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Peripheral oculomotor training in individuals with healthy visual systems: Effects of training and training transfer
Vision Research 133 (2017) 95–99
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Vision Research journal homepage: www.elsevier.com/locate/visres
Peripheral oculomotor training in individuals with healthy visual systems: Effects of training and training transfer Dylan Rose ⇑, Peter Bex Northeastern University, Psychology Department, United States
a r t i c l e
i n f o
Article history: Received 20 January 2016 Received in revised form 6 October 2016 Accepted 8 October 2016
Keywords: Eye movements Training Preferred retinal locus Central visual field loss
a b s t r a c t Individuals with pathological or simulated central visual field loss can be trained to use a preferred retinal locus (PRL) as a substitute for their non-functioning fovea. The functional benefits of a stable PRL are well documented, but little is known about oculomotor adaptations during PRL acquisition or transfer of training to another location in response to real or simulated disease progression. In this study, eight normally-sighted observers were trained to use a pseudo-PRL (pPRL) at one of two locations by guiding an eccentrically placed, gaze-contingent ring over a fixation target. The pPRL location was 6.4 degrees in either inferior or right visual field, balanced across observers. Training was completed in two sessions of 200 hundred trials separated by a week. Between sessions, the pPRL position was switched. Task performance was quantified both in terms of gaze stability around the fixation target and gaze accuracy in terms of distance between the target and ring centers. The latter was used to provide feedback by covarying the diameter of the ring to make the task easier or harder on the basis of subject performance. Accuracy and stability significantly increased with training and was comparable at each trained location. Performance gains were retained over a week and transferred from the first to the second pPRL location. Thus, pPRL training with feedback can provide sustained, generalizable improvements in oculomotor control following simulated foveal vision loss. These results suggest that low vision rehabilitation specialists may prioritize PRL training locations based on sensory function alone, since oculomotor gains are relatively uniform; and that training early in the disease process may benefit later adaptations should eye disease progress. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Individuals with retinal diseases such as age-related macular degeneration (AMD) that damage or destroy the fovea may develop one or more preferred retinal loci (PRL) as a compensatory adaptation to loss of vision in the center of the visual field (Fletcher and Schuchard, 1997; Von and Mackensen, 1962; Whittaker et al., 1988; White and Bedell, 1990; Whittaker et al., 1991). Over the course of weeks to months, either a single general-purpose PRL (Fletcher and Schuchard, 1997; Von and Mackensen, 1962; Whittaker et al., 1988) or a series of often task-specific PRLs (Timberlake et al., 1986) may be adopted. However, in spite of the substantial literature documenting the existence of PRLs, relatively little is known about the processes by which they are formed and whether formation at certain retinal locations may lead to superior performance on visual tasks relative to others. ⇑ Corresponding author. E-mail addresses: [email protected] (D. Rose), [email protected] (P. Bex). http://dx.doi.org/10.1016/j.visres.2016.10.016 0042-6989/Ó 2017 Elsevier Ltd. All rights reserved.
It has been demonstrated that the formation of a PRL is associated with improved functional vision outcomes in tasks such as reading (Crossland et al., 2004; Seiple et al., 2005; Palmer et al., 2010). Low-vision rehabilitation specialists therefore may train a PRL in patients with pathological central vision loss (Schuchard, 2005; Watson et al., 2006). However, few studies have specifically examined changes in oculomotor control during the acquisition of a PRL (Crossland et al., 2004). Understanding these processes is important, as many visual tasks require precise eye movement control, and its loss may impair visual function independently of retinal pathologies that directly affect sensory performance. This idea is supported by converging lines of evidence indicating both that fovea loss is associated with changes in oculomotor control (Bullimore and Bailey, 1995; Crossland et al., 2004; Schuchard, 2005) and that even at supposedly spared retinal locations at the same eccentricities, individuals with central field loss perform many tasks less well than individuals with healthy vision (McMahon et al., 1991; Mcmahon et al., 1993Mcmahon et al., 1993; Timberlake et al., 1986).
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One of the reasons for the relatively limited research in this area may be that there are substantial technical and methodological challenges associated with collecting eye movement data from patients with retinal disease. Patient populations express highly variable symptom profiles, making it difficult to obtain homogeneous samples of subjects for experimental purposes (Bowers and Reid, 1997). Fixational instability that accompanies fovea loss makes calibration of eye tracking systems on patients difficult. One method used to overcome these problems is to simulate central scotomas in individuals with healthy vision using gaze contingent stimuli on computer displays (e.g. Aguilar and Castet, 2011; McIlreavy et al., 2012). These simulations have numerous limitations, but if carefully controlled, can provide good approximations of the perceptual and behavioral consequences of visual impairments (Bowers and Reid, 1997). Simulations also facilitate the use of statistically powerful within-subjects/cross-over experimental designs, which would be difficult or impossible to implement among patients. A small but growing literature exists suggesting that over the course of several hours of explicit training using simulated scotomas, individuals with healthy retinas can also form a ‘‘PRLLike” region (Kwon et al., 2013; Varsori et al., 2004; Walsh and Liu, 2014). We term this a ‘‘pseudo-PRL” (pPRL) because it is functionally similar to a true PRL but is not a product of a disease processes. The possibility of training pPRLs means that it may be possible to study oculomotor control during PRL development without invoking the difficulties associated with tracking the eye movements of patients with central scotomas. We therefore used a pPRL induction paradigm to ask the following questions: Does pPRL training reduce fixational stability and the magnitude of oculomotor deviations or errors around a target? Such changes are thought to be an important part of the process of adaptation to retinal disease (Crossland et al., 2004), and are considered an important objective in rehabilitation programs as well (Mandelcorn et al., 2013). These findings further point to the important role that both implicit (natural process of response to disease progression in unmanaged retinal disease) and explicit (direction through rehabilitation training) feedback plays in the stabilization of a PRL. Explicit feedback in particular has been shown to improve treatment outcomes during visual rehabilitation training in response closs (Contestabile et al., 2002; Hall and Ciuffreda, 2001Hall and Ciuffreda, 2001; Pusswald et al., 2013; Vingolo et al., 2007). To our knowledge, however, there is little available data on changes to oculomotor control as a function of the implicit time-course of training or practice at a PRL or pPRL site (though see Kwon et al., 2013; Varsori et al., 2004; Walsh and Liu, 2014 for some discussion of this issue). Are oculomotor control changes associated with pPRL development affected by meridional performance differences across the retina? Although it is generally understood that functional vision and oculomotor control performance fall as a function of increasing retinal eccentricity, there is debate regarding the merits of selecting a PRL at specific retinal orientations relative to the fovea. There are well-documented meridional asymmetries in visual function such as acuity and contrast sensitivity (Skrandies, 1987), attentional resolution (Rezec and Dobkins, 2004) and the volitional control of ‘‘sustained” attention (Alpeter et al., 2000; MacKeben, 1999MacKeben, 1999), chromatic sensitivity (Levine and McAnany, 2005), motion sensitivity (Edwards and Badcock, 1993; Levine and McAnany, 2005), and crowding (He et al., 1996). Is pPRL training retained across time and transferred across locations? Many retinal diseases progress over time and therefore a trained PRL may eventually be claimed by an advancing
lesion (Nilsson et al., 1998). We therefore examine whether effects of pPRL training at one location are ‘‘carried over” to subsequent training at a different location. It is important to note that while these questions and the methods we have chosen to address them are strongly informed by current research on pPRL induction, our approach differs in one important respect. Specifically, because we wished to test whether training transfers between locations, subjects were not permitted toselect the pPRL site for themselves. This is undoubtedly a key feature of the development of true PRL, and thus an important component of a realistic simulation of the same process with pPRL. However, it would be difficult or impossible to have subjects spontaneously select pPRL locations that were equally eccentric but in different locations, or to maintain a constant distance between trained locations.
