ORIGINAL RESEARCH ARTICLE
published: 17 October 2014
doi: 10.3389/fpsyg.2014.01189
Perceptual learning in patients with macular degeneration
Tina Plank1 † , Katharina Rosengarth1 † , Carolin Schmalhofer1 , Markus Goldhacker 1 , Sabine Brandl-Rühle 2
and Mark W. Greenlee1 *
1
2
Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
Department of Ophthalmology, University Medical Center Regensburg, Regensburg, Germany
Edited by:
Gianluca Campana, Università degli
Studi di Padova, Italy
Reviewed by:
Angelika Lingnau, University of Trento,
Italy, Italy
Michael B. Hoffmann,
Otto-von-Guericke University,
Germany
*Correspondence:
Mark W. Greenlee, Institute for
Experimental Psychology, University
of Regensburg, Universitaetsstrasse
31, Regensburg 93053, Germany
e-mail: mark.greenlee@psychologie.
uni-regensburg.de
†Tina
Plank and Katharina Rosengarth
have contributed equally to this work.
Patients with age-related macular degeneration (AMD) or hereditary macular dystrophies
(JMD) rely on an efficient use of their peripheral visual field. We trained eight AMD and
five JMD patients to perform a texture-discrimination task (TDT) at their preferred retinal
locus (PRL) used for fixation. Six training sessions of approximately one hour duration
were conducted over a period of approximately 3 weeks. Before, during and after training
twelve patients and twelve age-matched controls (the data from two controls had to be
discarded later) took part in three functional magnetic resonance imaging (fMRI) sessions
to assess training-related changes in the BOLD response in early visual cortex. Patients
benefited from the training measurements as indexed by significant decrease (p = 0.001)
in the stimulus onset asynchrony (SOA) between the presentation of the texture target
on background and the visual mask, and in a significant location specific effect of the PRL
with respect to hit rate (p = 0.014). The following trends were observed: (i) improvement
in Vernier acuity for an eccentric line-bisection task; (ii) positive correlation between the
development of BOLD signals in early visual cortex and initial fixation stability (r = 0.531);
(iii) positive correlation between the increase in task performance and initial fixation
stability (r = 0.730). The first two trends were non-significant, whereas the third trend
was significant at p = 0.014, Bonferroni corrected. Consequently, our exploratory study
suggests that training on the TDT can enhance eccentric vision in patients with central
vision loss.This enhancement is accompanied by a modest alteration in the BOLD response
in early visual cortex.
Keywords: perceptual learning, fMRI BOLD, cortical plasticity, visual cortex, macular degeneration
INTRODUCTION
Visual performance in a variety of tasks, for example in
the detection or discrimination of certain stimulus patterns,
has been shown to improve with training. The results of
this perceptual learning appear to have long lasting effects
(e.g., Gibson, 1963; Goldstone, 1998; Fahle and Poggio, 2002;
Seitz and Watanabe, 2005; Sagi, 2011; Frank et al., 2014). At the
same time it often takes only hours or days of practice to enhance
perceptual abilities dramatically. This has been shown for texture
discrimination (Karni and Sagi, 1991), orientation discrimination (Schoups et al., 2001), spatial frequency discrimination
(Fiorentini and Berardi, 1981; Sireteanu and Rettenbach, 1995),
Vernier discrimination tasks (Poggio et al., 1992), and the discrimination of motion direction (Ball and Sekuler, 1982), among
others.
Perceptual learning thus appears to provide an ideal approach
to be used in clinical settings as well, in the attempt to improve the
abilities of visually impaired persons. Recent studies have focused
on amblyopia, where perceptual learning proved to improve vision
in the amblyopic eye (e.g., Polat et al., 2004; Zhou et al., 2006; Levi
and Li, 2009; Astle et al., 2010, 2011; Levi, 2012; Chung et al.,
2012; Hussain et al., 2012). Other applications include applying
perceptual learning in myopia and presbyopia (Polat, 2009; Polat
et al., 2012), in adults with impairments in stereopsis (Ding and
Levi, 2011) and in children with visual impairment (Huurneman
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et al., 2013) and developmental dyslexia (Gori and Facoetti, 2014).
In patients with central vision loss, Chung (2011) used rapid serial
visual presentation (RSVP) in an oral sentence-reading task to
improve patients’ reading ability, a paradigm that has already been
shown to improve reading speed in the peripheral visual field in
both younger (Chung et al., 2004; Yu et al., 2010b) and older (Yu
et al., 2010a) normally sighted adults. In Chung’s (2011) study,
RSVP reading speed improved on average by 53%.
Central vision loss is often caused by atrophy of photoreceptor cells in the macula, as can be observed in age-related
macular degeneration (AMD) or hereditary retinal dystrophies
(juvenile form, JMD) like Stargardt’s disease or cone-rod dystrophy. Patients with central scotoma often develop eccentric
viewing to cope with visual tasks like reading. The so-called “preferred retinal locus” (PRL) is a location in the eccentric visual
field that is habitually used by MD patients as a pseudo-fovea
(Bäckman, 1979; Timberlake et al., 1987; Whittaker et al., 1988;
Guez et al., 1993; Fletcher and Schuchard, 1997). In this study,
we trained AMD/JMD patients to perform a TDT (Karni and
Sagi, 1991) with the target located at or near the PRL, with
the aim to improve patients’ visual abilities at this specific location in their visual field. To investigate possible transfer effects
to other tasks or abilities, we used the Freiburg Visual Acuity
and Contrast Test (FrACT; Bach, 1996) before and after training. Possible effects on quality of life issues were assessed with
October 2014 | Volume 5 | Article 1189 | 1
Plank et al.
the Visual Function Questionnaire VFQ-25 (Mangione et al.,
2001).
We were also interested in the neural correlates of training
using functional magnetic resonance imaging (fMRI). The neural correlates of perceptual learning are still not well understood.
Results so far indicate an increase of the BOLD signal in primary visual cortex (Schwartz et al., 2002) with the training of a
TDT. But it was also shown with fMRI that with repeated training learning is accompanied by an initial increase followed by
a decrease in response (Yotsumoto et al., 2008). We observed a
similar development in a recent study on the effect of trial-bytrial feedback on a challenging coherent-motion discrimination
task (Goldhacker et al., 2014). In the initial phase of training
we observed an increase in the fMRI-BOLD signal in primary
visual cortex. With repeated training the BOLD signal in early
visual cortex decreases. At the same time the performance of participants increases further or remains constant at a high level.
