Perceptual learning: A case for early selection

Abstract

Perceptual learning is any relatively permanent change of perception as a result of experience. Visual learning leads to sometimes dramatic and quite fast improvements of performance in perceptual tasks, such as hyperacuity discriminations. The improvement often is very specific for the exact task trained, for the precise stimulus orientation, the stimulus position in the visual field, and the eye used during training. This specificity indicates location of the underlying changes in the nervous system at least partly on the level of the primary visual cortex. The dependence of learning on error feedback and on attention, on the other hand, proves the importance of top-down influences from higher cortical centers. In summary, perceptual learning seems to rely at least partly on changes on a relatively early level of cortical information processing (early selection), such as the primary visual cortex under the influence of top-down influences (selection and shaping). An alternative explanation based on late selection is discussed.

Full text

Journal of Vision (2004) 4, 879-890 http://journalofvision.org/4/10/4/ 879
Perceptual learning: A case for early selection
Manfred Fahle
Institute of Brain Research, Human Neurobiology,
University of Bremen, Germany
Perceptual learning is any relatively permanent change of perception as a result of experience. Visual learning leads to
sometimes dramatic and quite fast improvements of performance in perceptual tasks, such as hyperacuity discriminations.
The improvement often is very specific for the exact task trained, for the precise stimulus orientation, the stimulus position
in the visual field, and the eye used during training. This specificity indicates location of the underlying changes in the
nervous system at least partly on the level of the primary visual cortex. The dependence of learning on error feedback and
on attention, on the other hand, proves the importance of top-down influences from higher cortical centers. In summary,
perceptual learning seems to rely at least partly on changes on a relatively early level of cortical information processing
(early selection), such as the primary visual cortex under the influence of top-down influences (selection and shaping). An
alternative explanation based on late selection is discussed.
Keywords: visual perception, plasticity of visual function, specificity of learning, error feedback, orientation dependence,
monocular learning, interocular transfer
Introduction
Perceptual learning: Hyperacuity
as a sensitive probe
Perceptual learning is any relatively permanent change
of perception (usually improvement as measured by
changes in perceptual thresholds or brain physiology) as a
result of experience. The specification “relatively perma-
nent” distinguishes perceptual learning from sensitization
(and habituation) as well as from priming, which denote
more transient changes in perception. In contrast to classi-
cal conditioning, perceptual learning involves individual
stimuli rather than the association of two or more stimuli,
and is not restricted to one specific response, as in operant
conditioning. Perceptual learning clearly is of the implicit
or procedural type: it does not lead to conscious insights
that can be (easily) communicated, as is the case in declara-
tive, or factual learning. The brain circuits storing facts and
events (episodes) seem to at least partially differ from those
analyzing the outer world. Hence, in amnesic syndromes,
scenes may be analyzed without subsequent memory (e.g.,
after lesions of the hippocampal formation). Perceptual
learning, on the other hand, seems to change the very cor-
tical circuits solving the perceptual task trained. In this re-
view, I will present results suggesting that perceptual learn-
ing is (a) very specific for elementary attributes of the stimu-
lus, such as its orientation, and (b) able to change signal
processing even on the level of primary sensory cortices that
were considered as “hard wired” in adults in the not too
distant past.
The perceptual task employed to test perceptual learn-
ing in most of the experiments reported here is vernier
acuity, a type of visual hyperacuity (Wülfing, 1892;
Westheimer, 1976). In these hyperacuity tasks, even un-
trained observers can attain thresholds around 10 arcsec.
(These are thresholds calculated according to the conven-
tional definition, while the appropriately calculated thresh-
olds are a factor of 2 higher, cf. Harris & Fahle, 1995).
These thresholds are at least slightly below the spacing of
foveal photoreceptors, and through training, they can im-
prove by up to a factor of 5 (i.e., to 2 arcsec in especially
gifted and trained observers).
Obviously, performance in these tasks is not deter-
mined primarily by the optics of the eye nor by the photo-
receptor spacing, though both factors are important be-
cause they ensure that the requirements of the sampling
theorem for complete, high-resolution reconstruction of
the original stimulus are met (cf., Barlow, 1981; Crick,
Marr, & Poggio, 1981). Performance is instead limited by
the signal-to-noise ratio of the information reaching the
cortex and by the precision and selectivity of cortical proc-
essing. Hyperacuity is a good choice to study learning proc-
esses in visual perception because it is a very sensitive
measure based on cortical processing. Moreover, hyperacu-
ity is not some freak ability of over-trained laboratory ob-
servers but can be achieved even without specific training,
and simultaneously at many positions in the visual field
(Fahle, 1991).
Results
Specificity of perceptual learning for stimu-
lus orientation, position, and eye trained
In a first series of experiments, we investigated the
specificity of perceptual learning on low-level features of the
stimuli, such as orientation, position in the visual field, and
the eye used during training. Observers usually sat 2 m or
further away from an analogue monitor (HP or Tektronix),
controlled by a Power-Mac computer via custom-made high-
speed 16-bit D/A converters with an output rate of 1 mega-
pixel/s. Vernier stimuli consisted of thin (1 arcmin), bright
greenish lines (around 50-400 cd/m
2
) on a dark surround.
doi:10.1167/4.10.4 Received April 8, 2002; published October 26, 2004 ISSN 1534-7362 © 2004 ARVO

Journal of Vision (2004) 4, 879-890 Fahle 880
Each of the vernier elements was usually about 10-arcmin
long and presented for 100-150 ms. Observers had to indi-
cate in a binary forced-choice task without time pressure
(maximum 5-s reaction time allowed) whether the lower (or
left for horizontal verniers) element was offset to the right
or left (respectively up or down) relative to the upper (right)
element. Observers had to indicate their choice by pressing
the appropriate one of two push buttons. Usually, we re-
corded the number of correct responses for a vernier with a
fixed offset size for experiments consisting of only two ses-
sions, whereas thresholds were measured in experiments
with more sessions. A staircase procedure (PEST; Taylor &
Creelman, 1967) controlled vernier offset size in these ex-
periments where thresholds were defined as 75% correct
responses. Individual sessions lasted for about 1 hr, usually
consisting of 20 blocks of 80 stimulus presentations each
for the experiments with fixed vernier offsets. Only one
session per observer took place each day, with sessions fol-
lowing each other in intervals of no more than 3 days, usu-
ally on subsequent days.