2. Materials & methods Eye movement data for this project were collected using an SR Research Eyelink 1000 infrared eye tracking system. Stimuli were presented on a 68.58 cm diagonal width ASUS VG278He monitor running at a 144 Hz refresh rate. Subjects were seated at a distance of 55 cm from the display, which therefore subtended a 54° horizontal visual angle. Subjects’ heads were stabilized during the experiment using an Eyelink-supplied chin and forehead rest. Stimuli were generated, displayed, and modified in real time on the basis of input from the Eyelink through the use of the MATLAB Psychtoolbox (Brainard, 1997) and Eyelink Toolbox (Cornelissen et al., 2002). Data were sampled at a rate set to match the refresh rate of the monitor. Subjects’ gaze profile was calibrated to the display using the standard nine-point calibration protocol provided with the Eyelink before each experimental session began. Eight participants (six women, two men) with normal or corrected-to-normal vision were recruited from the authors’ laboratory and from the undergraduate population at Northeastern University. Undergraduates received course credit towards the completion of their introductory psychology course in exchange for their participation. All were naive to the purposes of the study at intake and indicated their willingness to participate by signing an informed consent document associated with a protocol approved by the University Ethics Board. The Northeastern University Ethics Board evaluated the protocol and confirmed that this research adhered to the tenets of the Declaration of Helsinki. Participants completed a total of 400 training trials, split into two training sessions of 200 trials each. Sessions were separated by a period of roughly one week (mean 8.6 days, sd 3.2 days). Within a session, each subject completed four blocks of 50 trials. Between blocks, they were asked to rest for as long as they felt they needed. All were then re-calibrated using the same nine-point calibration procedure before continuing the experiment. Trials lasted for fifteen seconds. During a trial, subjects were asked to center a gaze contingent ring over a motionless fixation target and to maintain that position for as long as they were able (see Fig. 1 for a schematic representation of the task). The fixation target was a full-contrast red (RGB: [255,0,0]) circle with a diameter of 24 pixels. The line forming the gaze contingent ring was three pixels wide and drawn as a full contrast green (RGB: [0,255,0]). Trials fell into one of two orientation conditions: ‘‘east” or ‘‘south”. In the east, the ring was drawn 6.4° (128 pixels) to the right, and in the south 6.4° below, the foveated point of regard. Each session contained trials associated with one condition. At the beginning of the second training session, subjects switched conditions. The order in which they were completed was counterbalanced between subjects.
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Subject GM was excluded from all analyses of stability data as raw gaze position data was not collected for them. Average byblock, across-subject performance for both outcome measures is presented in Fig. 3a and b. In both, points are mean performance across subjects and trials within a block of training; error bars are fitted 95% confidence intervals. In both cases, a similar pattern of performance emerged across time: rapid improvement between the first and second blocks of training, then a leveling off toward the end of the first training session. At the beginning of the second
Average Accuracy (Log Pixels)
Within a trial, if subjects remained ‘‘on target” over the fixation target for a period of 100 ms, the diameter of the ring was reduced by a factor of 1.18, making the task more difficult. Similarly, if they fell ‘‘off target”during the same period, its diameter was increased to an equivalent degree, making the task easier. Finally, they were prevented from using their fovea to assist them by removing the fixation target from the screen if their foveated point of regard fell within a 2° radius of the target center. This approxmiated an absolute central scotoma of the same size. Performance on the task was measured in two ways. First, gaze ‘‘accuracy” was assessed in terms of the within-trial median logtransformed (natural log or base e) euclidean distance between the center of the ring and the center of the target. Second, their gaze ‘‘stability” with respect to the target was assessed in terms of the area within a fitted isocontour containing 95% of the gaze points within a trial (See Castet and Crossland, 2012 for a full description of this method). Fig. 2 is a schematic representation of the two performance measures and their relationship to each other. Data from this experiment were analysed using the R statistical programming language. First, hierarchical linear models (HLMs) were fitted to both the stability and error data. The structure of both models included fixed effects terms for the total block number (one to eight), orientation condition, and an interaction term between orientation condition and whether it was the first or second training session. Both also included by-subject random effects and slopes to capture potential individual differences in both baseline performance and rate of learning. This approach also allowed us to control significant multi-collinearities in the data at a number of scales resulting from the repeated-measures design of the experiment and correlations between sample-to-sample deviations from the target. Second, to assess the degree to which effects of training survived the inter-set period and transferred to the new training location, we used paired t-tests comparing subjects’ performance in both outcome measures between the first block of the first training session (block 1) and the first block of the second (block 5).
3. Results
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Fig. 1. Subjects completed a training session composed of 200 trials in one orientation condition, then completed a second session in the other approximately a week later. Keeping the ring on target caused it to contract after 100 ms, making the task harder. Falling off target during the same period caused it to expand, making the task easier. The green ring is the gaze contingent ring; the red circle represents the fixation target. Recorded foveal position (yellow circle) was not shown to research participants. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 2. The distance between the center of the red target dot and the centroid of the cluster of gaze points (blue line) is the subject’s gaze ‘‘accuracy” with respect to the target. The region highlighted in green is a fitted isocontour that contains 95% of the available gaze points(yellow circles), representing a subject’s gaze ‘‘stability” to the target. Data are simulated and presented in normalized, arbitray units. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Absolute Block Number Fig. 3. a: mean log-transformed median distance (pixels) between the centers of the target and ring as a function of training block number across subjects and trials. Dots represent within block means, and bars 95% confidence intervals. The dashed line between blocks 4 and 5 on the x-axis is the point at which subjects switched orientation conditions. b: mean stability performance (pixels-squared) within blocks and across subjects. The elements of this figure are otherwise the same as in a.