We interpret this development in the BOLD signal over several measurements and days as an indication for neuroplastic
changes in visual cortex as a consequence of intensive training. In the initial training phase, additional neural resources are
recruited to learn the new perceptual task. After the task has
been well practiced, neural processing becomes more automatic
with equivalent high performance, thus less neural resources are
needed. As suggested by Yotsumoto et al. (2008), the increase
of brain activation in early visual cortex in the initial phase
of learning could be mediated by an increase in the number
or strength of synaptic connections, while the drop in activation at a later stage could be explained by synaptic downscaling
after performance becomes saturated. This pattern is also in line
with reports of participants, suggesting that they only guess at
the beginning of training, while later they claim to “see” the
differences in the stimuli clearly and almost without any effort
(Goldhacker et al., 2014). Further studies show that perceptual
learning can even lead to a parallelization of a visual conjunction
search task which can only be solved in a serial manner initially
(Frank et al., 2014).
In this study we explored the effects of perceptual learning
in patients with central visual field loss. We investigate whether
repeated intensive training can improve performance on the TDT,
while altering the response of neurons in early visual cortex
responsible for the processing of peripheral information. To test
for the visual-field specificity of training, during fMRI we tested
patients for targets located at their PRL or at a location opposite
of the PRL (OppPRL). Comparison with an age-matched control
group should indicate the extent to which this form of learning is
specific for persons with central vision loss.
MATERIALS AND METHODS
PATIENTS AND CONTROL SUBJECTS
Eight patients with diagnosed AMD and five patients with juvenile
macular dystrophy (JMD; i.e., three patients with cone-rod dystrophy and two patients with Stargardt’s disease) participated in the
study (8 males, 5 females; mean age 63.8 years, range 47–79 years).
Additionally twelve healthy age-matched control subjects took part
in the experiment (4 males, 8 females; mean age 62.1 years, range
47–78 years). All participants signed an informed consent form
Frontiers in Psychology | Perception Science
Perceptual learning in macular degeneration
prior to participating in the study and received modest monetary
compensation for their participation. The study was approved by
the Ethics Committee of the University of Regensburg and conducted in accordance with the ethical guidelines of the Declaration
of Helsinki.
CLINICAL CHARACTERISTICS AND VISUAL FIELD MEASUREMENTS
Table 1 presents details on demographic characteristics of patients
and controls, including the gender, age, diagnosis, duration of disease at time of study, study eye, scotoma size, visual acuity, position
of PRL, and fixation stability in the study eye. The dominant eye
was chosen as the study eye. Eye dominance was determined by
a modified version of the A-B-C Vision Test (Miles, 1930; Porac
and Coren, 1976), by aiming a distant target through an opening
formed by their hands. The study eye of the controls was always the
eye corresponding to the study eye of their age-matched patient.
Since some of our measures were conducted in the Eye Hospital,
fixation stability, and visual acuity could only be determined at the
start of the study.
Best-corrected visual acuity was determined by using a Vision
Screener (Rodenstock Rodavist 524/S1) and Eye Charts for distant visual acuity (Oculus Nr. 4616). Scotoma size was measured
using kinetic Goldmann perimetry with the isopters III/4e, I/4e,
I/3e, I/2e, and I/1e in all patients except patients P8, P10,
and P11. Defined as edges of the scotomata, those points were
marked, where isopter III/4e were no longer detected. Scotoma
size is reported in Table 1 as scotoma diameter in degrees of
visual angle as an average and approximation of rounded vertical
and horizontal dimensions. Reliability of the Goldmann perimetric measures depends on fixation stability. For patients P8,
P10, and P11 no Goldmann perimetry was available. Scotoma
size was inferred from fundus photography (autofluorescence
imaging as described in Rosengarth et al., 2013) instead. Controls did not undergo Goldmann perimetry. Figure 1 depicts
the shape of each patient’s scotoma in the respective study eye
as inferred from fundus photography. The techniques differ in
principle as Goldmann perimetry provides direct visual field
measures based on measures of visual function while fundus photography provides indirect evidence based on changes to fundus
morphology.
As described in Rosengarth et al. (2013), we used a Nidek MP-1
microperimeter (Nidek Co, Japan) to measure fixation stability.
Patients were requested to fixate (eccentrically) a red cross of 4◦
visual angle in diameter for approximately 30 s, whereas controls
fixated the target with their fovea. The technique measures 25
samples per second, resulting in 750 fixation samples over 30 s.
During the measurement the camera sometimes lost track of the
subject’s eye. This can be due to eye blinks or fixation instability in the form of large saccades. The Nidek software records
the time period that was measured and the proportion of the
time span that was effectively tracked, as well as the percentages
of fixation points that fell in a range of 2 or 4◦ diameter visual
angle around the center of the fixation target, based on the time
spans effectively tracked. Thus fixation stability can be overestimated by long or frequent time spans where the camera had lost
track of eye position due to large saccades. To compensate for
this we corrected the given fixation stability in the following way
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Plank et al.
Perceptual learning in macular degeneration
Table 1 | Characteristics of patients (P1–P13) and controls (C1–C12) according to age, gender, diagnosis, duration of disease in years, study eye,
decimal visual acuity, scotoma size (diameter in degrees visual angle), position of PRL (in degrees visual angle in x,y -coordinates with 0,0 put
at central vision), and fixation stability (percentage of fixation in 2 and 4◦ visual angle around fixation target; patients fixated with their PRL,
controls fixated with their fovea); m, male; f, female; Stargardt, Stargardt’s disease; OS, oculus sinister; OD, oculus dexter.
Patient
Age
Gender Diagnosis
Nr.