It turned out that observers, on average, significantly
improved performance in a standard vernier discrimination
task, though there was high inter-observer variance (Fahle
& Edelman, 1993). Typically, performance improved fast
initially and slower after about 10-20 min of training (see
Figure 1). Earlier, we had found that the improvement in
vernier discrimination through learning did not generalize
to a stimulus rotated by 90° (Poggio, Fahle, & Edelman,
1992). In the present experiment, by stepwise reducing the
rotation of the stimulus after the first training session in six
subsequent groups of observers, we found that stimulus
rotation by as little as 10° was sufficient to reduce perform-
ance to baseline (i.e., there was no generalization of im-
provement in vernier discrimination from one stimulus to
a stimulus rotated by as little as 10°: learning had to start
from scratch for the new orientation) (Figure 1). Even after
a stimulus rotation of 4°, performance decreased slightly
and full transfer occurred only for rotations of no more
than 2° (results not shown).
Improvement is similarly specific for position in the
visual field. Eight observers practiced vernier discrimina-
tion sequentially at eight positions in the visual field, in
pseudo-random order. These positions were all located on
an imaginary circle around the fovea with a radius of 10°
(i.e., all stimuli were presented at 10° eccentricity). Observ-
ers practiced vernier discrimination for 1 hr at each of the
positions while fixation was monitored. During this time,
they improved performance, on average, by 7 % (e.g., from
80% to 87% correct responses) (Figure 2). However, when
stimuli were presented at a new visual field position, per-
formance dropped by 7%, hence there was no transfer at all
between these visual field positions. Learning, it seems, is
highly specific for location in the visual field, and even
these rather regularly spaced locations that had a distance
of no more than (2 × 10 ×
π
/8) ≈8° from their nearest
neighbors did not show any sign of being able to transfer
the improvement achieved during training. These results
60
70
80
90
0.5 1 1.5 20
time (h)
performance
(% correct)
±5° =11
n
Figure 1. No transfer of improvement through learning afte
r
stimulus rotation by 10 deg. Eleven observers practiced vernie
r
discriminations with a stimulus slanted by 5 deg relative to the
vertical. Performance (i.e., the percentage of correct responses)
improved within 1 hr of training. On the next day, a single bloc
k
of presentations at the old orientation (point immediately left o
f
vertical red line) proved that performance remained constant
over night. When the stimulus was rotated by 10 deg to a slant o
f
5 deg in the opposite direction, performance dropped to pretrain-
ing levels (first point to the right of vertical line). The first orienta-
tion was retested at the end of the experiment. Means and SEs
of 11 observers (after Fahle, 1998).
-15
-10
-5
0
5
10
15
mean
position 8
position 7
position 6
position 5
position 4
position 3
position 2
position 1
=8
constant position
change of position
change
of performance
(%)
1
2
3
4
5
6
7
8
FP
10 deg
n
Figure 2. Specificity of perceptual learning for the visual field
position trained. Eight observers practiced vernier discrimina-
tions sequentially at 8 positions at 10º distance from the fovea.
A
t each position, their mean performance improved during the 1
hr of training at each position by, on average, 7% (with one ex-
ception: position 4). But when proceeding to the next visual field
position, performance dropped by roughly the same amount.
Hence, improvement did not transfer between different visual
field positions (after Fahle, Edelman & Poggio, 1995).

Journal of Vision (2004) 4, 879-890 Fahle 881
indicate that improvement through perceptual learning is
also very specific for position in the visual field. A number
of different perceptual learning tasks showed a similar
specificity for visual field position (Dill & Fahle, 1997; cf.,
however, Beard et al., 1996).
In the third experiment, a new group of six observers
practiced vernier discriminations monocularly, starting
with either the left or the right eye patched for five sessions.
Observers improved monocular performance during these
first five sessions, similar to that seen in binocular im-
provement. At the start of the sixth session, the contralat-
eral eye was patched instead. Improvement did not transfer
to the previously patched eye; hence, learning was specific
even for the particular eye trained (Figure 3). Several other
groups found a similar specificity of perceptual learning for
the particular eye and visual field position trained in com-
pletely different tasks (e.g., Karni & Sagi, 1991).
Specificity of perceptual learning for differ-
ent tasks based on orientation cues
Discriminating the orientation of short line elements
obviously relies on some form of orientation discrimina-
tion. The same seems to be true for vernier discrimination
(see Watt, 1984) and also for curvature detection (see
Kramer & Fahle, 1996).
Eighteen observers were divided into six groups who
practiced vernier, curvature, and orientation discrimination
for 1 hr each in counterbalanced order. Each of the three
parts of Figure 4 shows the results of three groups of six
observers, each group practicing one of the three tasks for 1
hr each during each of the sessions. It is obvious that ob-
servers improved through training in each of the sessions
but that the improvement did not transfer to another task
(cf., Fahle, 1997). Moreover, separate analysis of the data of
each of the six groups revealed no indication of transfer
between any pair of tasks.