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session, both stability and accuracy performance fell sharply, then ultimately improved to levels beyond that achieved during the first session of training. In the fitted model for the error data, only the absolute block number term attained statistical significance (coefficient value: -.122, p < 0.01). This suggests that the training was associated with significant improvements in fixational accuracy at the pPRL site. The parameter associated with training block number indicates that after completing all eight blocks, the magnitude of subjects’ errors with respect to the target fell by a factor of nearly one natural log unit, i.e. an almost threefold (2.7) improvement. For the stabillity data, the absolute block number was also the only paramater that reached statistical significance (coefficient value: 18,192.570, p < 0.01), indicating that stability also significantly improved as a function of training. Fig. 4 presents means and 95% confidence intervals across subjects and trials between blocks one and five. A two-tailed paired ttest comparing accuracy performance between these blocks produced a significant result (df ¼ 398; t ¼ 4:51; p < 0:001); stability performance between them was not significantly different (df ¼ 349; t ¼ 0:4654; p < 0:001). This suggests that stability performance gains may be less resistant to decay over time than accuracy performance, though recovery and improvement appears to resume quickly starting in the third block of the second training session (block 7). 4. Discussion We have demonstrated that explicit training at an induced pPRL location can significantly improve both fixation stability and accuracy with respect to a stable target. A paired t-test comparing performance between the end of the first and the beginning of the second training session suggested that training benefits to accuracy persist over at least a week (and those for stability are quickly recovered and improved upon after the beginning of the second session). Both stability and accuracy gains also appeared to be ultimately robust to orientation changes of up to 90°. These findings point to a possible dissociation between oculomotor control and functional vision maps. Assuming similar results are obtained in patients with pathological central vision loss, they could have important implications for the development of our method as a training program to assist the visually impaired. If
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Fig. 4. Means and 95% confidence intervals for outcome measures (accuracy in red, stability in blue) between training block 1 (the first block of the first session) and training block 5 (the first block of the second training session). Data in this figure have been transformed to z-scores (mean zero, standard deviation of one) in order to set them on a shared scale. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the center of the oculomotor control map – and thus the site of the most precise eye movement control – can effectively be shifted through training in any direction away from the fovea, then there may be little or no penalty in terms of oculomotor components of visual function associated with training a PRL at a particular orientation relative to some other. This would greatly simplify the decision concerning where to train a PRL in clinical populations, and suggests that for oculomotor control, equivalent rehabilitation outcomes may be achieved at different orientations. As discussed above, meridional asymmetries favor the lower visual field for acuity and contrast sensitivity (Skrandies, 1987), attentional resolution (Rezec and Dobkins, 2004), chromatic sensitivity (Levine and McAnany, 2005), motion sensitivity (Edwards and Badcock, 1993; Levine and McAnany, 2005), and crowding (He et al., 1996) (for review see Alpeter et al., 2000; MacKeben, 1999). Our results therefore suggest that the decision about where to train a PRL might depend primarily on meridional sensory factors, with little or no advantage to particular orientations for oculomotor control. This work is preliminary and therefore subject to several qualifications. First, we investigated training effects at only two orientations relative to the fovea: below and to the right. Although we assume that any meridional effects are symmetric along one axis above or below the horizontal midline or to the left and right of the vertical, it is possible that this assumption would not hold. Additional studies with changes made between randomly selected pairs of orientations on the cardinal axes relative to the fovea, as well as between locations not on the cardinal axes, are required to fully generalize our findings. We have also only examined the effect of our training program in terms of performance in the simplest oculomotor control scenario: maintaining a stable fixation on a highly-visible, nonmoving, eccentrically-positioned target. It is unknown whether they extend to more complicated oculomotor tasks such as making saccades or pursuit. Nevertheless, improvements in fixation stability in the peripheral visual field are associated with functional benefits in patients (Crossland et al., 2004; McMahon et al., 1991) and healthy controls (Falkenberg et al., 2007). It is therefore possible that improvements in fixation stability we report should be accompanied by improvements in visual function. Nevertheless, it is important to also acknowledge that for tasks such as reading, outcomes at different retinal orientations may be constrained by additional factors such as the extent of the perceptual span in different directions relative to the trained pPRL location (McConkie and Rayner, 1976b, 1976a). The relationship between oculomotor and sensory performance is complex, and any explicit claims regarding the impact of this training program on one as a function of the other require further explicit testing. Because this study was performed using subjects with healthy vision and a simulated scotoma, it remains to be seen whether our findings would extend to training in the context of pathological retinal scotomas. Although our method may have applications outside the treatment of visual disease, it is obviously of substantially less value if it does not accurately reflect to at least a limited degree the experience of those with retinal disease. We are currently developing tools to facilitate eye tracking, and thus this program of research on those with real visual impairment (see Wiecek et al., 2015). Finally, additional testing may also be required to establish whether improvements associated with the training program reach some asymptote as a function of time, as the time and testing burden associated with training are significant, and it is possible that fixational stability during later trials may be unfairly reduced as result of fatigue. Although the effects of training we report are substantial, we did not collect data to compare subjects’ performance in the eccentric viewing conditions to their foveal perfor-
