Journal of Vision (2007) 7(13):2, 1–10

http://journalofvision.org/7/13/2/

1

Effect of spatial distance to the task stimulus on
task-irrelevant perceptual learningof static Gabors
Shigeaki Nishina

Department of Psychology, Boston University,
Boston, MA, USA

Aaron R. Seitz

Department of Psychology, Boston University,
Boston, MA, USA

Mitsuo Kawato

ATR Computational Neuroscience Laboratories,
Kyoto, Japan

Takeo Watanabe

Department of Psychology, Boston University,
Boston, MA, USA

It was previously shown that sensitivity improvements to a task-irrelevant motion direction can be obtained when it ispresented in concurrence with observers’ performance of an attended task (A. R. Seitz & T. Watanabe, 2003; T. Watanabe,
J. E. Náñez, & Y. Sasaki, 2001). To test whether this task-irrelevant perceptual learning (TIPL) is speciﬁc for motion and toclarify the relationships between the observer’s task and the resultant TIPL, we investigated the spatial proﬁle of thesensitivity enhancement for a static task-irrelevant feature. During the training period, participants performed an attentionallydemanding character identiﬁcation task at one location, whereas subthreshold, static, Gabor patches, which were masked
in noise, were presented at different locations in the visual ﬁeld. Subjects’ sensitivity to the Gabors was compared between
the pre- and posttraining tests. First, we found that TIPL extends to learning of static visual stimuli. Thus, TIPL is not a
specialized process to motion stimuli. As to the effect of spatial location, the largest improvement was found for the Gaborspresented in closest proximity to the task. These data indicate that the learning of the task-irrelevant visual featuresigniﬁcantly depends on the task location, with a gradual attenuation according to the spatial distance between them. Theseﬁndings give further insights into the mechanism of perceptual learning.
Keywords: perceptual learning, task-irrelevant learning, spatial attention, internal reward, orientation discrimination, Gabor,
RSVP
Citation: Nishina, S., Seitz, A. R., Kawato, M., & Watanabe, T. (2007). Effect of spatial distance to the task stimulus on taskirrelevant perceptual learning of static Gabors. Journal of Vision, 7(13):2, 1–10, http://journalofvision.org/7/13/2/,
doi:10.1167/7.13.2.

Introduction
It is well-established that with training adults can show
significant improvements in various perceptual tasks(Fahle & Poggio, 2002), such learning effects are called
perceptual learning (PL). PL has been found to be highly
specific to basic stimulus attributes, such as retinotopic
location, angle of orientation, direction of motion, andeven to the eye of training (Ahissar & Hochstein, 1993; Ball
& Sekuler, 1982; Dosher & Lu, 1998; Fiorentini & Berardi,
1980; Herzog & Fahle, 1999; McKee & Westheimer, 1978;
Poggio, Fahle, & Edelman, 1992; Schoups, Vogels, Qian,& Orban, 2001). For example, in some cases, learning at
one location or of one orientation does not transfer toanother location or orientation.
Until recently, PL was thought to require attention to be
directed to the learned visual feature during training.
doi: 1 0. 11 67 / 7 . 1 3 . 2

However, a series of studies revealed the phenomenon oftask-irrelevant perceptual learning (TIPL), where thesensitivity improvements develop without attentional
focus toward the learned visual feature (Amitay, Irwin,& Moore, 2006; Dinse, Ragert, Pleger, Schwenkreis, &Tegenthoff, 2003; Ludwig & Skrandies, 2002; Seitz &Watanabe, 2003, 2005; Seitz, Náñez, Holloway, Koyama,& Watanabe, 2005; Watanabe, Náñez, & Sasaki, 2001).
For instance, Seitz and Watanabe (2003) reported an
improvement in sensitivity specific to task-irrelevant
motion stimuli that were subliminally presented in
temporal correlation with the target stimuli of thesubject’s main task. These results have led to a model ofPL that suggests that a featurally nonspecific learning
signal, which is triggered by successfully detecting thetask targets, results not only in learning of task-relevant
stimuli, but also in learning of task-irrelevant stimuli(Seitz & Watanabe, 2005).

Received March 25, 2007; published October 12, 2007

ISSN 1534-7362 * ARVO

Journal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

2Participants
Seven subjects (four female and three male, age range
18–35 years), who were naive as to the purpose of thestudy, participated and received payment for their completion of the experiment.