Duration
Study eye Decimal visual
Scotoma size in
Position of PRL (in ◦
Fixation stability in
visual angle)
study eye
of disease
acuity
study eye
(in years)
(study eye)
(diameter in ◦
visual angle )
x
y
2◦
4◦
P1
64
M
AMD
6
OS
0.1
10
−8
1
74
95
P2
64
M
AMD
7
OS
0.08
25
−4
−1.5
76
95
P3
79
M
AMD
9
OD
0.2
10
−6
3
89
100
P4
47
F
Stargardt
13
OD
0.05
15
−1.5
−6
95
100
P5
63
M
Cone-rod
19
OD
0.1
15
0
−5
22
22
−6
33.7 66.7
dystrophy
P6
57
M
Stargardt
18
OD
0.05
15
0
P7
58
M
AMD
5
OD
0.02
20
−13
−0.5
10.6 28.3
P8
61
F
AMD
8
OS
0.3
10
−6
−3
60
100
P9
72
F
AMD
12
OS
0.1
10
5
0
88
98
P10
74
F
AMD
20
OS
0.2
10
4
−4
83
100
P11
63
F
AMD
8
OD
0.1
20
−6
3
90
99
P12
59
M
Cone-rod
13
OS
0.1
10
−9
0
90
100
59
OD
0.1
10
0
−4
100
100
dystrophy
P13
69
M
Cone-rod
C1
64
F
−
−
OS
0.9
−
−
−
100
100
C2
67
M
−
−
OS
1.0
−
−
−
100
100
C3
71
M
−
−
OD
1.0
−
−
−
100
100
C4
47
F
−
−
OD
1.0
−
−
−
100
100
C5
78
M
−
−
OD
0.9
−
−
−
100
100
C6
52
F
−
−
OD
1.6
−
−
−
100
100
C7
63
F
−
−
OS
0.8
−
−
−
100
100
C8
51
F
−
−
OS
1.4
−
−
−
100
100
C9
64
F
−
−
OS
1.2
−
−
−
99
100
C10
54
F
−
−
OD
1.0
−
−
−
99
100
C11
56
F
−
−
OS
1.4
−
−
−
100
100
C12
78
M
−
−
OD
0.9
−
−
−
85
97
dystrophy
(see Plank et al., 2011): First we calculated the mean time span
for which the camera lost track of eye position in the normally
sighted control group, who fixated with their fovea. The resulting mean value of 9 s (SE = 3.0 s) yielded an estimate of the
time that could be attributed to eye blinks. In a second step we
subtracted this amount from the measured time, in which the
camera had lost track of the eye of each patient. The individual
difference between the measured time remaining and the effectively tracked time we attribute to large saccades. This time span
was added to the effectively tracked time in each patient. On this
basis we recalculated the percentages of fixation points falling in
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a range of 2 and 4◦ visual angle around the target for the patient
group.
The Nidek MP-1 was also used to measure a microperimetry of 30◦ diameter around the patients’ PRL, for all patients
except P8 and P11. Patients fixated a central cross with their
PRL on intact retina and were instructed to press a button as
soon as they perceived a target. We used “strategy-fast” with
static light points of intensity 16 and 8 dB, maximal brightness of 127 cd/m2 , that were presented for 200 ms each on
a grid comprising the 30◦ of the visual field centered around
the PRL.
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Perceptual learning in macular degeneration
FIGURE 1 | Continued
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Plank et al.
FIGURE 1 | Continued
Schematic depiction of positions of PRLs for all patients (upper left;
blue triangles mark the left eye as study eye, blue diamonds mark the
right eye as study eye, labeled with patient numbers 1–13) and
schematic depictions of the shape of each patient’s scotoma as
inferred from fundus photography (autofluorescence (P2, P3, P4, P5, P6,
P8, P10, P11, P12, P13) or infrared reflection imaging (P1, P7, P9; blue
symbols code the trained PRL position, red symbols code the
untrained OppPRL position). The x- and y-axis of the plots give the
eccentricity in degrees of visual angle.
The positions of PRLs were also assessed via the Nidek fundus
images. They were later verified using a video eyetracker (High
Speed Video Eyetracker Toolbox, Cambridge Research Systems,
UK), while the patients fixated a target on a computer monitor.
The distribution of positions of patients’ PRLs in the visual field
is given in Figure 1.
STIMULI AND TASK
Patients and controls were trained in a modified version of the
TDT described by Karni and Sagi (1991). During training subjects
were positioned with a distance of 60 cm in front of a 19-inch
screen with a refresh rate of 75 Hz, while the luminance for black
was 0.93 cd/m2 and for white 106 cd/m2 . We used Matlab (version 7.12.0) and the Psychophysics Toolbox (Brainard, 1997) for
programming the stimuli and the experimental design. Subjects
were instructed to fixate with their individual PRL while controls
had to hold their fixation in the center of the screen. To support
patients’ fixation a white dot (0.75◦ ) was placed at their individual
PRL position. Controls fixated at a white circle (0.5◦ visual angle)
at the center of the screen. During a trial, participants had to
discriminate the global orientation (horizontal/vertical) of three
tilted lines, located in their PRL, against a uniform background of
horizontal lines (see Figure 2).
Stimulus size was increased in comparison to the original
paradigm (Karni and Sagi, 1991), with a line length of 2◦ and
line width of 0.3◦ visual angle. We did not scale the target elements nor the distractors in the background for different eccentric
locations, since stimulus displays had to fit into a 30-degree diameter display. Target position was individually adjusted according
to each patient’s PRL position. Each control subject was assigned
to one particular patient and adopted that patient’s PRL position
as target position in the task. On each trial, the target stimulus
FIGURE 2 | Schematic depiction of a single trial in the training
sessions. While subjects were successfully fixating with their PRL
(white dot) the target stimulus appeared for 13.3 ms followed by a
mask, which was shown for 106.4 ms. The time between stimulus
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Perceptual learning in macular degeneration
was shown for 13.3 ms, followed by a blank screen with variable
stimulus onset asynchrony (SOA) and a mask stimulus (106.4 ms
duration), after which the participants responded with a button press (two buttons on a standard keyboard; see Figure 2).
In each block the SOA was adjusted by using an adaptive procedure (two–down, one-up), starting with a SOA of 492.1 ms,
to determine the 70.7% correct threshold (Levitt, 1971). Initial
step size was 53.2 ms, which was decreased by 13.3 ms (i.e., the
duration of one frame on the display) after each turning point. A
block stopped after 32 trials and the last measured SOA was taken
as the 70.7% threshold of this block. In a pre-training session
the initial individual SOA threshold was determined by running
five experimental blocks. This initial SOA threshold was then
used in all fMRI sessions. All patients and controls performed
six training sessions on separate days over a period of approximately 3–4 weeks. Each session consisted of 20 blocks, each with
32 trials. One block took about 2 min, depending on individual
SOA and reaction times, and each session lasted approximately
45 min.