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mance. While it is highly unlikely that our training method could help participants achieve fovea-like levels of fixational stability, an accurate estimate of the rate of approach to this value would help to establish the point at which training gains are outweighed by increasing participant burdens. References Aguilar, C., & Castet, E. (2011). Gaze-contingent simulation of retinopathy: Some potential pitfalls and remedies. Vision Research, 51, 997–1012. Alpeter, E., MacKeben, M., & Trauzettel-Klosinski, S. (2000). The importance of sustained attention for patients with maculopathies. Vision Research, 40, 1539–1547. Bowers, A., & Reid, V. (1997). Eye movements and reading with simulated visual impairment. Ophthalmic and Physiological Optics, 17, 392–402. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436. Bullimore, M. A., & Bailey, I. L. (1995). Reading and eye movements in age-related maculopathy. Optometry & Vision Science, 72, 125–138. Castet, E., & Crossland, M. (2012). Quantifying eye stability during a fixation task: A review of definitions and methods. Seeing and Perceiving, 25, 449–469. Contestabile, M., Recupero, S., Palladino, D., De Stefanis, M., Abdolrahimzadeh, S., Suppressa, F., & Gabrieli, C. B. (2002). A new method of biofeedback in the management of low vision. Eye, 16, 472–480. Cornelissen, F. W., Peters, E. M., & Palmer, J. (2002). The eyelink toolbox: Eye tracking with matlab and the psychophysics toolbox. Behavior Research Methods, Instruments, & Computers, 34, 613–617. Crossland, M. D., Culham, L. E., & Rubin, G. S. (2004). Fixation stability and reading speed in patients with newly developed macular disease. Ophthalmic and Physiological Optics, 24, 327–333. Edwards, M., & Badcock, D. R. (1993). Asymmetries in the sensitivity to motion in depth: A centripetal bias. Perception, 22, 1013. 1013. Falkenberg, H. K., Rubin, G. S., & Bex, P. J. (2007). Acuity, crowding, reading and fixation stability. Vision Research, 47, 126–135. Fletcher, D. C., & Schuchard, R. A. (1997). Preferred retinal loci relationship to macular scotomas in a low-vision population. Ophthalmology, 104, 632–638. Hall, E., & Ciuffreda, K. (2001). Eccentric viewing training in macular degeneration using auditory ocular motor biofeedback. Journal of Behavioral Optometry, 12, 87–93. He, S., Cavanagh, P., & Intriligator, J. (1996). Attentional resolution and the locus of visual awareness. Nature, 383, 334–337. Kwon, M., Nandy, A. S., & Tjan, B. S. (2013). Rapid and persistent adaptability of human oculomotor control in response to simulated central vision loss. Current Biology, 23, 1663–1669. Levine, M. W., & McAnany, J. J. (2005). The relative capabilities of the upper and lower visual hemifields. Vision Research, 45, 2820–2830. MacKeben, M. (1999). Sustained focal attention and peripheral letter recognition. Spatial Vision, 12, 51–71. Mandelcorn, M. S., Podbielski, D. W., & Mandelcorn, E. D. (2013). Fixation stability as a goal in the treatment of macular disease. Canadian Journal of Ophthalmology/ Journal Canadien d’Ophtalmologie, 48, 364–367. McConkie, G., & Rayner, K. (1976a). Asymmetry of the perceptual span in reading. Bulletin of the Psychonomic Society, 8, 365–368. McConkie, G., & Rayner, K. (1976b). The span of the effective stimulus during a fixation in reading. Perception & Psychophysics, 17, 365–368.
99
McIlreavy, L., Fiser, J., & Bex, P. J. (2012). Impact of simulated central scotomas on visual search in natural scenes. Optometry and Vision Science, 89, 1385–1394. Mcmahon, T. T., Hansen, M., Stelmack, J., Oliver, R., & Viana, M. A. (1993). Saccadic eye movements as a measure of the effect of low vision rehabilitation on reading rate. Optometry & Vision Science, 70, 506–510. McMahon, T., Hansen, M., & Viana, M. (1991). Fixation characteristics in macular disease. Relationship between saccadic frequency, sequencing, and reading rate. Investigative Ophthalmology & Visual Science, 32, 567–574. Nilsson, U. L., Frennesson, C., & Nilsson, S. E. G. (1998). Location and stability of a newly established eccentric retinal locus suitable for reading, achieved through training of patients with a dense central scotoma. Optometry & Vision Science, 75, 873–878. Palmer, S., Logan, D., Nabili, S., & Dutton, G. N. (2010). Effective rehabilitation of reading by training in the technique of eccentric viewing: evaluation of a 4-year programme of service delivery. British Journal of Ophthalmology, 94, 494–497. Pusswald, G., Steinhoff, N., & Müller, C. (2013). Coupling acoustic feedback to eye movements to reduce spatial neglect. Topics in Stroke Rehabilitation, 20, 262–269. Rezec, A. A., & Dobkins, K. R. (2004). Attentional weighting: A possible account of visual field asymmetries in visual search? Spatial Vision, 17, 269–293. Schuchard, R. A. (2005). Preferred retinal loci and macular scotoma characteristics in patients with age-related macular degeneration. Canadian Journal of Ophthalmology/Journal Canadien d’Ophtalmologie, 40, 303–312. Seiple, W., Szlyk, J. P., McMahon, T., Pulido, J., & Fishman, G. A. (2005). Eyemovement training for reading in patients with age-related macular degeneration. Investigative Ophthalmology & Visual Science, 46, 2886–2896. Skrandies, W. (1987). The upper and lower visual field of man: Electrophysiological and functional differences. Springer. Timberlake, G. T., Mainster, M. A., Peli, E., Augliere, R. A., Essock, E. A., & Arend, L. E. (1986). Reading with a macular scotoma. I. Retinal location of scotoma and fixation area. Investigative Ophthalmology & Visual Science, 27, 1137–1147. Varsori, M., Perez-Fornos, A., Safran, A. B., & Whatham, A. R. (2004). Development of a viewing strategy during adaptation to an artificial central scotoma. Vision Research, 44, 2691–2705. Vingolo, E. M., Cavarretta, S., Domanico, D., Parisi, F., & Malagola, R. (2007). Microperimetric biofeedback in AMD patients. Applied Psychophysiology and Biofeedback, 32, 185–189. Von Noorden, G. K., & Mackensen, G. (1962). Phenomenology of eccentric fixation. American Journal of Ophthalmology, 53, 642–661. Walsh, D. V., & Liu, L. (2014). Adaptation to a simulated central scotoma during visual search training. Vision Research, 96, 75–86. Watson, G. R., Schuchard, R. A., De l Aune, W. R., & Watkins, E. (2006). Effects of preferred retinal locus placement on text navigation and development of advantageous trained retinal locus. Journal of Rehabilitation Research and Development, 43, 761. White, J., & Bedell, H. (1990). The oculomotor reference in humans with bilateral macular disease. Investigative Ophthalmology & Visual Science, 31, 1149–1161. Whittaker, S., Budd, J., & Cummings, R. (1988). Eccentric fixation with macular scotoma. Investigative Ophthalmology & Visual Science, 29, 268–278. Whittaker, S. G., Cummings, R. W., & Swieson, L. R. (1991). Saccade control without a fovea. Vision Research, 31, 2209–2218. Wiecek, E. K., Jackson, M. L., & Bex, P. J. (2015). Binocular microperimetry with simulated asymmetric bilateral scotomas. Investigative Ophthalmology & Visual Science, 56, 549. 549.