Apparatus

Figure 1. (A) Example of Gabor patches on random dot noisebackground with different Gabor contrasts. (B) The phases ofExperiments 1 and 2. Each subject performed pre- and posttests
for measuring Gabor sensitivity, before and after the 7-day training
sessions, respectively.

Although results of TIPL are highly suggestive of theexistence of a featurally nonspecific task-driven learning
signal, we know very little regarding the properties of thissignal. To better understand the signal, in the present
studies, we investigate two questions regarding TIPL.
First, is there any limitation to the spatial extent of TIPL?
Second, is TIPL a specialized phenomenon related toprocessing of motion stimuli (used in previous studies of
TIPL) or will TIPL hold true for other stimulus features,such as the orientation of a static Gabor stimulus?
To explore the spatial aspect and the generality of TIPL,
we presented subthreshold Gabor patches, which were
spatially masked in noise (see Figure 1A), at different
spatial locations while the subject performed an attentionally demanding character identification task. Our results
confirm that TIPL generalizes to static orientation stimuli
and suggest that there is a spatial restriction to the learningof these task-irrelevant stimuli.

The stimuli were presented using Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) for MATLAB\ (TheMathWorks, Natick, MA) on a Macintosh G4 computer.
The stimuli appeared on a Radius 21-in. CRT monitor
connected to the computer, with a resolution of 1,600 
1,200 pixels and a refresh rate of 60 Hz. The view
distance was 0.76 m and the pixel size was 1.13 arcmin. A
chin rest was used to maintain the subject’s head position.
The subjects used a computer keyboard to make responses.
Eye movements were measured for some subjects
during the training sessions using ViewPoint EyeTracker\
system (Arrington Research, Scottsdale, AZ). This eye
tracking system uses infrared video that has 0.15- spatial
and 60 Hz temporal resolutions.

Stimuli
The task-irrelevant stimuli were static Gabor patches
that were superimposed on a background that was filledwith spatial white noise (Figure 1A). We adopted Gaborswith static background noise because they are in many
ways analogous to the motion stimuli we have used inprevious studies (Seitz & Watanabe, 2003; Watanabe
et al., 2001). Also, in pilot experiments we found that thisstimulus yielded more gradual psychometric functions andmore within and across subject consistency than thoseobtained with contrast modulated Gabors in the absence of
background noise. Spatial frequency of the Gabors waseither 0.5 or 5.0 cycles/deg (counterbalanced acrosstrials), and the sigma of its Gaussian factor was 1.0-.

Experiment 1
In the first experiment, we examined the effect of task
location along a horizontal axis in the visual field(Figure 2). TIPL was compared between two locations;
one close and the other distant to the task. Based onclassical results of spatial and orientation specificity ofPL, we investigated how learning under different conditions develops at the same time in a within-subject
design. We measured performance improvement at different spatial locations and orientations independently andevaluated the effect of the distance from the task-relevant
stimuli by comparing changes in performance acrossconditions.

Figure 2. Schematic ﬁgure for spatial conﬁguration of visual
stimuli used in Experiment 1. The contrasts of the Gabors weresubthreshold in the actual experiment. The side of task isbalanced across subjects.

Journal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

Two spatial frequencies were used so that cells tuned to awide range of spatial frequencies would be stimulated andcould potentially contribute to effects of learning. Results
showed no systematic differences between the two spatial
frequencies used (no significant difference in average
performance in the pretest; p = .199, paired t test).
The noisy Gabor images were created by randomly
selecting 20% of pixels from the Gabor image and 80% of
pixels from the noise image. The background noise was
generated from a sinusoidal luminance distribution withthe exception that 20% of the pixels (same as signal-tonoise ratio of the Gabor) were chosen to be gray. In thisway, the statistics of the luminance distributions were
preserved between the Gabor and the background, so that
there were no texture elements that could distinguish theGabor patch from the noise field when the contrast of theGabor was brought to 0%. The mean backgroundluminance was 33 cd/m2, and the maximum luminanceof the display was 67 cd/m2 (luminance table shown in
Supplementary Table 1). The contrast of the Gabor usedin the training experiment was 12%, which was determined beforehand by a pilot experiment so that mostsubjects performed at chance level when attempting todiscriminate the orientation of this stimulus. We havefound in previous studies that choosing a single chancelevel signal value from the subject-average psychometricfunction is more reliable than choosing a different value
for each subject based on individuals’ psychometricfunctions, which can be highly unreliable especially at
the tails (Seitz and Watanabe, unpublished observations).
The background noise was redrawn every 300 ms, and theonsets and the offsets of Gabors were always synchronized
to the onsets of the background. In the test sessions, thecontrast values of the Gabor were chosen from the set(0%, 15%, 30%, 45%, and 60%), with the contrast rangeof the background set to 100%.