EYETRACKING DURING PSYCHOPHYSICAL MEASUREMENTS
A trial only could be evoked if fixation was stable, which was
assured by an eye tracking system (resolution 0.05◦ , 250 Hz, HighSpeed Video Eye-Tracker Toolbox, Cambridge Research Systems,
Rochester, UK), thus the onset of trials could be delayed in case
of unstable fixation. Calibration was done by controls with their
fovea and by patients with their PRLs, resulting in a constant shift
with respect to the position of the fovea. This constant shift, in
coordinates of the individual PRL, was added as a correction factor
to the tracked position of the eye.
STIMULI AND TASK DURING fMRI
During the fMRI sessions, visual stimuli were projected onto a
circular screen (31◦ visual angle in diameter at a distance of
60 cm) placed behind the head of the participant at the end
of the scanner bore and visible via a mirror placed within the
MRI head coil. Subjects underwent an fMRI session before training, after three training sessions and again after another three
training sessions. The stimuli as described above appeared in a
distance of 60 cm on the screen (luminance of the dark background was 1.7 cd/ m2 , luminance of the white line elements
was 193 cd/m2 ). The fMRI sessions differed somewhat from the
and mask (SOA) was adjusted to provide a constant hit rate of
70.7%. At the end of each trial subjects had to indicate by button
press whether the three lines of the target formed a horizontal or
vertical array.
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Plank et al.
training sessions, since the target stimulus was presented randomly in half of the trials in the PRL position and in half of
the trials in the opposite hemifield (OppPRL), leading to slightly
lower performance (see below). This was indicated by a brief
color change of the white fixation dot before appearance of the
target stimulus. In most subjects the fixation dot at the target
location turned to red at the PRL or blue to indicate that the target would appear at the OppPRL. In some patients the color of
the dot only changed when the target was to appear in the OppPRL because those patients had problems in differentiating the
colors red and blue. This color cueing was kept constant for the
matched control subjects. As in the training session patients fixated with their PRL, while control subjects kept fixation in the
center of the screen. No eyetracker was used during fMRI, but
fixation stability could be estimated from psychophysical test sessions. The SOA achieved before training sessions served as fixed
SOA for all three fMRI sessions. At the beginning of a trial the
dot changed its colour for 505.4 ms, followed by the target for
13.3 ms. After an individual SOA the mask was presented for
106.4 ms. Then a fixation pause with temporal jitter of 3–4 s
succeeded before a new trial started. Each block consisted of 100
trials (50 PRL, 50 OppPRL), lasting for, on average, 8 min, again
depending on individual SOA and reaction times. Three blocks
were conducted in one fMRI session. The participants viewed all
test stimuli in all training and testing situations monocularly with
their study eye.
FREIBURG VISUAL ACUITY AND CONTRAST TEST
Before and after training subjects’ visual acuities and contrast sensitivity at the trained position in the visual field were assessed
by applying the FrACT1 (Bach, 1996) to monitor for possible improvements induced by training. Thereby the Landolt C
contrast sensitivity test with 100 and 50 arcmin diameter, the contrast grating test and the Vernier test were chosen. Luminance
linearization was applied as implemented in the software.
VISUAL FUNCTION QUESTIONNAIRE
To assess the patients’ own perception of their visual function before and after perceptual learning we used the National
Eye Institute’s VFQ-25 (Mangione et al., 2001) in its German
translation.
BEHAVIORAL DATA ANALYSIS
According to stimulus onset asynchronies obtained in the training sessions a 2 × 6 ANOVA for the factors group (patients,
controls) and session (training session 1–6) was performed. To
test explicitly for group differences in SOAs between training
sessions 1 and 6, we applied two t-tests. For the fMRI sessions we conducted 2 × 2 × 3 ANOVAs related to the factors
group (patients, controls), location (PRL, Opposite PRL) and
session (before, during and after training) with respect to the
dependent variables hit rate and reaction time. Additionally, we
performed two 2 × 3 ANOVAs with the factors location (PRL,
OppPRL) and session (before, during and after training), separately for each group, with respect to the dependent variables
Perceptual learning in macular degeneration
hit rate and reaction time. To test explicitly for group differences in hit rates between fMRI session 1 (before training) and
3 (after training), at the PRL and OppPRL, we applied four
t-tests.
Additionally we performed correlation analysis between initial
fixation stability, assessed before training started, and the development of hit rate and BOLD percent signal change in the PRL and
OppPRL associated area in the early visual cortex.
For all ANOVAs, we corrected for violation of sphericity
assumption if necessary by using Greenhouse–Geisser correction
(p < 0.05). All statistical tests were performed using PASW 21 for
Windows.
One patient (P13) was not able to participate in the fMRI sessions for physical reasons. We only included his behavioral values
for the group analysis of the SOA measurement (see below). In
total, data from 13 patients and 12 control subjects entered the
SOA analysis of the behavioral data acquired during the training
sessions. During the fMRI sessions, hit rate and reaction time were
recorded in 11 patients and 10 control subjects. Behavioral data
from one patient (P12) and two control subjects (C4 and C12)
were lost due to technical problems with the response box.
According to the subtest of the FrACT and the VFQ a possible
impact of training was assessed by paired t-tests (before and after
training). Data from the FrACT were acquired in 13 patients and 12
control subjects. Data from one patient (P7) was excluded from the
analysis of Landolt C contrast sensitivity, because he was not able
to do the test. The data from another patient (P2) was excluded
from the analysis of the Vernier test, owing to his inability to
execute the Vernier test before training. Data from all 13 patients
were available for the VFQ analysis.