Contents lists available at ScienceDirect
Vision Research journal homepage: www.elsevier.com/locate/visres
Peripheral oculomotor training in individuals with healthy visual systems: Effects of training and training transfer Dylan Rose ⇑, Peter Bex Northeastern University, Psychology Department, United States
a r t i c l e
i n f o
Article history: Received 20 January 2016 Received in revised form 6 October 2016 Accepted 8 October 2016
Keywords: Eye movements Training Preferred retinal locus Central visual field loss
a b s t r a c t Individuals with pathological or simulated central visual field loss can be trained to use a preferred retinal locus (PRL) as a substitute for their non-functioning fovea. The functional benefits of a stable PRL are well documented, but little is known about oculomotor adaptations during PRL acquisition or transfer of training to another location in response to real or simulated disease progression. In this study, eight normally-sighted observers were trained to use a pseudo-PRL (pPRL) at one of two locations by guiding an eccentrically placed, gaze-contingent ring over a fixation target. The pPRL location was 6.4 degrees in either inferior or right visual field, balanced across observers. Training was completed in two sessions of 200 hundred trials separated by a week. Between sessions, the pPRL position was switched. Task performance was quantified both in terms of gaze stability around the fixation target and gaze accuracy in terms of distance between the target and ring centers. The latter was used to provide feedback by covarying the diameter of the ring to make the task easier or harder on the basis of subject performance. Accuracy and stability significantly increased with training and was comparable at each trained location. Performance gains were retained over a week and transferred from the first to the second pPRL location. Thus, pPRL training with feedback can provide sustained, generalizable improvements in oculomotor control following simulated foveal vision loss. These results suggest that low vision rehabilitation specialists may prioritize PRL training locations based on sensory function alone, since oculomotor gains are relatively uniform; and that training early in the disease process may benefit later adaptations should eye disease progress. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Individuals with retinal diseases such as age-related macular degeneration (AMD) that damage or destroy the fovea may develop one or more preferred retinal loci (PRL) as a compensatory adaptation to loss of vision in the center of the visual field (Fletcher and Schuchard, 1997; Von and Mackensen, 1962; Whittaker et al., 1988; White and Bedell, 1990; Whittaker et al., 1991). Over the course of weeks to months, either a single general-purpose PRL (Fletcher and Schuchard, 1997; Von and Mackensen, 1962; Whittaker et al., 1988) or a series of often task-specific PRLs (Timberlake et al., 1986) may be adopted. However, in spite of the substantial literature documenting the existence of PRLs, relatively little is known about the processes by which they are formed and whether formation at certain retinal locations may lead to superior performance on visual tasks relative to others. ⇑ Corresponding author. E-mail addresses: [email protected] (D. Rose), [email protected] (P. Bex). http://dx.doi.org/10.1016/j.visres.2016.10.016 0042-6989/Ó 2017 Elsevier Ltd. All rights reserved.
It has been demonstrated that the formation of a PRL is associated with improved functional vision outcomes in tasks such as reading (Crossland et al., 2004; Seiple et al., 2005; Palmer et al., 2010). Low-vision rehabilitation specialists therefore may train a PRL in patients with pathological central vision loss (Schuchard, 2005; Watson et al., 2006). However, few studies have specifically examined changes in oculomotor control during the acquisition of a PRL (Crossland et al., 2004). Understanding these processes is important, as many visual tasks require precise eye movement control, and its loss may impair visual function independently of retinal pathologies that directly affect sensory performance. This idea is supported by converging lines of evidence indicating both that fovea loss is associated with changes in oculomotor control (Bullimore and Bailey, 1995; Crossland et al., 2004; Schuchard, 2005) and that even at supposedly spared retinal locations at the same eccentricities, individuals with central field loss perform many tasks less well than individuals with healthy vision (McMahon et al., 1991; Mcmahon et al., 1993Mcmahon et al., 1993; Timberlake et al., 1986).
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One of the reasons for the relatively limited research in this area may be that there are substantial technical and methodological challenges associated with collecting eye movement data from patients with retinal disease. Patient populations express highly variable symptom profiles, making it difficult to obtain homogeneous samples of subjects for experimental purposes (Bowers and Reid, 1997). Fixational instability that accompanies fovea loss makes calibration of eye tracking systems on patients difficult. One method used to overcome these problems is to simulate central scotomas in individuals with healthy vision using gaze contingent stimuli on computer displays (e.g. Aguilar and Castet, 2011; McIlreavy et al., 2012). These simulations have numerous limitations, but if carefully controlled, can provide good approximations of the perceptual and behavioral consequences of visual impairments (Bowers and Reid, 1997). Simulations also facilitate the use of statistically powerful within-subjects/cross-over experimental designs, which would be difficult or impossible to implement among patients. A small but growing literature exists suggesting that over the course of several hours of explicit training using simulated scotomas, individuals with healthy retinas can also form a ‘‘PRLLike” region (Kwon et al., 2013; Varsori et al., 2004; Walsh and Liu, 2014). We term this a ‘‘pseudo-PRL” (pPRL) because it is functionally similar to a true PRL but is not a product of a disease processes. The possibility of training pPRLs means that it may be possible to study oculomotor control during PRL development without invoking the difficulties associated with tracking the eye movements of patients with central scotomas. We therefore used a pPRL induction paradigm to ask the following questions: Does pPRL training reduce fixational stability and the magnitude of oculomotor deviations or errors around a target? Such changes are thought to be an important part of the process of adaptation to retinal disease (Crossland et al., 2004), and are considered an important objective in rehabilitation programs as well (Mandelcorn et al., 2013). These findings further point to the important role that both implicit (natural process of response to disease progression in unmanaged retinal disease) and explicit (direction through rehabilitation training) feedback plays in the stabilization of a PRL. Explicit feedback in particular has been shown to improve treatment outcomes during visual rehabilitation training in response closs (Contestabile et al., 2002; Hall and Ciuffreda, 2001Hall and Ciuffreda, 2001; Pusswald et al., 2013; Vingolo et al., 2007). To our knowledge, however, there is little available data on changes to oculomotor control as a function of the implicit time-course of training or practice at a PRL or pPRL site (though see Kwon et al., 2013; Varsori et al., 2004; Walsh and Liu, 2014 for some discussion of this issue). Are oculomotor control changes associated with pPRL development affected by meridional performance differences across the retina? Although it is generally understood that functional vision and oculomotor control performance fall as a function of increasing retinal eccentricity, there is debate regarding the merits of selecting a PRL at specific retinal orientations relative to the fovea. There are well-documented meridional asymmetries in visual function such as acuity and contrast sensitivity (Skrandies, 1987), attentional resolution (Rezec and Dobkins, 2004) and the volitional control of ‘‘sustained” attention (Alpeter et al., 2000; MacKeben, 1999MacKeben, 1999), chromatic sensitivity (Levine and McAnany, 2005), motion sensitivity (Edwards and Badcock, 1993; Levine and McAnany, 2005), and crowding (He et al., 1996). Is pPRL training retained across time and transferred across locations? Many retinal diseases progress over time and therefore a trained PRL may eventually be claimed by an advancing
lesion (Nilsson et al., 1998). We therefore examine whether effects of pPRL training at one location are ‘‘carried over” to subsequent training at a different location. It is important to note that while these questions and the methods we have chosen to address them are strongly informed by current research on pPRL induction, our approach differs in one important respect. Specifically, because we wished to test whether training transfers between locations, subjects were not permitted toselect the pPRL site for themselves. This is undoubtedly a key feature of the development of true PRL, and thus an important component of a realistic simulation of the same process with pPRL. However, it would be difficult or impossible to have subjects spontaneously select pPRL locations that were equally eccentric but in different locations, or to maintain a constant distance between trained locations.