Procedure
The experiment consisted of 10 sessions; first a practicesession to acquaint subjects with the Gabor sensitivity
task, second a pretest, then seven training sessions, andfinally a posttest (Figure 1B). Each session was conducted
on a separate day.
Testing sessions

Sensitivity to the Gabor stimuli was measured before
and after the training phase for each subject using themethod of constant stimuli. In each trial, a Gabor pattern
was presented at one of the two locations (see schematicin Figure 2) for 300 ms, followed by a ring of lines
indicating the three possible orientations of the Gabor.
The orientations were 15-, 75-, and 135- clockwise
relative to the vertical line when presented in the rightvisual field, and mirrored orientations (j15-, j75-, andj135-) in the left visual field. They were centered at 3.0-

3apart from the fixation. The task consisted of a threealternative forced choice (3AFC) and the subject responded
by pressing a key corresponding to the perceived orientation. Each of the three orientations was presented equallyoften at the five contrast levels (including 0% contrast) andwith the two spatial frequencies. Each of these 30 conditions was repeated three times in each block. A session
consisted of 12 blocks of 90 trials, 1,080 trials in total.
Different Gabor contrasts were interleaved and locations of
the Gabor were blocked. Six blocks were used for eachlocation and block order was randomized.
Training sessions

In the training sessions, subjects were asked to performa peripheral rapid serial visual presentation (RSVP)
character identification task while maintaining fixation
on a dot presented at the center of the screen. Spatialconfiguration of the experiment is shown in Figure 2. Two
RSVP sequences were presented, one at the left and one at
the right side of the visual field. Subjects were directed toattend to one of the sequences and report target charactersof that sequence. The side of the task was randomly
chosen for each subject and instructed beforehand. For
each subject, the side of the task did not change throughthe entire training and the subjects could ignore theunattended character sequence. The centers of the circles
around the RSVP sequences were located at 5.0- apartfrom the fixation point. Thus, the distances from near and
far Gabors to the task RSVP were 2.0- and 8.0-,
respectively.
In each trial, the attended RSVP sequence consisted of
two digits as the targets and nine alphabets as the
distractors. At the end of each trial, subjects reportedwith key-presses the identity of the two digits in order ofpresentation. No feedback was given; as is typical instudies of TIPL (Seitz & Watanabe, 2003; Watanabe
et al., 2001). Potential confusion between characters (like1 and I) was avoided by removing such alphabets from theset of possible distractors. Each character in a sequence
was presented for 100 ms and the interval between
consecutive characters was 200 ms. The positions of thetarget digits in a sequence were randomized for each trial
with the constraint that the two targets could not appear
consecutively. Only the attended sequence contained
digits, and the unattended dummy sequence consisted ofonly alphabets.
Gabor patches were presented in the subthreshold
contrast at two spatial locations, which were positioned
between the central fixation and the two RSVP sequences.
During each trial, the two Gabor orientations, paired-withtarget and paired-with-distractor orientations, were presented. One of the two orientations was temporally pairedwith the two target digits and the other was paired with
two of the distractors. Temporal positions of the target andthe distractor that are paired with Gabors were randomly
and independently assigned for each trial (temporal

Journal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

distribution of the Gabors relative to the target digits isshown in Supplementary Figure 5). For each subject, andat each location, the orientation paired with target digits
and that paired with distractor alphabets was fixed. One of
the three orientations at each location was treated as acontrol and not presented in the training sessions. The
duration of Gabors was 300 ms, and they were presented
100 ms before the onset of paired letters. Thus, the paired
letters were temporally presented at the very center ofthe duration of Gabors. The training session consisted of
400 trials and lasted about 1 hr.