STRUCTURAL AND FUNCTIONAL MRI MEASUREMENTS
Magnetic resonance imaging scanning was performed with a 3Tesla Allegra head scanner (Siemens, Erlangen, Germany) and
a one-channel head coil. Functional whole-brain images were
acquired interleaved with a T2∗ -weighted gradient echo planar
imaging (EPI) sequence (time-to-repeat, TR = 2 s; time-to-echo,
TE = 30 ms; flip angle, FA = 90◦ ) consisting of 34 transverse slices (voxel-size = 3 mm × 3 mm × 3 mm; field of
view, FOV = 192 mm × 192 mm). In addition, we collected a
high-resolution structural scan (160 sagittal slices each) with a
T1-weighted, magnetization prepared rapid gradient echo (MPRAGE) sequence (TR = 2.25 s, TE = 2.6 ms, FA = 9◦ , voxel
size = 1 mm × 1 mm × 1 mm, FOV = 240 mm × 256 mm).
The sequence was optimized for the differentiation of gray and
white matter by using parameters from the Alzheimer’s disease
Neuroimaging Initiative project2 .
MRI DATA ANALYSIS
Magnetic resonance imaging data analysis was performed with Statistical Parametric Mapping 8 (Wellcome Center of Neuroimaging,
London3 ). First a temporal interpolation of the functional data
using the slice time function in SPM8 was conducted. Afterward a motion correction over all sessions was applied to the
2 http://adni.loni.ucla.edu/
1 http://www.michaelbach.de/fract/download.html
Frontiers in Psychology | Perception Science
3 http://www.fil.ion.ucl.ac.uk/spm/
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Plank et al.
functional images followed by co-registering each participant’s
structural brain scan of the first session (before training) to the
functional images. Then images were normalized to the MNI
space, re-sampled to a 2 mm × 2 mm × 2 mm resolution and
smoothed with a three-dimensional Gaussian kernel (full-width
at half-maximum = 8 mm).
In the first-level statistical design the possible positions of the
PRL and the OppPRL as prediction variable for each session were
modeled separately and then convolved with the hemodynamic
response function.
For a region-of-interest (ROI) analysis the SPM toolbox Marsbar was applied (Brett et al., 2002). A functional localizer was
used to assess the individual representation area of the PRL,
the OppPRL, and the fovea in the early visual cortex of the
patients. Accordingly, during a separate fMRI scan contrast
reverting checkerboard disks (size: 9◦ × 9◦ visual angle, presented with a reversal rate of 8 Hz) and chromatic images of
everyday objects (e.g., animals, tools, vehicles, musical instruments; 7.3◦ × 7.3◦ visual angle) were visually presented on
the individually determined position of the PRL, a location
of the same eccentricity OppPRL and the fovea (corresponding to the scotoma region in the patients). For the control
subjects the PRL/OppPRL coordinates of their age-matched
patient were used. The PRL localizer scans were also conducted
monocularly with the same study eye. The photographs used
in the PRL localizer paradigm were collected from free Internet databases or taken by the authors. Stimuli were presented
blockwise on a gray background, together with a baseline condition (gray background of medium luminance). The blocks
were presented in four repetitions. Contrast reverting checkerboards and meaningful pictures were presented in the center,
the PRL or the opposite PRL in separate blocks of 13 s each,
the baseline condition (blank screen) in blocks of 18 s. In a
block with meaningful pictures, the picture changed every 2.2 s
without a gap, so that six different pictures were presented
sequentially in each object block (for a detailed description see
Rosengarth et al., 2013).
In a GLM analysis we modeled six regressors for the two types
of stimuli (checkerboards, everyday objects) and the three locations (fovea, PRL, OppPRL) while the baseline condition (blank
screen) served as an implicit baseline for the analysis to avoid an
overspecification of the statistical design. Individually weighted
T-maps for contrasts PRL > OppPRL and OppPRL > PRL were
calculated. A sphere of 5-mm radius was placed on the voxel with
the highest t-value of the resulting cluster in striate and extrastriate visual cortex. ROIs were always located in the hemisphere
contralateral to the PRL/OppPRL location in the visual field. Since
no explicit retinotopic mapping of visual area borders was conducted, we cannot separate these activations into the respective
visual areas. These spheres served as ROIs for calculation of the
individual percent signal changes in projection zones for the PRL
and OppPRL in the visual cortex by applying these ROIs for the
individual GLMs applied to the data of the main experiment.
The individual percent signal changes were integrated in a
2 × 2 × 3 factorial ANOVA with the factors group (patients, controls), location (PRL, OppPRL) and sessions (before, during, after
training).
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Perceptual learning in macular degeneration
We also tested for the existence of a linear or quadratic trend in
the factor session, with one-factorial ANOVAs, separately for each
location (PRL, OppPRL) and group (patients, controls).
Because of technical issues two control subjects (C4 and C12)
had to be excluded from the analysis of the fMRI data resulting in
12 patients and 10 controls for that analysis.
We also correlated patients’ fixation stability with the development of percent signal change of the BOLD response with the
training.
RESULTS
BEHAVIORAL DATA
In agreement with the original results of Karni and Sagi (1991),
during training patients and controls showed a training-induced
improvement in performance, as reflected in a significantly
decreasing SOA over the six training sessions [F(1,23) = 14.47;
p = 0.001; see Figure 3). No significant effect of group was
observed, suggesting that both patients and controls learned
the task equally well. Although not significant, there was a
trend toward an interaction between the factors group and session [F(1,23) = 3.5; p = 0.074]. This trend in the results
appears to be due to the fact that patients started generally with higher SOAs, which were followed by a steeper
decrease of SOAs over training compared to control subjects. Differences in SOAs in training session 1 between the
patient and control group just failed to reach significance
[t(23) = 2.2; p = 0.08, Bonferroni corrected for multiple comparisons]. SOAs in training session 6 were indistinguishable between patient and control group [t(23) = 0.46;
p = 1.00].
During the fMRI sessions there was a significant effect of session [F(1,19) = 13.6; p = 0.002] for the dependent variable hit
rate, but there was no effect of location nor group in the omnibus
ANOVA (Figure 4, upper panel). Additionally hit rates exhibited
a significant interaction between target location (PRL, OppPRL)
and group [F(1,19) = 4.6; p = 0.045]. As can be seen in Figure 4,
and was also tested by additional ANOVAs separately for patients
and controls, there was a significant effect of target location (PRL,
FIGURE 3 | Mean of stimulus onset asynchrony (SOA in ms) over six
training sessions. The SOA decreases significantly (p = 0.001) from the
first to the last training session in both patient (n = 13) and control (n = 12)
groups. Error bars show ±1 SE of the mean.