= 73.032 + 0.255 ( ± 0.065)
r
2
= 0.472; = 0.0012
= 73.858 + 0.172 ( ± 0.07)
r
2
= 0.263; = 0.025
mean slope:
0.156 ( ± 0.106) = 0.024
mean slope:
0.274 ( ± 0.107) = 0.030
mean slope:
0.170 ( ± 0.093) = 0.033
= 75.516 + 0.172 ( ± 0.068)
r
2
= 0.261; = 0.0214
70
75
80
85
60
block number
session 1
session 2 session 3
performance
(% correct)
p
p
p
SE
SESE
yyy
SE
SESE
x
x
x
ppp
α
d
d
d
α
α
20
40
30
10
50
0
=6
5
10
15
20
25
010203040
block number
threshold (arcsec)
R.E.
L.E.
L.E.
R.E.
n
Figure 3. Specificity of perceptual learning for the eye used dur-
ing training. Half of observers practiced vernier discrimination
with the left eye patched, whereas the right eye was patched fo
r
the second half of observers. After 5 days of training for 1 h
daily, the contralateral eye was patched during training. Thresh-
olds had improved significantly over the first 5 days, but in-
creased with an overshoot when the patch was moved to the
contralateral eye (after block 22). Means and SEs of six observ-
ers (after Fahle in Fahle & Poggio,
Figure 4. Upper panel. Not only orientation discrimination but
also vernier offset and curvature discrimination can be based on
orientation cues. Here, the discrimination is between a slant to
the right versus a slant to the left. Lower panel. Performance as
a function of practice in three hyperacuity tasks based on dis-
crimination of orientation cues: vernier, orientation, and curvature
discrimination (after Fahle,
2002).
1997).The same number of observers
practiced each of these tasks in each of the sessions; hence, the
stimulus condition is counterbalanced between sessions. In each
session, observers significantly improve performance, and retain
this improvement on the first block of the next session, usuall
y
on the next day (first data point to the right of the first two vertical
lines). Changing to a new type of orientation-discrimination tas
k
(second points to the right of lines) decreases performance to
baseline level. The very last data point tests performance for the
condition of the first session. Mean results and SEMs for 18 ob-
servers. Insets give slopes and correlation factors of linear re-
gressions through the data points of each session.

Journal of Vision (2004) 4, 879-890 Fahle 882
Perceptual learning without (explicit)
memory: Amnesic patients
A recent study of six patients suffering from amnesic
syndrome supports the hypothesis of an involvement of
relatively low levels of cortical processing in perceptual
learning, rather than a purely cognitive level of learning.
Testing of the patients was similar to that of normal ob-
servers, apart from the fact that the intervals between the
response of the patient and the next stimulus presentation
were 3-4 s rather than 0.5 s as with the normal observers.
Moreover, patients indicated the direction of offset ver-
bally, and the experimenter then pushed the corresponding
buttons without himself seeing the stimuli. Two of the six
patients tested clearly improved performance within as few
as 2 sessions with 5 blocks of 80 presentations each, tested
at a 1-week interval (i.e., receiving less than half the number
of stimulus presentations used to train normal observers)
(Figure 5; Fahle & Daum, 2002). Another two patients im-
proved somewhat, whereas performance of the remaining
two patients stayed constant, similar to the results of about
15% of the healthy student population of around 300 ob-
servers we tested so far. Although after the 1-week gap fol-
lowing the first session the patients did not recollect that
they had ever before participated in such an experiment
(and did not remember the experimenter), the performance
of the six patients as a group improved significantly as a
result of training. This finding indicates that perceptual
learning does not require normal function of the neuronal
circuits underlying explicit or declarative learning.
The role of attention
Does improvement through training in perceptual tasks
require attention or is it automatic, that is, based on mere
stimulus presentation? A recent study reports that perform-
ance for detecting the predominant direction of dot mo-
tion improves even if this motion is not consciously per-
ceived. Hence improvement can be independent not only
of attention to the stimulus but also of conscious percep-
tion (Watanabe, Náñez, & Sasaki, 2001)!
In hyperacuity learning, on the other hand, attention
certainly seems to play an important role. When two
vernier stimuli are presented simultaneously, resembling a
cross, and only one of the verniers is attended, offset dis-
crimination only for this stimulus improves over the course
of training. Half of observers started by indicating the offset
of the horizontal vernier (up versus down; cf., Figure 6;
Herzog & Fahle, 1994), whereas the other half attended to
the vertical vernier and indicated its offset (left versus
right). After 1 hr of training, observers’ tasks were ex-
changed: those initially responding to the offset of the hori-
zontal vernier now attended to the vertical one and vice
versa. Performance dropped at this transition, though the
stimulus had not been changed at all, just the task was dif-
ferent (Figure 6): an argument against motor improvement
(of accommodation or fixation; see below) as the basis of
perceptual learning. On the other hand, changing the mo-
tor instructions (press left button for right offset and vice
versa) yielded perfect transfer of improvement (data not
shown). Hence, the mere presentation of the stimulus ele-
ments was not sufficient for improvement, but the ele-
1
10
100
RE
1
10
100
1000
AS
10
100
1000
10000
MH
HW
0 1 2 3 4 5 7 8 9
1
10
100
block number
threshold
(arcsec)
JR
HS
0123456789
10
10
6
0 1 2 3 4 5 7 8 9
10
6
Figure 5. Improvement in a vernier discrimination task in six am-
nesic patients (after Fahle & Daum, 2002). Six patients suffering
from amnesic syndrome practiced vernier discriminations for 5
blocks with 80 presentations each and for another 5 blocks 1
week later (gap between blocks 5 and 6 indicates 1-week inter-
val). Thresholds of two patients (RE and MH) improved dramati-
cally as a result of training, whereas those of the remaining fou
r
patients either improved somewhat (JR and HW) or not at all (AS
and HS). Hence, at least some amnesic patients are capable o
f
perceptual learning.