Results
For the RSVP training task, significant performance
improvement was observed across sessions, F(6, 6) =20.5, p G .001 (one-way ANOVA). Mean percent correct
was 61 T 7.1% (standard error of the mean, SEM, acrosssubjects) for the first session and 83 T 3.3% for the lastsession (see Supplementary Figure 1).
To evaluate changes in Gabor sensitivity, each subject’s
performance was evaluated before and after training on
six oriented Gabor stimuli (three at each of the two
locations). We expected the transfer of learning effects
between the two paired Gabors to be minimal due to thefact that they differed both in location and in orientation.
In this way, we could separately examine possible
learning effects for the Gabors paired at the near versus
far locations as well as those paired with the target versus
distractor characters.
The performance change was calculated as the summed
difference in performance across contrast levels between
pre- and posttests. Figure 3 shows the result for eachspatial location and pairing condition (raw psychometricfunctions are plotted in Supplementary Figure 2). A two-

Figure 3. Experiment 1, results. Improvement for each locationand orientation is shown. Error bars are the standard error of themean (SEM). Single star shows signiﬁcance of a post hoc test
(Tukey’s HSD, p G .05). Signiﬁcant improvement was found for theGabor that was shown at spatially closer to the task andtemporally paired with the target characters.

4

way repeated measures ANOVA (Distance  Orientation)
revealed significant effect of orientation, F(2, 12) = 7.33,
p G .01, and significant interaction of distance andorientation, F(2, 12) = 5.765, p G .02. A post hoc analysis(Tukey’s HSD, p G .05) showed significantly higher
improvement for the paired-with-target Gabor presented
near the task location.
To control for the possibility that these results are due toa spatial bias in eye movements toward the task-relevant
RSVP sequence, we measured the eye movements of twoof the subjects in Experiment 1 during the training
sessions. We found that the subjects were extremely goodat fixating their eyes to the central dot while performing
the task. We did not find any significant spatial bias of eye
movements that correlated with the presentation of thetask targets. Given that we examined eye movements inonly in a few sessions, we cannot entirely rule out apossible involvement of eye movements in the spatial
restriction of TIPL, but we find these to be an unlikely
explanation of our results.

Discussion
We found TIPL for the orientation of the static Gaborpatterns paired with targets of the subject’s RSVP task.
These results replicate previous findings of TIPL, where theperformance on a task-irrelevant feature improves even
without the subject’s knowing the presentation of thefeature. Importantly, we have shown for the first time that
TIPL occurs not only for the motion direction stimuli thathave been used in earlier studies, but also for staticorientation stimuli. These two types of stimuli (orientationand motion) are believed to be predominantly processed indifferent visual processing streams, and our results suggestthat TIPL is a general learning property of the visual system.
We also observed a larger learning effect for the Gaborstimuli presented closer to the task location. These results
indicate that the learning signal has a spatially limited
effect on irrelevant visual inputs. A number of factors
make us confident that this spatial restriction of TIPL is
not due to trivial effects of stimulus configuration or eye
position. First, as stated in the results, no positional bias ineye movements, either toward or away from the task
targets, was observed in the subjects for whom eye
movements were monitored. Second, the spatial configuration of visual stimuli is horizontally symmetrical, andthus we minimized asymmetries in stimulus interactions
or a spatial bias caused by exogenous attentional effects.
Third, the Gabors were presented as subthreshold (andsubjects were required to direct focused attention to thelocation of the RSVP task). Thus, we are thus confident
that subjects did not know that visual Gabor patterns were
presented during the training sessions. Altogether, thesefactors indicate that the spatial restriction of learning islikely due to either spatial effects of endogenous attention
and/or spatial effects of the hypothesized learning signal.

Journal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

The results of the present experiment show that thelearning signal has spatially limited effect on the irrelevant visual inputs. However, as only two locations were
examined, the detail of the spatial extent of such learning
signal is still unclear. Also, because the tested locations
spanned both visual hemifields, the results do notdisassociate whether the obtained specificity is indicativeof a relatively small spatial restriction of learning aroundthe task location, or a more global effect that covers thewhole hemifield. To address this question, we conducted
Experiment 2, where effects of TIPL were compared forthree locations within a single hemifield.