2. Materials & methods Eye movement data for this project were collected using an SR Research Eyelink 1000 infrared eye tracking system. Stimuli were presented on a 68.58 cm diagonal width ASUS VG278He monitor running at a 144 Hz refresh rate. Subjects were seated at a distance of 55 cm from the display, which therefore subtended a 54° horizontal visual angle. Subjects’ heads were stabilized during the experiment using an Eyelink-supplied chin and forehead rest. Stimuli were generated, displayed, and modified in real time on the basis of input from the Eyelink through the use of the MATLAB Psychtoolbox (Brainard, 1997) and Eyelink Toolbox (Cornelissen et al., 2002). Data were sampled at a rate set to match the refresh rate of the monitor. Subjects’ gaze profile was calibrated to the display using the standard nine-point calibration protocol provided with the Eyelink before each experimental session began. Eight participants (six women, two men) with normal or corrected-to-normal vision were recruited from the authors’ laboratory and from the undergraduate population at Northeastern University. Undergraduates received course credit towards the completion of their introductory psychology course in exchange for their participation. All were naive to the purposes of the study at intake and indicated their willingness to participate by signing an informed consent document associated with a protocol approved by the University Ethics Board. The Northeastern University Ethics Board evaluated the protocol and confirmed that this research adhered to the tenets of the Declaration of Helsinki. Participants completed a total of 400 training trials, split into two training sessions of 200 trials each. Sessions were separated by a period of roughly one week (mean 8.6 days, sd 3.2 days). Within a session, each subject completed four blocks of 50 trials. Between blocks, they were asked to rest for as long as they felt they needed. All were then re-calibrated using the same nine-point calibration procedure before continuing the experiment. Trials lasted for fifteen seconds. During a trial, subjects were asked to center a gaze contingent ring over a motionless fixation target and to maintain that position for as long as they were able (see Fig. 1 for a schematic representation of the task). The fixation target was a full-contrast red (RGB: [255,0,0]) circle with a diameter of 24 pixels. The line forming the gaze contingent ring was three pixels wide and drawn as a full contrast green (RGB: [0,255,0]). Trials fell into one of two orientation conditions: ‘‘east” or ‘‘south”. In the east, the ring was drawn 6.4° (128 pixels) to the right, and in the south 6.4° below, the foveated point of regard. Each session contained trials associated with one condition. At the beginning of the second training session, subjects switched conditions. The order in which they were completed was counterbalanced between subjects.
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Subject GM was excluded from all analyses of stability data as raw gaze position data was not collected for them. Average byblock, across-subject performance for both outcome measures is presented in Fig. 3a and b. In both, points are mean performance across subjects and trials within a block of training; error bars are fitted 95% confidence intervals. In both cases, a similar pattern of performance emerged across time: rapid improvement between the first and second blocks of training, then a leveling off toward the end of the first training session. At the beginning of the second
Average Accuracy (Log Pixels)
Within a trial, if subjects remained ‘‘on target” over the fixation target for a period of 100 ms, the diameter of the ring was reduced by a factor of 1.18, making the task more difficult. Similarly, if they fell ‘‘off target”during the same period, its diameter was increased to an equivalent degree, making the task easier. Finally, they were prevented from using their fovea to assist them by removing the fixation target from the screen if their foveated point of regard fell within a 2° radius of the target center. This approxmiated an absolute central scotoma of the same size. Performance on the task was measured in two ways. First, gaze ‘‘accuracy” was assessed in terms of the within-trial median logtransformed (natural log or base e) euclidean distance between the center of the ring and the center of the target. Second, their gaze ‘‘stability” with respect to the target was assessed in terms of the area within a fitted isocontour containing 95% of the gaze points within a trial (See Castet and Crossland, 2012 for a full description of this method). Fig. 2 is a schematic representation of the two performance measures and their relationship to each other. Data from this experiment were analysed using the R statistical programming language. First, hierarchical linear models (HLMs) were fitted to both the stability and error data. The structure of both models included fixed effects terms for the total block number (one to eight), orientation condition, and an interaction term between orientation condition and whether it was the first or second training session. Both also included by-subject random effects and slopes to capture potential individual differences in both baseline performance and rate of learning. This approach also allowed us to control significant multi-collinearities in the data at a number of scales resulting from the repeated-measures design of the experiment and correlations between sample-to-sample deviations from the target. Second, to assess the degree to which effects of training survived the inter-set period and transferred to the new training location, we used paired t-tests comparing subjects’ performance in both outcome measures between the first block of the first training session (block 1) and the first block of the second (block 5).
3. Results
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Fig. 1. Subjects completed a training session composed of 200 trials in one orientation condition, then completed a second session in the other approximately a week later. Keeping the ring on target caused it to contract after 100 ms, making the task harder. Falling off target during the same period caused it to expand, making the task easier. The green ring is the gaze contingent ring; the red circle represents the fixation target. Recorded foveal position (yellow circle) was not shown to research participants. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 2. The distance between the center of the red target dot and the centroid of the cluster of gaze points (blue line) is the subject’s gaze ‘‘accuracy” with respect to the target. The region highlighted in green is a fitted isocontour that contains 95% of the available gaze points(yellow circles), representing a subject’s gaze ‘‘stability” to the target. Data are simulated and presented in normalized, arbitray units. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Absolute Block Number Fig. 3. a: mean log-transformed median distance (pixels) between the centers of the target and ring as a function of training block number across subjects and trials. Dots represent within block means, and bars 95% confidence intervals. The dashed line between blocks 4 and 5 on the x-axis is the point at which subjects switched orientation conditions. b: mean stability performance (pixels-squared) within blocks and across subjects. The elements of this figure are otherwise the same as in a.