block number
performance
(% correct)
Figure 6. Two vernier stimuli are combined. Observers start with
discriminating offset directions of either the horizontal or the ver-
tical vernier stimulus. After 1 hr of training (in the second ses-
sion), they respond to the perpendicular stimulus that had not
been attended to during the first session. This switch of attention
to another task decreases performance without any change o
f
the physical stimulus (after Herzog & Fahle, 1994). One of the
vernier targets is shown as a dotted line in each of the stimuli o
f
the graph only to indicate that this vernier was not attended to.
The physical stimuli were always solid lines in the experiments
and did not change over the course of the experiment.

Journal of Vision (2004) 4, 879-890 Fahle 883
ments had to be attended to yield improved performance,
in contrast to the results with random dot kinematograms
(Watanabe et al., 2001).
The role of feedback
Improvement through learning in hyperacuity tasks is
possible even in the absence of external error feedback
(McKee & Westheimer, 1978; Fahle et al., 1995) but often
is significantly faster if feedback is present. If only half of
the incorrect responses are followed by an error signal (in-
complete feedback), observer’s improvement is almost as
fast as with complete feedback where each incorrect re-
sponse leads to an error signal (Herzog & Fahle, 1997).
This finding poses difficult problems for neuronal net-
work theories of perceptual learning based on a teacher
signal, which allows the observer to classify each stimulus.
With partial feedback, half of the incorrect responses
would be classified as being correct, and this should
strongly decrease learning but it does not. (A possible rem-
edy would be to use only the error signals for response
modification, but this method would not be able to reliably
discriminate between correct versus incorrect responses.)
Random feedback signals, on the other hand, without any
correlation to the correctness of the response, effectively
prevented improvement through training if observers as-
sumed that they received correct error feedback (Herzog &
Fahle, 1997).
Improvement is about as fast with block feedback when
the percentage of correct responses is indicated after each
block of 80 presentations as it is for complete trial-by-trial
feedback (Herzog & Fahle, 1997). Most surprisingly, ma-
nipulating the block feedback in a way similar to the condi-
tion of random feedback, by presenting a number uncorre-
lated with the actual performance of the observer, also pre-
vents improvement (Herzog & Fahle, 1997). Hence, feed-
back can strongly influence the speed and extent of visual
learning, indicating that several top-down influences, not
just attention, must play a major role in this type of learn-
ing, even if it occurs partly on early levels of cortical infor-
mation processing.
The role of motor factors
Extremely high visual resolution is possible only at the
very center of the visual field, subserved by the foveola.
Resolution starts deteriorating at a distance of less than 1
deg from the center. So to achieve optimal performance,
targets for most tasks have to fall into this very center of the
fovea (but which is still more than 20-30 photoreceptor
diameters wide). This may not be the case for inexperi-
enced observers in the darkish experimental room under
somewhat artificial viewing conditions: Observers may not
be able to maintain a sufficiently precise fixation for the
duration of the whole experiment. Similarly, for optimal
retinal image quality, accommodation has to be very pre-
cise, and this may not be easy to achieve throughout a
whole session for inexperienced observers. So several skep-
tics argued that in reality, improvement through training
might be the result of motor learning: improvement of ac-
commodation, or fixation, or both. (After all, improvement
through training is based on motor improvement in many
forms of procedural learning.) These skeptics continued by
arguing that this motor improvement would be specific for
the stimulus and eye employed and hence disappear after
any change of orientation or eye used in the experiment.
A simple experiment ruled out this suspicion. Half of
observers started to practice a three-dot vernier discrimina-
tion task. Here, the task was to indicate, in the usual binary
forced-choice way, whether the middle dot was offset to the
right or left relative to an imaginary line through the two
end points. The other half of observers performed a three-
dot bisection task (i.e., they had to indicate whether the
middle one of three dots was closer to the upper or to the
lower end point). These two stimuli are very similar to each
other indeed: Thresholds for discrimination of vernier off-
sets are in the order of 10-15 arcsec, usually higher by a fac-
tor of around 2 for the bisection task. Hence, position of
the middle point differs by about 1 photoreceptor diameter
between a vernier offset to the left versus to the right and
similarly between displacement up versus down. Differ-
ences in the position of the middle dot are even smaller
between the stimuli for two tasks (e.g., between a middle
dot displaced up and one displaced to the right) (Figure 7
and Fahle & Morgan, 1996). According to the theorem of
Pythagoras, this difference between dot positions would
block number
performance
(% correct)
n
=12
Figure 7. Failure to transfer perceptual improvement between
virtually identical stimuli, due to task difference (after Fahle &
Morgan, 1996). Half of the observers started with a three-dot
vernier discrimination task; the other half of the observers started
with practicing a three-dot bisection task for about 1 hr. The tran-
sition between tasks is indicated by the thin vertical lines. The
next day observers exchanged tasks. There was no transfer o
f
improvement between the tasks, though the stimuli were virtuall
y
identical. The nearest data point to the left of the thin vertical line
(21st block) was recorded on the second day.