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Plank et al.
FIGURE 4 | Mean hit rates (A) and reaction times (B) in the PRL
(trained location, blue symbols) and the OppPRL (untrained location,
red symbols) for the patient (n = 11, left panel) and the control
groups (n = 10, right panel) before the first, fourth and after the
sixth training session. Error bars show ±1 SE of the mean. Data were
collected during the fMRI sessions, with individually fixed SOA. An
omnibus ANOVA revealed a significant effect of session [F (1,19) = 13.6;
OppPRL) in the patient group [F(1,10) = 8.78; p = 0.014]. Accordingly, the hit rate was significantly higher when the TDT target was
located in or near the PRL compared to when it was located in the
opposite visual hemifield. The control group showed no significant
location effect. Both groups, patients [F(2,20) = 9.5; p = 0.001]
and controls [F(1,9) = 5.7; p = 0.04], showed a significant session
effect, but no significant interactions.
For reaction times during the fMRI sessions we observed again
a main effect of session [F(2,38) = 6.6; p = 0.003], indicating a
decrease of reaction times with training, but no effect for location
Frontiers in Psychology | Perception Science
Perceptual learning in macular degeneration
p = 0.002] for the dependent variable hit rate, but no main effect of
target location nor group. Additionally hit rates exhibited a significant
interaction between target location (PRL, OppPRL) and group
[F (1,19) = 4.6; p = 0.045]. For reaction times an omnibus ANOVA
revealed again a main effect of session [F (2,38) = 6.6; p = 0.003], but
no main effect for location nor group. Also no significant interactions
were apparent.
nor group (see Figure 4, lower panel). No significant interactions
were apparent.
TRANSFER OF TDT TRAINING
The results of the FrACT, analyzed with paired t-tests, showed
a trend toward improvement of the Vernier task [t(11) = 2.22;
p = 0.048, not corrected for multiple comparisons; otherwise
p = 0.2, Bonferroni corrected] in the patient group (see Figure 5).
Here it has to be noted that an additional patient (P2) was not able
to perform the task before the perceptual training, but achieved a
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FIGURE 5 | Results from the FrACT (Bach, 1996). Two Landolt-C tests (with
50 and 100 arcmin optotypes, in % Michelson contrast), a grating contrast
test and a Vernier test were conducted. Improvements in patients’
performance with training were only apparent in the Vernier task [t (11) = 2.22;
p = 0.048, not corrected for multiple comparisons; p = 0.2 Bonferroni
Table 2 | Correlation coefficients (r ) and p -values (p ; not corrected for
multiple comparisons) between initial fixation stability (percentage of
fixations within 2◦ of fixation target) and difference in mean percent
signal change (upper rows) before the first and fourth training
session, as well as before the first and after the sixth training session
for PRL and OppPRL target locations.
Delta % signal
Difference
Difference
change
“during–before”
“after–before”
r
p
r
p
corrected]. One patient (P2) was not able to perform the task before the
perceptual training, but achieved a threshold of 5.58 arcmin in the task after
the perceptual training. One patient (P7) was not able to perform the
Landolt-C contrast sensitivity test, neither before nor after TDT training. Error
bars show ±1 SE of the mean.
development of hit rate in the patient group, separately for the
trained PRL and the untrained OppPRL, we found a significant
positive correlation with difference in hit rate between before and
during training, but only for the trained PRL (p = 0.007, not
corrected for multiple comparisons; otherwise p = 0.014, Bonferroni corrected; see Table 2; Figure 6). A correlation between
fixation stability and development of reaction times in the patient
group, separately for PRL and OppPRL, revealed no significant
results.
PRL
0.155
0.629
0.531
0.075
fMRI DATA
OppPRL
0.042
0.896
0.444
0.148
Patients exhibited a trend for increased percent signal changes
from the second to the third fMRI session which was similar for the
PRL and OppPRL projection zones in the early visual cortex, but
which failed to reach statistical significance (see Figure 7, upper
panel). While patients showed no obvious change in percent signal
change from the first to the second fMRI session control subjects
revealed an increase of percent signal change from the first to the
second fMRI session in both the trained and untrained projection
zones in the early visual cortex. From the second to the third
fMRI session, patients exhibited a modest increase in BOLD signal,
whereas controls showed a decrease for the signal in the trained
PRL associated area and a stabilization of the OppPRL associated
area.
A repeated-measures ANOVA revealed no significant effect
of session [F(2,40) = 1.7; p = 0.20], nor an effect of location
[F(1,20) = 0.02; p = 0.89] or group [F(1,20) = 2.09; p = 0.16]
in the omnibus ANOVA. Also no interactions were significant.
One-factorial ANOVAs for the factor session for the patients and
controls separately with target locations either PRL or OppPRL
indicated a marginally significant quadratic trend (blue line in
Figure 7, upper right panel) for the control group [F(1,9) = 5.05;
p = 0.05, not corrected for multiple comparisons; otherwise
p = 0.1, Bonferroni corrected]. Moreover, a non-significant linear
trend (blue line in Figure 7, upper left panel) was apparent for
the patient group [F(1,11) = 3.04; p = 0.11, not corrected for
multiple comparisons; otherwise p = 0.22, Bonferroni corrected]
with respect to the effect of training (sessions performed before,
Delta Hit rate
PRL
OppPRL
0.730
0.007
0.361
0.275
−0.145
0.652
0.015
0.966
Significant values are shown in bold font. These values are based on patient data
only (n = 12).
threshold of 5.58 arcmin in the task after the perceptual training.
Contrast sensitivity measures did not differ before and after training, neither for Landolt-C with 100 arcmin diameter [t(11) = 0.05;
p = 0.96] nor with 50 arcmin diameter [t(11) = −0.5; p = 0.62],
nor for the contrast grating test [t(12) = 0.85; p = 0.41, all pvalues not corrected for multiple comparisons]. One patient (P7)
was not able to perform the Landolt-C contrast sensitivity test,
neither before nor after TDT training. The control group did not
improve significantly with training in any subtests of the FrACT.