Experiment 2
In Experiment 2, we further investigated the spatial
specificity using a unilateral configuration of visual
stimuli, where the two letter sequences were presented in
the upper and lower quadrants of the same visual hemifield, and task-irrelevant stimuli were presented at threedifferent, equally eccentric, locations between the letter
sequences (Figure 4). The stimuli were arranged so that
their spatial locations were spatially symmetrical about
the horizontal axis.

Participants
Nine subjects (six female and three male, age range
18–35 years) who were naive as to the purpose of thestudy participated and received payment for theircompletion of the experiment. All subjects had normal
or corrected-to-normal vision.

Figure 4. Schematic ﬁgure for spatial conﬁguration of the visual
stimuli used in Experiment 2. In the actual experiment, thebackground was ﬁlled with random pixel noise (for details, see
Stimuli section of Experiment 2). In this example, the locations of
the two letter sequences and the three subthreshold Gabors areon the right side, and the task is at the upper location. Thoseconditions are balanced among subjects.

5

Apparatus
We used the same experimental apparatus as those usedin Experiment 1, with the exception that the monitor was a
ViewSonic VX922 19-in. LCD with resolution of 1,280 
1,024 pixels and minimum response time of 2 ms. The
monitor was adjusted so that the luminance range wasqualitatively matched to that of the CRT monitor used inExperiment 1. Given that a number of parameters havechanged between Experiments 1 and 2, only a qualitative
comparison of results across the experiments is valid. Our
main purpose in determining monitor settings is to achieve
reliable psychometric functions in the experiments.

Procedure
The experiment consisted of 10 sessions; first a practicesession to acquaint subjects with the Gabor sensitivity
task, second a pretest, then seven training sessions, andfinally a posttest (Figure 1B). Each session was completed
on a separate day.

Stimuli
Gabors were presented at one of the three possible
locations that were centered 3.0- apart from the fixation(see Figure 4). The middle location was horizontallyaligned to the fixation, and the other two were at thelocations T45- rotated around the fixation. The orientations used in this experiment were the same (15, 75, and
135 for the right side and j15, j75, and j135 for the leftside) as those used in Experiment 1. The sigma of theGaussian factor of the Gabor was 0.6-. Gabor pattern andbackground random dots were mixed so that 70% of
pixels were the background noise and 30% was Gabor. In
this experiment, 30% of the noise pixels were chosen to be
the intermediate gray value so as to avoid textural cues at
0% contrast. We used a slightly different signal-to-noise
ratio than those in Experiment 1 because the ratio of theGabor signal to the background noise used in Experiment 1
was too low for some subjects under the configuration of
stimuli used in this experiment. The new parameters were
determined based on a pilot experiment consisting onlyof a test session. The mean background luminance was
42 cd/m2, and the maximum luminance was 83 cd/m2(luminance table shown in Supplementary Table 2).
Testing sessions

In Experiment 2, we used a two-interval forced choice(2IFC) detection task to measure sensitivity at each of thethree locations and orientations of presentations via themethod of constant stimuli. A trial consisted of twoconsecutive stimulus presentations (300 ms each) with aJournal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

6delay interval (300 ms) between them. In each trial, aGabor pattern was presented at one of the three locations
in either the first or the second presentation interval. The
contrast values of the Gabor were chosen from the set(15%, 30%, 45%, 60%, and 75% contrast) for the signal
interval and 0% for the noise interval. The contrast rangeof the background noise was set to 100%. Subjects were
instructed to report the interval of Gabor presentation via
a keyboard response. A session consisted of 1,080 trials in
total and lasted about an hour.
Training sessions

The procedure of the training sessions was identicalto that of Experiment 1 with the exception that the
spatial configuration of the task-relevant and task-irrelevant stimuli (Figure 4). At each location, one of theGabor orientations was paired with target digits.
Another orientation was paired with distractor letters.
The third orientation was control and not presented
during the training sessions. The contrast of the Gaborpresented in the training sessions was 15%, which was
determined by a pilot experiment, so that most of thesubjects showed chance-level performance. The mean
performance for 15% contrast in the actual pretest was
53 T 1.8% (SEM across subjects). Thus, performance atthe exposed contrast level was approximately at chance,
and it was unlikely that subjects could have seen Gabor at
this level while paying intensive attention to the RSVP
task (subject debriefing confirmed that the Gabors went
undetected during training). The centers of two letter
sequences were 2.0- horizontally and 4.5- vertically apartfrom the fixation. The distances of near, middle, and far
Gabors from the RSVP task were 2.4-, 4.6-, and 6.6-,
respectively.