Journal of Vision (2004) 4, 879-890 Fahle 884
be
n
1
2
+ n
2
2
, if n
1
, n
2
are the displacements at threshold
for vernier and for bisection, respectively, or
2
n ≈1.4n
for n
1
= n
2
. The difference between displacements to the
left versus to the right, on the other hand, would be 2n.
Improvement does not transfer between the three-dot
bisection and vernier tasks. A repeated measures analysis of
variance with two within factors (block and sequence) on
the individual data of all observers yielded a significant dif-
ference between the first and second condition, with lower
performance during the second condition (76.8 +/– 0.84)
than during the first condition (80.8 +/– 0.75). These re-
sults clearly demonstrate that transfer of improvement may
fail even between virtually identical stimuli. The relevant
parameter seems to be the task required from the observer,
and motor components such as steady fixation or accom-
modation do not play an important role. Improvement of
any of these factors through training with either the three-
dot vernier or bisection task should not be disrupted by a
position change of the middle dot smaller than that be-
tween the two stimuli discriminated during the first part of
the experiment!
Discussion
What could possibly be the cortical mechanisms under-
lying the improvement of vernier discrimination through
perceptual learning? We saw above that improvements on
the motor side, as are common in many forms of proce-
dural learning, can be excluded. So we have to look for im-
provements on the sensory side.
Two straightforward lines of reasoning based on im-
proved sensory processing are able to explain the specificity
of perceptual learning. The first one emphasizes changes in
receptive field structure of specific cortical neurons,
whereas the second one emphasizes improvements in signal
selection. These lines of argument are not mutually exclu-
sive, but rather differ in the type of approach: more physio-
logical versus more formal. Clearly, changes in input selec-
tion of any neuron lead to a change in its receptive field
structure and simultaneously to changed signal selection, so
the two processes are intrinsically related.
For both lines of argument, the question arises con-
cerning the exact level of processing at which improvement
is achieved. This question concerning the neuronal level of
perceptual learning is, in many respects, similar to the one
regarding the location, in the nervous system, of the neu-
ronal process underlying stimulus selection based on atten-
tion. As with attentional processing, one could contrast an
early selection hypothesis of perceptual learning with a late
selection hypothesis (for a discussion on attention-based
selection processes, see Broadbent, 1958; Deutsch &
Deutsch, 1963; Johnston & Heinz, 1979).
Here I address this basic controversy regarding the cor-
tical level on which perceptual learning operates. It has
been argued that the specificity of perceptual learning indi-
cates an early level of the underlying cortical modifications
(e.g., Poggio et al., 1992), whereas this has been questioned
by others. Mollon and Danilova (1996; cf., also Morgan,
1992) pointed out convincingly that learning might take
place on levels beyond the primary visual cortex in spite of
the high stimulus specificity. These authors argued that
even though neurons on these higher levels are binocularly
activated, the activations stemming from each of the two
eyes might differ from each other (e.g., as a result of slight
differences in the photoreceptor geometry between the two
eyes). The controversy will be exemplified first in terms of
signal detection theory and second in terms of physiology
(i.e., receptive fields). Hence, in the following, I will discuss
both psychophysical and neurophysiological findings.
Improvement of signal detection:
Early selection
By selecting those signals discriminating best between
two stimuli while ignoring those that respond in a similar
way to both stimuli, the decision level can benefit from a
greatly improved signal-to-noise ratio and, hence, improve
performance (cf., Pelli, 1985; similar processes may happen
during childhood; cf., Andrews, 1964). This is to say that
perceptual learning might be based, to a certain amount,
on changing the weights with which individual inputs in-
fluence the overall reaction of the observer. Thus, by elimi-
nating the influence of uninformative inputs, the amount
of noise at the decision level is decreased.
Rejection (e.g., by inhibition) of the less relevant signals
could occur principally on all levels of signal analysis before
the decision stage. Two straightforward alternatives come to
mind: (1) changes in signal processing by early selection
(e.g., on a level where neurons are still monocularly acti-
vated but show orientation specificity); and (2) late selec-
tion with improvement of input selection and/or signal
processing on higher processing levels and lack of transfer
due to subtle changes of activation patterns elicited by, for
example, presentation to different eyes (for monocular
presentation).
Better and more appropriate selection and processing
of input signals on an early level is the most straightforward
explanation for the high stimulus specificity and the lack of
transfer between similar stimulus positions, orientations,
and between the eyes (Poggio et al., 1992).
But such a permanent change in input selection on an
early stage would interfere with other perceptual tasks.
Moreover, purely bottom-up driven modifications of input
selection cannot explain dependence of performance on
feedback and the lack of transfer between vernier, orienta-
tion, and curvature discrimination (see above and, e.g.,
Herzog & Fahle, 1998).

Journal of Vision (2004) 4, 879-890 Fahle 885
Improvement of signal detection:
Late selection
The second alternative, more adequate input selection
and processing of signals exclusively on higher processing
levels, would require that the inputs, in the case of mo-
nocular stimulus presentation, are too different to allow
generalization between the eyes (Mollon & Danilova, 1996;
an extreme form of this alternative would be input selec-
tion on the level of the decision process). This hypothesis
allows for incorporating the effects of attention and feed-
back. However, the crucial assumption is that the inputs
from one eye differ clearly from those of the other eye, and
that the inputs used at one stimulus orientation differ from
those used at a very similar orientation.
This alternative explanation has to cope with several
problems, too. In healthy observers, the retinal mosaic is
relatively similar in the two eyes and observers cannot, even
after training, indicate which eye was stimulated during
short monocular presentations (Helmholtz, 1867). At the
same time, the variance of the exact pattern of neuronal
activity evoked by a simple line stimulus must be enormous
in each of the eyes due to fixation instability and tremor.
