active. Even the results from the current experiment can be thought from this vantage point and perhaps this is part of the differential training effects. Other literature, such as in motor learning, shows the need for an error signal to provide feedback to guide improvements (Seidler, Kwak, Fling, & Bernard, 2013; Deveau, Ozer, & Seitz, 2014). In order to parse out the influence of feedback, an experiment would need to be designed to include this parameter.  Another experiment that could be run to complement fixation and pursuit in perception experiments (Spering et al., 2011) would be a design that would require subjects to fixate at points on the screen and the subject needs to intercept where they think the ball would be (as in   51 our current set-up). This would hopefully strengthen the literature that perception of visual motion is increased during smooth pursuit than during fixation. It would also add another layer to this literature due to the engagement of a hand movement, not just a perceptual judgment indicated by a response on a keyboard. Further, this experiment could address whether performance improvements are due to eye movements or perception.  4.3 Practical Implications 4.3.1 Sports Training  In sports, it is important to perfect skills and athletic performance. The investigation of elementary training protocols could benefit athletes in the future. This study addressed the question whether training eye movements can impact hand movements, which would be imperative to all ball sports involving hand movement interceptions, such as baseball, cricket and tennis. The results of this thesis indicate that when trying to achieve a skill (i.e. accurate manual interception), it must not be broken down into its requisite parts (i.e. both eye and hand movement need to be involved). This would mean that for sports, it is important to practice the skill of batting to reciprocally improve eye movement and hand movement accuracy together, not just watching a ball be thrown or swinging a bat. Engaging the hand allows one to strengthen the other for continual improvement. Moreover, Gibson (1979) discussed the idea that movement is imperative for perception and the opposite was also necessary – perception-action coupling. Sports research has continued to investigate this coupling and have concluded that they are bound together in many motor tasks, such as an aiming task (Paterson, van der Kamp, Bressan, & Savelsbergh, 2013; Warren, 1990). Interestingly, smooth pursuit eye movements can also be modified by athletic experiences. Von Lassberg, Beykirch, Campos, & Krug (2012) showed that regular gymnastics training significantly increases smooth pursuit performance over time and   52 that the patterns of smooth pursuit movements change as well. This contributes to the support that motor engagement has the ability to enhance eye movement quality, which would in turn help the motor movement.   Many studies have shown that we can improve vision, but many of the effects acquired are not transferable into practical application, such as in a sporting environment. Deveau, Ozer, and Seitz (2014) used a novel integrative perceptual learning approach that trained with an assortment of stimuli for the University of California Riverside baseball team. After practicing these multisensory perceptual learning tasks, the team showed improvements in their batting statistics – indicating a transfer to a real-world situation. They suggest that one way to see transfer from specific training is to use an integrated and various methods approach. This is important when we want to design eye and hand movement training protocols to enhance athletic performance. Much like needing to train the eye and hand together, as indicated by results from our study, in order to see practical effects it is necessary to train all the fundamental skills. Nevertheless, a basic training paradigm such as the one used in this thesis potentially provides a critical component of a greater training protocol.  4.3.2 Clinical Rehabilitation  Improving visual-motor functions is highly important for a large variety of diseases, such as movement disorders (e.g., Parkinson’s disease) or visual-motor dysfunction following injury or trauma (e.g., stroke). Indeed, a recent study showed that 68% of a large sample of stroke patients had abnormal eye movements, including smooth pursuit deficits (Rowe et al., 2009). Thus, involving eye movements in movement rehabilitation training could be beneficial. In this current study, we demonstrate that eye movements and hand movements can be improved   53 through training with long-lasting stable effects, which could potentially address the reduced function of these patients with adapted protocols to accommodate other motor complications.    Using a training study, such as the one presented in this thesis, forms an exploratory basis for understanding how to train these movements with healthy controls, in hopes of seeing similar benefits in patient populations. Perhaps it is important to train hand movements in these populations in order to see an impact in eye movements, which would hopefully translate to daily life. The information gathered from basic training studies may ultimately lead to the development of new clinical rehabilitation techniques incorporating vision and eye movements. A better understanding of the mechanisms underlying normal human vision and eye movement can advance current protocols, enhance technologies and improve quality of life for those whom are struggling with disease or hardship. 4.4 Conclusions  Our original hypotheses collectively implied that the eye leads the hand. 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