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session, both stability and accuracy performance fell sharply, then ultimately improved to levels beyond that achieved during the first session of training. In the fitted model for the error data, only the absolute block number term attained statistical significance (coefficient value: -.122, p < 0.01). This suggests that the training was associated with significant improvements in fixational accuracy at the pPRL site. The parameter associated with training block number indicates that after completing all eight blocks, the magnitude of subjects’ errors with respect to the target fell by a factor of nearly one natural log unit, i.e. an almost threefold (2.7) improvement. For the stabillity data, the absolute block number was also the only paramater that reached statistical significance (coefficient value: 18,192.570, p < 0.01), indicating that stability also significantly improved as a function of training. Fig. 4 presents means and 95% confidence intervals across subjects and trials between blocks one and five. A two-tailed paired ttest comparing accuracy performance between these blocks produced a significant result (df ¼ 398; t ¼ 4:51; p < 0:001); stability performance between them was not significantly different (df ¼ 349; t ¼ 0:4654; p < 0:001). This suggests that stability performance gains may be less resistant to decay over time than accuracy performance, though recovery and improvement appears to resume quickly starting in the third block of the second training session (block 7). 4. Discussion We have demonstrated that explicit training at an induced pPRL location can significantly improve both fixation stability and accuracy with respect to a stable target. A paired t-test comparing performance between the end of the first and the beginning of the second training session suggested that training benefits to accuracy persist over at least a week (and those for stability are quickly recovered and improved upon after the beginning of the second session). Both stability and accuracy gains also appeared to be ultimately robust to orientation changes of up to 90°. These findings point to a possible dissociation between oculomotor control and functional vision maps. Assuming similar results are obtained in patients with pathological central vision loss, they could have important implications for the development of our method as a training program to assist the visually impaired. If
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Fig. 4. Means and 95% confidence intervals for outcome measures (accuracy in red, stability in blue) between training block 1 (the first block of the first session) and training block 5 (the first block of the second training session). Data in this figure have been transformed to z-scores (mean zero, standard deviation of one) in order to set them on a shared scale. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the center of the oculomotor control map – and thus the site of the most precise eye movement control – can effectively be shifted through training in any direction away from the fovea, then there may be little or no penalty in terms of oculomotor components of visual function associated with training a PRL at a particular orientation relative to some other. This would greatly simplify the decision concerning where to train a PRL in clinical populations, and suggests that for oculomotor control, equivalent rehabilitation outcomes may be achieved at different orientations. As discussed above, meridional asymmetries favor the lower visual field for acuity and contrast sensitivity (Skrandies, 1987), attentional resolution (Rezec and Dobkins, 2004), chromatic sensitivity (Levine and McAnany, 2005), motion sensitivity (Edwards and Badcock, 1993; Levine and McAnany, 2005), and crowding (He et al., 1996) (for review see Alpeter et al., 2000; MacKeben, 1999). Our results therefore suggest that the decision about where to train a PRL might depend primarily on meridional sensory factors, with little or no advantage to particular orientations for oculomotor control. This work is preliminary and therefore subject to several qualifications. First, we investigated training effects at only two orientations relative to the fovea: below and to the right. Although we assume that any meridional effects are symmetric along one axis above or below the horizontal midline or to the left and right of the vertical, it is possible that this assumption would not hold. Additional studies with changes made between randomly selected pairs of orientations on the cardinal axes relative to the fovea, as well as between locations not on the cardinal axes, are required to fully generalize our findings. We have also only examined the effect of our training program in terms of performance in the simplest oculomotor control scenario: maintaining a stable fixation on a highly-visible, nonmoving, eccentrically-positioned target. It is unknown whether they extend to more complicated oculomotor tasks such as making saccades or pursuit. Nevertheless, improvements in fixation stability in the peripheral visual field are associated with functional benefits in patients (Crossland et al., 2004; McMahon et al., 1991) and healthy controls (Falkenberg et al., 2007). It is therefore possible that improvements in fixation stability we report should be accompanied by improvements in visual function. Nevertheless, it is important to also acknowledge that for tasks such as reading, outcomes at different retinal orientations may be constrained by additional factors such as the extent of the perceptual span in different directions relative to the trained pPRL location (McConkie and Rayner, 1976b, 1976a). The relationship between oculomotor and sensory performance is complex, and any explicit claims regarding the impact of this training program on one as a function of the other require further explicit testing. Because this study was performed using subjects with healthy vision and a simulated scotoma, it remains to be seen whether our findings would extend to training in the context of pathological retinal scotomas. Although our method may have applications outside the treatment of visual disease, it is obviously of substantially less value if it does not accurately reflect to at least a limited degree the experience of those with retinal disease. We are currently developing tools to facilitate eye tracking, and thus this program of research on those with real visual impairment (see Wiecek et al., 2015). Finally, additional testing may also be required to establish whether improvements associated with the training program reach some asymptote as a function of time, as the time and testing burden associated with training are significant, and it is possible that fixational stability during later trials may be unfairly reduced as result of fatigue. Although the effects of training we report are substantial, we did not collect data to compare subjects’ performance in the eccentric viewing conditions to their foveal perfor-