Even under optimal fixation, observers move their eyes over
an area with a side-length of more than 1 arcmin, and
probably much more. Hence, the same stimulus (e.g., the
three dots presented for the three-dot bisection/vernier
discrimination task; Figure 7) will activate different groups
of neurons, to a widely differing amount, even if presented
repeatedly to the same eye. The amplitude of eye move-
ments is larger than the (vernier) offset to be detected.
Hence, it would be surprising if the higher cortical areas
were able to achieve such an impressive improvement of
performance on the basis of a highly variable monocular
input within a few hundred stimulus presentations. On the
other hand, observers are completely unable to make use of
this improvement when analyzing the ensemble of inputs
from the other retina when the partner eye is tested. These
considerations argue against any explanation of eye-specific
learning based on “labeled lines,” given the binocular na-
ture of most neurons beyond the primary visual cortex.
A possible solution: Top-down control
This indirect argument does not safely refute late selec-
tion. But the additional evidence of changes induced by
perceptual learning in early components (latency around 50
ms) of evoked potentials, especially over the occipital cortex
(Fahle & Skrandies, 1994), and the results of animal ex-
periments favor the early selection hypothesis. Moreover, I
would argue that selection is most beneficial if exerted on a
neuronal level as early as possible and that learning occurs
at the lowest appropriate level in the visual system (cf.,
Karni, 1996).
The primary visual cortex represents the visual world in
an ordered way with high positional precision and small
receptive fields, whereas receptive fields increase on later
levels of visual processing. Hence, suppression of all inputs
not activated by a target is essential to isolate this target
from nearby objects that could interfere with processing of
the target. Suppression would yield highest improvement if
exerted before the signals originating from the target con-
verge, on higher cortical levels, with the signals evoked by
other objects. Hence, I would advocate the first alternative:
selection of the optimal input signals on an early level un-
der top-down-control, combined with early modification of
processing. A change of processing on this early level must
occur, ideally in a task-specific way, under top-down con-
trol, changing, for example, lateral interactions between
neurons. Task-specific selection of the input best suited to
solve the task at hand would lead to temporary modifica-
tions of receptive field structure of at least some of the re-
ceptive fields on this level, and there exists an intimate rela-
tionship between input selection and cortical processing.
We will now consider possible neuronal implementations
for changes of input selection.
Neuronal mechanisms: Early selection
compatible with neurophysiology of
primary visual cortex?
The specificity for orientation, position, and especially
the eye trained immediately points to a specific location, in
the visual system, of the neuronal changes underlying per-
ceptual learning. Only in area 17, the primary visual cortex,
exist neurons sensitive both to stimulus orientation (unlike
those in the retina and the lateral geniculate nucleus
[LGN]) and the eye stimulated (cf., Figure 8). The most par-
simonious explanation for the results presented above is
that perceptual learning relies on an improvement in proc-
essing by those neurons in area 17 best suited to discrimi-
nate between verniers offset in opposite directions, hence
an early selection (see Poggio et al., 1992). If this explana-
tion of the experimental results is correct, then perceptual
learning would involve changes in connectivity between
neurons already on the level of the primary visual cortex.
This may be an implausible assumption because the
primary visual cortex was considered for quite some time to
be a hard-wired first stage of analysis (Marr, 1982). But
more recent electrophysiological evidence points to plastic-
ity even in the adult primary visual cortex (Gilbert & Wie-
sel, 1992; Eysel, Eyding, & Schweigart, 1998; Fahle &
Skrandies, 1994; Godde, Leonhardt, Cords, & Dinse,
2002), supporting the hypothesis that perceptual learning
may involve plasticity even of primary visual cortex. The
recent electrophysiological results demonstrating plasticity
of the adult primary visual cortex, therefore, fit nicely with
the assumptions developed on the basis of psychophysical
experiments demonstrating the specificity of perceptual
learning as cited above. To conclude, physiological knowl-
edge should no longer prevent us from speculating about
plasticity of the primary visual cortex, hence from assuming
modification of an early cortical level.

Journal of Vision (2004) 4, 879-890 Fahle 886
Neuronal mechanisms: Improvement of
orientation tuning on an early level?
Assuming that perceptual learning modifies receptive
fields in the primary visual cortex, as suggested by the hy-
pothesis of early selection, what type of modification would
we expect? Receptive fields of neurons in the primary visual
cortex typically consist of an elongated excitatory field cen-
ter determining the orientation specificity of the neuron
and of inhibitory surrounds on both sides of the center.
The neuron is most strongly activated by light falling on the
receptive field center without extending into the inhibitory
surround. The receptive fields of neurons with different
orientation preferences and slightly differing receptive field
positions are clearly able to discriminate between a straight
vernier and an offset one, or between an offset to the left
and an offset to the right (Figure 9; cf., Wilson, 1986). The
precision of discrimination depends, among other factors,
on the width of the receptive field center. So a straightfor-
ward and plausible hypothesis regarding the neuronal
changes underlying perceptual learning with vernier stimuli
would be that learning leads to permanently narrower re-
ceptive field centers and hence a narrower orientation
band-width on an early level, such as the primary visual
cortex as discussed (and rejected) by Herzog and Fahle
(1998) (Figure 9).