Compared to values acquired before TDT training, patients
yielded higher scores in the VFQ in the category of social functioning [t(12) = 2.79; p = 0.016, not corrected for multiple
comparisons; otherwise p = 0.18, Bonferroni corrected] after
training. All other scales showed no significant differences before
and after training.
EFFECT OF FIXATION STABILITY IN BEHAVIORAL DATA
When we correlated fixation stability before training (percentage
of fixations around 2◦ visual angle of the fixation point) and the
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Plank et al.
FIGURE 6 | Correlations between the difference in hit rate (before the
first and fourth training session (“during”), left panel, as well as before
the first and after the sixth training session (“after”), right panel, and
initial fixation stability for the trained PRL location (A) or the untrained
OppPRL location (B) for patient data only. Squares correspond to values
from JMD patients, diamonds to those from AMD patients. Correlation
during and after) on percent signal change in the PRL projection
zone in the early visual cortex. For the OppPRL condition (red
lines in Figure 7, upper left and right panel), no such trends were
observed (p = 0.42 and p = 0.35, respectively, not corrected for
multiple comparisons).
EFFECT OF FIXATION STABILITY IN FMRI DATA
When we correlated fixation stability (percentage of fixations
around 2◦ visual angle of the fixation point) and the development
of BOLD signal in visual cortex, we found a positive correlation
between fixation stability and difference in percent signal change
before and after training that just failed to reach significance
(p = 0.075, not corrected for multiple comparisons; otherwise
p = 0.15, Bonferroni corrected; see Table 2; Figure 8).
As becomes evident from Figures 6 and 8, a gap in fixation
stability could be observed between three patients (P5, P6, and
P7) with fixation stability <40% and the remaining patients, who
exhibit more stable fixation (≥60%). After excluding the data from
these three patients with fixation stability <40%, an ANOVA of
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Perceptual learning in macular degeneration
coefficients for the trained PRL location are r = 0.730 (p = 0.007; p = 0.014,
Bonferroni corrected) for the difference in hit rate “during–before,” and
r = 0.361 (p = 0.275) for the difference in hit rate “after–before.” For the
untrained OppPRL location correlation coefficients are r = −0.145 (p = 0.652)
for the difference in hit rate “during–before,” and r = 0.015 (p = 0.966) for the
difference in hit rate “after–before.”
BOLD percent signal change revealed a significant effect of training
session [F(2,16) = 4.1; p = 0.038; see Figure 7, lower panel] within
the patient group.
DISCUSSION
In this study we investigated whether patients with central vision
loss can benefit from perceptual learning. We wanted to determine
whether patients with central vision loss can be efficiently trained
at their eccentric PRL to perform a challenging TDT and if such
a learning effect might be reflected in fMRI-BOLD signal changes
in the respective projection zone in early visual cortex. Further we
investigated whether the gains accruing via TDT training at the
PRL could generalize to other aspects of visual performance and
vision-related aspects of quality of life.
Both patients and control subjects exhibited a typical learning
effect on the TDT which was indicated by a significant reduction
in SOA in both groups. This result is consistent with the classical findings of Karni and Sagi (1991), Schwartz et al. (2002), or
Yotsumoto et al. (2008). Behavioral data acquired during fMRI
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FIGURE 7 | Mean of percent signal change in the projection zones in
the early visual cortex of the PRL (trained location, blue symbols)
and the OppPRL (untrained location, red symbols) for patients
(n = 12) and controls (n = 10; upper row) before the first, fourth and
after the sixth training session. An omnibus ANOVA revealed no
indicated a significant effect of training on hit rates and reaction
times. Considering the two groups (patients, controls) separately
there was a significant effect of training in the patient group
for the factor location (PRL, OppPRL), which was not the case
for the control group. We further observed a significant interaction between target location and group with respect to hit rates
(see Figure 4A). Before training patients showed similar hit rates
for targets presented at the PRL and OppPRL locations in the
visual field. During training their hit rate increased for targets
presented at the PRL compared to when they were presented at
the location OppPRL. In contrast control subjects showed also
an increase in hit rates with training but no difference between
the trained and untrained locations. One explanation for this
finding could be that patients use their PRL additionally in their
daily life which could influence the training procedure and efficiency. Therefore it might also be more intuitive for the patients
to train on targets presented in their PRL since the PRL functions as a pseudo fovea, which is not the case for the control
subjects.
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Perceptual learning in macular degeneration
significant effects. The lower row shows these values for those patients
who exhibited fixation stability over 60% (n = 9). Here, a
repeated-measures ANOVA within the patient group revealed a significant
effect of training session (p = 0.038). Error bars show ±1 SE of the
mean.
In the fMRI results, we found neither a significant effect of
session, nor of location nor of group in the omnibus ANOVA.
We could observe a linear trend for the factor “training session”
at the signal in the PRL projection zone in early visual cortex in
the patient group while the control group seemed to exhibit a
quadratic trend in that area. McGovern et al. (2012) claim that
the low signal change which is sometimes found in studies dealing
with perceptual learning in early visual areas (e.g., Ghose et al.,
2002) might not be associated with the increase of performance
directly. This suggestion also seems to hold here, since we could
find clear learning effects according to SOA, hit rates and reaction times but only subtle changes of the amplitude of the BOLD
signal with training. McGovern et al. (2012) argue that probably
more brain areas than the early visual cortex might be involved in
perceptual learning. The linear trend in patients of the signal in
the PRL associated area in early visual cortex according to training is expressed in an increase of signal from the second to the
third fMRI session. When we restricted our analysis to the subgroup of patients with high fixation stability (≥60%), we found a
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Plank et al.
FIGURE 8 | Correlations between the difference in percent signal
change (before the first and fourth training session (“during”), left
panel, as well as before the first and after the sixth training
session (“after”), right panel, and initial fixation stability for the
trained PRL location (A) or the untrained OppPRL location (B) for
patient data only (n = 12). As in Figure 6, squares correspond to
values from JMD patients, diamonds to those from AMD patients.
significant increase of BOLD response in early visual cortex with
training. This result is consistent with several other studies which
report an increase in neural signal in early visual cortex with training. Frank et al. (2014) show also an increase of percent signal
change over learning sessions while subjects trained in a challenging perceptual learning task. The time course of the neural signal
referring to the trained location in early visual cortex in the control
group follows the pattern observed in the study by Yotsumoto et al.