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mance. While it is highly unlikely that our training method could help participants achieve fovea-like levels of fixational stability, an accurate estimate of the rate of approach to this value would help to establish the point at which training gains are outweighed by increasing participant burdens. References Aguilar, C., & Castet, E. (2011). Gaze-contingent simulation of retinopathy: Some potential pitfalls and remedies. Vision Research, 51, 997–1012. Alpeter, E., MacKeben, M., & Trauzettel-Klosinski, S. (2000). The importance of sustained attention for patients with maculopathies. Vision Research, 40, 1539–1547. Bowers, A., & Reid, V. (1997). Eye movements and reading with simulated visual impairment. Ophthalmic and Physiological Optics, 17, 392–402. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436. Bullimore, M. A., & Bailey, I. L. (1995). Reading and eye movements in age-related maculopathy. Optometry & Vision Science, 72, 125–138. Castet, E., & Crossland, M. (2012). Quantifying eye stability during a fixation task: A review of definitions and methods. Seeing and Perceiving, 25, 449–469. Contestabile, M., Recupero, S., Palladino, D., De Stefanis, M., Abdolrahimzadeh, S., Suppressa, F., & Gabrieli, C. B. (2002). A new method of biofeedback in the management of low vision. Eye, 16, 472–480. Cornelissen, F. W., Peters, E. M., & Palmer, J. (2002). The eyelink toolbox: Eye tracking with matlab and the psychophysics toolbox. Behavior Research Methods, Instruments, & Computers, 34, 613–617. Crossland, M. D., Culham, L. E., & Rubin, G. S. (2004). Fixation stability and reading speed in patients with newly developed macular disease. Ophthalmic and Physiological Optics, 24, 327–333. Edwards, M., & Badcock, D. R. (1993). Asymmetries in the sensitivity to motion in depth: A centripetal bias. Perception, 22, 1013. 1013. Falkenberg, H. K., Rubin, G. S., & Bex, P. J. (2007). Acuity, crowding, reading and fixation stability. Vision Research, 47, 126–135. Fletcher, D. C., & Schuchard, R. A. (1997). Preferred retinal loci relationship to macular scotomas in a low-vision population. Ophthalmology, 104, 632–638. Hall, E., & Ciuffreda, K. (2001). Eccentric viewing training in macular degeneration using auditory ocular motor biofeedback. Journal of Behavioral Optometry, 12, 87–93. He, S., Cavanagh, P., & Intriligator, J. (1996). Attentional resolution and the locus of visual awareness. Nature, 383, 334–337. Kwon, M., Nandy, A. S., & Tjan, B. S. (2013). Rapid and persistent adaptability of human oculomotor control in response to simulated central vision loss. Current Biology, 23, 1663–1669. Levine, M. W., & McAnany, J. J. (2005). The relative capabilities of the upper and lower visual hemifields. Vision Research, 45, 2820–2830. MacKeben, M. (1999). Sustained focal attention and peripheral letter recognition. Spatial Vision, 12, 51–71. Mandelcorn, M. S., Podbielski, D. W., & Mandelcorn, E. D. (2013). Fixation stability as a goal in the treatment of macular disease. Canadian Journal of Ophthalmology/ Journal Canadien d’Ophtalmologie, 48, 364–367. McConkie, G., & Rayner, K. (1976a). Asymmetry of the perceptual span in reading. Bulletin of the Psychonomic Society, 8, 365–368. McConkie, G., & Rayner, K. (1976b). The span of the effective stimulus during a fixation in reading. Perception & Psychophysics, 17, 365–368.
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McIlreavy, L., Fiser, J., & Bex, P. J. (2012). Impact of simulated central scotomas on visual search in natural scenes. Optometry and Vision Science, 89, 1385–1394. Mcmahon, T. T., Hansen, M., Stelmack, J., Oliver, R., & Viana, M. A. (1993). Saccadic eye movements as a measure of the effect of low vision rehabilitation on reading rate. Optometry & Vision Science, 70, 506–510. McMahon, T., Hansen, M., & Viana, M. (1991). Fixation characteristics in macular disease. Relationship between saccadic frequency, sequencing, and reading rate. Investigative Ophthalmology & Visual Science, 32, 567–574. Nilsson, U. L., Frennesson, C., & Nilsson, S. E. G. (1998). Location and stability of a newly established eccentric retinal locus suitable for reading, achieved through training of patients with a dense central scotoma. Optometry & Vision Science, 75, 873–878. Palmer, S., Logan, D., Nabili, S., & Dutton, G. N. (2010). Effective rehabilitation of reading by training in the technique of eccentric viewing: evaluation of a 4-year programme of service delivery. British Journal of Ophthalmology, 94, 494–497. Pusswald, G., Steinhoff, N., & Müller, C. (2013). Coupling acoustic feedback to eye movements to reduce spatial neglect. Topics in Stroke Rehabilitation, 20, 262–269. Rezec, A. A., & Dobkins, K. R. (2004). Attentional weighting: A possible account of visual field asymmetries in visual search? Spatial Vision, 17, 269–293. Schuchard, R. A. (2005). Preferred retinal loci and macular scotoma characteristics in patients with age-related macular degeneration. Canadian Journal of Ophthalmology/Journal Canadien d’Ophtalmologie, 40, 303–312. Seiple, W., Szlyk, J. P., McMahon, T., Pulido, J., & Fishman, G. A. (2005). Eyemovement training for reading in patients with age-related macular degeneration. Investigative Ophthalmology & Visual Science, 46, 2886–2896. Skrandies, W. (1987). The upper and lower visual field of man: Electrophysiological and functional differences. Springer. Timberlake, G. T., Mainster, M. A., Peli, E., Augliere, R. A., Essock, E. A., & Arend, L. E. (1986). Reading with a macular scotoma. I. Retinal location of scotoma and fixation area. Investigative Ophthalmology & Visual Science, 27, 1137–1147. Varsori, M., Perez-Fornos, A., Safran, A. B., & Whatham, A. R. (2004). Development of a viewing strategy during adaptation to an artificial central scotoma. Vision Research, 44, 2691–2705. Vingolo, E. M., Cavarretta, S., Domanico, D., Parisi, F., & Malagola, R. (2007). Microperimetric biofeedback in AMD patients. Applied Psychophysiology and Biofeedback, 32, 185–189. Von Noorden, G. K., & Mackensen, G. (1962). Phenomenology of eccentric fixation. American Journal of Ophthalmology, 53, 642–661. Walsh, D. V., & Liu, L. (2014). Adaptation to a simulated central scotoma during visual search training. Vision Research, 96, 75–86. Watson, G. R., Schuchard, R. A., De l Aune, W. R., & Watkins, E. (2006). Effects of preferred retinal locus placement on text navigation and development of advantageous trained retinal locus. Journal of Rehabilitation Research and Development, 43, 761. White, J., & Bedell, H. (1990). The oculomotor reference in humans with bilateral macular disease. Investigative Ophthalmology & Visual Science, 31, 1149–1161. Whittaker, S., Budd, J., & Cummings, R. (1988). Eccentric fixation with macular scotoma. Investigative Ophthalmology & Visual Science, 29, 268–278. Whittaker, S. G., Cummings, R. W., & Swieson, L. R. (1991). Saccade control without a fovea. Vision Research, 31, 2209–2218. Wiecek, E. K., Jackson, M. L., & Bex, P. J. (2015). Binocular microperimetry with simulated asymmetric bilateral scotomas. Investigative Ophthalmology & Visual Science, 56, 549. 549.