Optic
chiasm
Lateral
geniculate
nucleus
(LGN)
Optic nerve
Optic
track
LGN
Optic
radiation
RIGHT HEMISPHERE
position 1
position 2
position 2
position 1
Right visual field
Primary visual cortex
right eye
left eye
binocular
A general consideration and two specific examples ar-
gue against this hypothesis in its “feedforward” form. First,
a change of receptive field structure could have conse-
quences for the processing of virtually all visual stimuli,
with potentially deleterious effects for other visual tasks,
such as the detection of low-contrast stimuli. The receptive
field may become too small to detect small differences in
luminance. (For such reasons, Marr [1982] postulated a
hard-wired [i.e., nonplastic] early level of processing.)
The first example immediately follows from this con-
sideration. We know that the receptive fields in the primary
visual cortex are similar to the ones shown in Figure 9 – all
the information flowing to subsequent levels of analysis will
pass through these early filters. If the receptive field width
decreased as a consequence of perceptual learning, the sig-
nal-to-noise ratio and hence performance for orientation
Figure 8. Schematic diagram of the visual system indicating the
only location of orientation-specific monocular neurons. Neurons
in the retina and in the lateral geniculate nucleus (LGN) have
rotation-symmetric receptive fields; hence, they cannot discrimi-
nate between stimuli oriented at different angles and cannot
subserve orientation-specific learning. Only some of the orienta-
tion-specific neurons in the primary visual cortex are monocularl
y
driven, whereas neurons in higher visual projection areas are
usually binocularly driven and should be unable to discriminate
between separate stimulations of the two eyes. So the most par-
simonious explanation for eye specificity of perceptual learning is
based on the assumption that these monocular cells are involved
in the learning.
Figure 9. A simple hypothesis about the neuronal basis of visual
hyperacuity with vernier stimuli, postulating that improvement
through training may be leading to narrower receptive field cen-
ters. Neurons in the visual cortex have receptive fields with an-
tagonistic center-surround characteristics. Neurons are optimall
y
activated by stimuli restricted to their receptive field centers with-
out activating the surround. Narrowing of the field center means
that the neurons are better able to discriminate between different
stimulus orientations, and between offset directions. Most mod-
els use orientational mechanisms rotated by several 10s of de-
grees relative to the target orientation (off-center mechanisms
(cf., left and right parts of figure; Findlay, 1973; Mussap & Levi,
1996; see also Morgan, 1986).

Journal of Vision (2004) 4, 879-890 Fahle 887
discrimination would improve. In consequence, perform-
ance for all perceptual tasks relying on orientation dis-
crimination should ameliorate. Practicing vernier discrimi-
nations should lead to better orientation discrimination,
and vice versa, and practicing of both tasks should transfer
to curvature detection because for low curvatures, too, the
feature used for discrimination seems to be an orientation
cue (Kramer & Fahle, 1996). However, improvement
through training did not transfer between these three tasks
(see Figure 4).
The second example supporting the argument against
permanent modifications of receptive fields as the basis of
vernier learning is based on the specificity for stimulus ori-
entation. If learning relied on the narrowing of early, orien-
tation-sensitive receptive field centers, the improvement
should transfer to similar stimulus orientations, as long as
the same neurons are involved in the detection process.
This consideration raises the question about the orienta-
tion bandwidths of cortical neurons. These have not been
measured directly in humans, but neurons in the macaque
cortex show orientation-bandwidths of between 20° and
60° (Movshon & Blakemore, 1973). That is to say that neu-
ronal responses to an oriented bar are best for a defined
(optimal) orientation and decrease to half that value if the
stimulus is rotated by 10° to 30° to either side. (Neurons
with small receptive fields, i.e., with best resolution for fine
grating stimuli, show the most narrow orientation tuning).
As we saw in Figure 1, the orientation tuning of perceptual
learning is much finer than 20°. A rotation of the stimulus
by 10° suffices to require completely new learning at the
rotated stimulus orientation. This is a strong argument
against the hypothesis that perceptual learning is based
primarily on a permanent modification of receptive field
structure in early visual areas. According to this hypothesis,
training with a stimulus of a given orientation would lead
to a narrowing of a wide range of orientation-selective re-
ceptive fields, whereas we find a very strict orientation se-
lectivity of improvement.
In summary, perceptual learning of hyperacuity tasks is
not just a permanent sharpening in the orientation tuning
of the (relatively) peripheral orientation specific filters. The
improvement is specific for each of the different tasks based
on detection of differences in line orientation, and is highly
specific for stimulus orientation far beyond the bandwidth
of the cortical neurons subserving orientation discrimina-
tion. Hence, the assumption that continuously active modi-
fications of early receptive field modifications are exclusive
in achieving perceptual improvement in a strictly feed for-
ward system lacks plausibility.
Neuronal mechanisms: Modification
on a late cortical level?
We realize that perceptual learning is unlikely to rely
on the permanent modification of receptive field properties
of “early” cortical neurons (e.g., by sharpening their orien-
tation tuning). Dependence of perceptual learning on at-
tention and on feedback add plausibility to the view that
improvement cannot be based exclusively on exposure-
dependent bottom-up processes permanently changing sig-
nal processing in the primary visual cortex (i.e., for all vis-
ual tasks; see Herzog & Fahle, 1994, 1998).
Moreover, it is undisputed that learning can change proc-
essing of visual information on higher or more cognitive
levels of cortical information processing. Traditionally, the
effects of perceptual learning have been attributed exclu-
sively to changes on these levels. More recently, as the addi-
tional involvement of early levels became clear, the inter-
play between these different levels has been elaborated on
by classifying different types or levels of perceptual learning
(Ahissar & Hochstein, 1997). Hence, the question is not
whether or not perceptual learning involves higher cortical
levels (it does) but whether or not it additionally involves
the primary visual cortex?