(2008) who also used a TDT. Similar to the trend of the present
results for the control group, they found an increase of signal from
the pre-training session to the second fMRI session followed by a
slight decrease of signal in the post-training session. Interestingly
this was only the case for the PRL associated area in early visual
cortex where subjects received training and not for the untrained
OppPRL associated area. As described in the Introduction, the
increase in BOLD signal observed in the initial phase of learning
suggests the recruitment of respective brain areas in early visual
cortex (Yotsumoto et al., 2008). The decline in the BOLD signal
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Perceptual learning in macular degeneration
Correlation coefficients for the trained PRL location are r = 0.155
(p = 0.629) for the difference in percent signal change “during–before,”
and r = 0.531 (p = 0.075) for the difference in percent signal change
“after–before.” For the untrained OppPRL location correlation coefficients
are r = 0.042 (p = 0.896) for the difference in percent signal change
“during–before,” and r = 0.444 (p = 0.148) for the difference in percent
signal change “after–before.”
would accordingly correspond to a consolidation process. In our
study the control subjects appeared to have reached the consolidation phase already after the first post-training session, while
patients still showed an increase in BOLD-signal up to the second
post-training session.
Considering the patients’ fixation stability there was on the one
hand a significant positive correlation between fixation stability
and hit rate (difference during and before training) if the target appeared at the position of the PRL and on the other hand
a positive correlation between fixation stability and percent signal change (difference after and before training) if the target was
located in the PRL projection zone in early visual cortex, that
just failed to reach significance. There was further a significant
effect of session when three patients, who exhibited extremely
poor fixation stability, were omitted from analysis. This finding suggests that fixation stability might be a prerequisite for a
successful learning curve in perceptual learning. Moreover, other
visual tasks seem to be affected by fixation stability. Plank et al.
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Plank et al.
(2013) reported that patients suffering from hereditary macular
dystrophies (JMD) with stable eccentric fixation performed better in a visual search task than patients with less stable eccentric
fixation. Interestingly this was also the case, if the target stimuli
were not in or near the position of the PRL. Fixation stability has
also usually been shown to be positively correlated with reading
speed in patients with central vision loss (e.g., Sunness et al., 1996;
Trauzettel-Klosinski and Tornow, 1996; Nilsson et al., 1998; Nilsson et al., 2003; Crossland et al., 2004; Rubin and Feely, 2009).
Please note that, since eye movements were not recorded during
fMRI sessions, we had to assume that the level of fixation stability
measured during psychophysical testing was also evident during
fMRI testing.
The FrACT sensitivity (Bach, 1996) revealed a training associated improvement in the patient group for the Vernier subtest.
However, it should be noted that the significance level of this
effect does not survive correction for multiple testing, suggesting
that caution must be exercised here and that further studies are
warranted. The other tasks seemed not to be influenced by the
training intervention. The reason for the marginal improvement
in the Vernier task might be due to the similarity among the stimuli
in the TDT and the Vernier task.
With respect to the transfer of TDT training the findings
reported above suggest that caution should be exercised when
interpreting their implications with respect to potential application in visual rehabilitation. Obviously studies with larger patient
samples are required that assess the amount of transfer of perceptual training at the PRL to other visual functions. The addition of a
“sham” training group would establish the extent to which placebo
effects influence perceptual learning in select patient groups. With
respect to the effects of oculomotor and eccentric-fixation training
in a similar patient group, we could recently rule out that the beneficial effects of training could be explained by a general placebo
effect (Rosengarth et al., 2013).
Earlier studies have pointed to a persistence of perceptual learning effects. Polat et al. (2004) found a two to fourfold increase in
contrast sensitivity in the amblyopic eye of trainees 12 months
after training on a flanker-task had ended. Our group has recently
shown that in healthy participants the effects of perceptual learning of a difficult conjunction visual search task are still evident at
9-month follow-up (Frank et al., 2014). We are currently retesting
the patients and controls of the present study with respect to this
aspect of the results (Plank et al., unpublished observations).
With respect to the results of the VFQ, patients exhibited higher
scores after training on the category of social functioning, which
considers personal contact und communication with other people. Šiaudvytytė et al. (2012) report differences in quality of life of
AMD patients compared to age-matched control subjects in several categories of the VFQ including social functioning. Possible
implications of these trends require further investigation in larger
patient samples.
CONCLUSION
In this study we trained patients with central vision loss in a TDT,
with the target appearing on their respective PRL, and compared
their results to an age-matched normal sighted control group. We
were also interested in the neural correlates of the learning process
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Perceptual learning in macular degeneration
in the visual cortex. Although the task appeared to be more difficult for the patient group than for the control group, patients were
able to do the task and showed significant learning effects. Patients
with stable eccentric fixation showed better performance accompanied by a larger increase in BOLD-signal in the PRL projection
zone of the early visual cortex. Owing to our strict inclusion and
exclusion criteria with respect to disease manifestation in the study
and companion eye of our patients, our results are limited to the
present patient sample, thereby demanding further verification of
beneficial effects of perceptual training in patients with different
forms of macular disease. Nevertheless, the present results support
the idea that perceptual learning can improve the efficient use of
the PRL location in patients with central vision loss.
ACKNOWLEDGMENTS
This work was supported by the Deutsche Forschungsgemeinschaft within the framework of Research Group FOR 1075:
Regulation and Pathology of Homeostatic Processes in Visual
Function. The authors thank Susanne Hammer for her help
with data collection and the City of Regensburg (Senior Citizens’
Office) for their assistance in participant recruitment, as well as all
participants of our study.
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Conflict of Interest Statement: The authors declare that the research was conducted
in the absence of any commercial or financial relationships that could be construed
as a potential conflict of interest.
Received: 11 July 2014; accepted: 01 October 2014; published online: 17 October 2014.
Citation: Plank T, Rosengarth K, Schmalhofer C, Goldhacker M, Brandl-Rühle S
and Greenlee MW (2014) Perceptual learning in patients with macular degeneration.
Front. Psychol. 5:1189. doi: 10.3389/fpsyg.2014.01189
This article was submitted to Perception Science, a section of the journal Frontiers in
Psychology.
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