Figure 5. The results of Experiment 2. Improvement for eachlocation and orientation is shown. Error bars are the standard
error of the means (SEM). Double stars show that the improvement was signiﬁcantly higher than each of the other conditions.
Single stars show that the improvement was higher than each of
the no star conditions (Tukey’s HSD, p G .05).

the middle location. In summary, the improvement was
highly significant for the Gabors presented at closest
location to the task, and also significant at the intermediate location. Importantly, the improvement at the intermediate location was significantly smaller than the closest
location.
The post hoc test also showed a significantly higher
improvement in paired-with-distractor condition, when theGabor was presented closest to the target (Tukey’s HSD,
p G .05), this latter effect may be evidence of the temporalprofile of TIPL (see Discussion section).

Discussion
Results
Significant improvement was observed for the trainingtask, F(6, 8) = 6.8, p G .01 (one-way ANOVA). Mean
percent correct was 27 T 8.5% (SEM across subjects) for
the first session, and 68 T 8.1% for the last session (seeSupplementary Figure 3).
The changes in sensitivity between the two test sessions
for the task-irrelevant Gabors are shown in Figure 5.
A two-way repeated measures ANOVA (Distance 
Orientation) revealed significant effect for both distance,
F(2, 16) = 4.502, p G .05, and orientation, F(2, 16) =6.250, p G .01. The interaction of distance and orientation
was also significant, F(4, 32) = 5.720, p G .002. A post hoc
test (Tukey’s HSD, p G .05) was conducted for further
analysis, and it showed significantly high improvement for
paired-with-target Gabors presented at either near or
middle location. It was also shown that the improvement
at the near location was significantly higher than that at

The results of Experiment 2 confirm those of Experiment 1 showing a spatial restriction of TIPL. In Experiment 2, we further find evidence that the magnitude oflearning may fall off smoothly with distance from thelocus of the task.
An additional result was that significant improvement
was observed for the orientation that was paired-withdistractors in the location closest to the task locus. We
suggest that this result may provide an indication of thetemporal profile of learning. Namely, that learning may be
explained by the fact that target letters appeared temporally close to the Gabors paired with distractor letters. In
this experiment, the Gabors paired with distractors were
presented just before the onset of the target letter with
about .2 probability, and also just after the offset of thetarget letter with about .2 probability (for histograms of
temporal offsets between target characters and distractor
Gabors, see Supplementary Figure 5). In those cases, theinterstimulus interval between the target character and theJournal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

Gabor paired with the distractor character was only
100 ms. Thus, learning of the orientation paired with
distractors may be explained by a relatively broad
temporal profile of the learning signal. However, although
this explanation is sensible, we are cautious regarding thevalidity of the learning for the distractor Gabor given thata similar effect was not observed in the equivalentcondition of Experiment 1 nor in previous studies of
TIPL. The effect here is smaller than that found for thepaired-with-target Gabors and is unclear whether it is afalse-positive or whether it is due to methodologicaldifferences between this and other experiments. Moredetailed examination is required to clarify this point and to
explore the temporal characteristics of TIPL.