What might be the changes of receptive fields on
higher levels of cortical signal processing? A number of dif-
ferent scenarios are possible. Basically, the neurons on
higher levels of processing may use more complex features
to discriminate vernier offsets to the right from those to the
left. Through training, they would learn which input neu-
rons on preceding or lower levels of cortical processing are
best suited to discriminate between those two classes of
neurons. Learning would consist, at least partly, in assuring
a higher impact of these neurons on the discriminating
neurons on the higher level and, hence, in changing the
receptive fields of these neurons in a task-specific way.
However, these higher level cortical neurons tend to be
binocularly activated and to possess large receptive fields.
Therefore, they would be expected to be less specific for the
exact stimulus parameters and be more disturbed by flank-
ing lines at close distance to the test stimuli (but see below
for a possible counter-argument based on labeled lines).
Let us summarize the findings based on the more
physiological approach to answer the question concerning
late versus early selection or change in receptive field prop-
erties. The high stimulus specificity of perceptual learning
with lack of transfer between very similar stimulus orienta-
tions and the lack of transfer between the two eyes as well
as the ability of observers to suppress nearby flanking lines
through training (Spang, Herzog, Holland-Moritz, Stein, &
Fahle, 2000) all support the argument in favor of plasticity
involving the level of the primary visual cortex. But the de-
pendence of learning on error feedback, the lack of gener-
alization between tasks based on orientation discrimina-
tion, as well as theoretical considerations, argue strongly
against plasticity on this early level. These considerations
against an early modification of receptive fields could be
invalidated by the postulate that the structure of receptive
fields would be adjusted to each task by top-down influ-
ences from higher cortical areas. A possible explanation of
the stimulus specificity of perceptual learning could then
rely on a change of receptive field properties of low-level
cortical neurons, in a task-specific way, under top-down
control. Some final arguments for this proposal follow.

Journal of Vision (2004) 4, 879-890 Fahle 888
Early versus late selection: Selected
evidence for early selection
It is hardly at all possible to isolate, in a complex recur-
rent system, the level on which a change occurs by means of
black-box methods such as psychophysics. In trying to re-
solve the controversy between early versus late learning, it
will be helpful also to consider the results of neurophysi-
ological studies. It is reassuring that the plasticity assumed,
on the basis of the psychophysical results (e.g., Poggio et al.,
1992), has a neuronal counterpart in the visual cortex (Gil-
bert & Wiesel, 1992; Fahle & Skrandies, 1994; Godde et
al., 2002) and auditory cortex (e.g., Recanzone, Schreiner,
& Merzenich, 1993; Menning, Roberts, & Pantev, 2000;
Tremblay et al., 2001), suggesting early selection to take
place.
Suppression of neuronal activation not relevant to
solve the task is especially important if, for example, flanks
are presented on both sides of vernier targets (cf., Spang et
al., 2000). Through learning, the influence of the flanks
can indeed be greatly reduced, a more difficult feat for neu-
rons with large receptive fields. Similar reasoning would
apply for neurons specialized for orientations different
from the stimulus and for those representing an eye not
stimulated under training conditions, making the assump-
tion of early selection of signals through perceptual learn-
ing even more feasible. Moreover, the psychophysical re-
sults of Watanabe et al. (2002) argue strongly in favor of an
involvement of early cortical levels in perceptual learning.
These authors find greater improvement through training
in lower level than in simultaneously trained higher level
visual motion processing in a perceptual learning task. Lo-
cal motion is processed at a very low level of motion proc-
essing, whereas global motion is processed at a higher level
stage by spatiotemporal integration. Hence, the learning
must take place on the lower processing level.
The hypothesis of early modification and selection of
visual input under top-down control seems to be best suited
to explain the psychophysical and electrophysiological find-
ings on perceptual learning. The psychophysical indicators
for plasticity in adult primary visual cortex agree well with
the results of electrophysiological experiments in humans
and animals. Both types of experiments provide the insight
that indeed we have to accept the notion of plasticity in
adult primary sensory cortices, because both the sum po-
tentials over the occipital pole of human observers and re-
ceptive field properties of single neurons in primary visual
cortex change as a result of training.
Conclusions
Visual perceptual learning leads to sometimes dramatic
and relatively fast improvements of performance in percep-
tual tasks, such as hyperacuity discriminations. The im-
provement often is very specific for the exact task trained,
the precise stimulus orientation, the stimulus position in
the visual field, and the eye used during training. This
specificity indicates location of the underlying changes in
the nervous system at least partly on the level of the pri-
mary visual cortex. The dependence of learning on error
feedback and on attention, on the other hand, proves the
importance of top-down influences from higher cortical
centers. In summary, perceptual learning seems to rely on
changes on a relatively early level of cortical information
processing, such as the primary visual cortex, under the
influence of top-down selection and shaping influences.
According to this view, the primary visual cortex is not a
hard-wired filtering device, but modifies its input signals in
a partly task-dependent way under top-down control. By
learning, new types of processing are implemented on this
early level. This conclusion is incompatible with older views
of primary sensory cortices assuming lack of plasticity in
adults, and is also incompatible with a strictly feedforward
signal processing in the cortex, while advocating a model of
information processing in a complex system with strong
feedback from higher to lower levels of processing.
Acknowledgments
Supported by the German Research Council Center of
Excellence (SFB 517). The author wishes to thank Michael
Morgan and John Mollon for constructive criticism.
Commercial relationships: none.
Corresponding author: Manfred Fahle.
Email: mfahle@uni-bremen.de.
Address: Institute of Brain Research, Human Neurobiol-
ogy, University of Bremen, Germany.
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