General discussion
In this study, we investigated how TIPL depends on
the relative spatial locations of task-relevant and taskirrelevant stimuli. In both Experiments 1 and 2, we used
an RSVP letter detection task, which required the subjects
to maintain intensive attention on the location of taskrelevant stimuli. Our results provide evidence that theeffects of TIPL fall off as a function of the distance
between the task-relevant target and task-irrelevant stimuli. The results also show for the first time that the TIPL
occurs for stimulus features other than motion directions
in a configuration in which attention is strictly controlled.
Namely, we found TIPL effects on the orientation of thestatic Gabor patterns to which subjects were exposed.
A key finding in this study is that TIPL was most robust
for the Gabor presented closest to the locus of the attended
task and fell off gradually from that point. Sensitivity to
the Gabors was significantly more enhanced when they
were presented in the same visual hemifield as the task inExperiment 1. In Experiment 2, three different locations inthe same visual hemifield as the task were examined, andwe found that learning was the greatest when the learned
visual feature was presented closest to the task, and thatthe amount of enhancement gradually decreased at moredistant locations.
It is noteworthy that unlike our previous studies
showing TIPL on motion, a significant performanceenhancement was found for the Gabor orientation pairedwith distractor characters, when it was presented at thelocation closest to the task. However, the enhancement
was weaker than that found for the Gabor orientations
paired with target characters. This result is in line with our
hypothesis that temporal relationship between task targets
and task-irrelevant features is important. A possibility isthat temporal window of the learning signal induced by
the successful detection of targets is so broad that thesignal may affect the Gabors presented temporally close to
the target characters (see Supplementary Figure 5). This

7

interpretation seems plausible but does not simply explain
why we did not find the similar effect in the Experiment 1.
Therefore, until replicated we remain cautious regarding
the validity of the learning effect found in paired-withdistractor condition. Further investigation will be required
to more clearly specify the temporal profile of TIPL.
Previous studies of TIPL have demonstrated thatlearning can occur for subliminally presented stimuli.
This also seems to be true in the present studies. Although
it is difficult to prove that the Gabor stimuli were at all
times truly subliminal, we have some confidence that
subjects did not perceive the Gabors while performing
the RSVP task. The subjects were required to direct
intense attention toward the task-relevant stimuli and thismade it difficult to attend to the location of the taskirrelevant stimuli. In addition, in the testing sessions ofExperiment 2, when the Gabors were task-relevant stimuli
and attention was directed to them, subjects were unable
to detect the Gabor stimuli at the contrast level presented
during the RSVP task (mean performance 53 T 1.8%
SEM). Furthermore, no subject reported noticing that theGabor patterns were presented during the training
sessions.
One might ask why TIPL is typically observed whenusing subthreshold stimuli. One explanation is that this isa result of the fact that TIPL is typically studied as an
attempt to show that learning can occur in the absence ofawareness (Seitz & Watanabe, 2005). However, otherstudies have found that task-irrelevant stimuli are notalways learned (Ahissar & Hochstein, 1993; Polley,
Steinberg, & Merzenich, 2006; Shiu & Pashler, 1992).
We have previously argued that other studies did not
manipulate the correlation between the task-relevant andtask-irrelevant stimuli and that these studies typicallyresemble our paired-with-distractor condition, whichusually shows no learning. However, a recent study
showed that activity in visual area MT+ showed peak
activation to perithreshold task-irrelevant motion signals
in the context of an RSVP task as compared to suprathreshold task-irrelevant stimuli. This result presents thepossibility that TIPL is most significant when subthresholdstimuli are used (Tsushima, Sasaki, & Watanabe, 2006).
Provided that perithreshold stimuli are used, the resultsof this and other studies of task-irrelevant learning support
the hypothesis that TIPL is not highly sensitive to theparameters of the stimuli. Studies of TIPL using motion
stimuli have found similar learning effects for motion
coherence algorithms using fixed-speed noise (Seitz &Watanabe, 2003; Watanabe et al., 2001) or white noise(Seitz, Lefebvre, Watanabe, & Jolicoeur, 2005) as well as
100% coherent, but low contrast moving-dots (Seitz et al.,2005). The current study adds to this by showing that
TIPL works for static orientation stimuli and is qualitatively similar under the different contrasts, signal-to-noise
ratios and monitor characteristics (CRT vs. LCD).
Although altogether this still represents a limited rangeof stimulus conditions, our collected results show that

Journal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

different strategies of degrading the perception of the taskirrelevant stimuli can be used to achieve TIPL. Further
research will be required to explore the relationshipbetween the saliency of the task-irrelevant stimuli, effectsof stimulus parameters, and the degree and the quality ofsubsequent learning.
What is the underlying mechanism that leads to a
spatially limited profile for TIPL? One possible interpretation is that TIPL results from a learning signal that has aspatially limited extent. Seitz and Watanabe (2005)
proposed a model to explain both task-irrelevant andtask-relevant learning in which task-related signals (due toeither external or internal factors) serve to reinforce
activity in low-level sensory processing stages in a
stimulus nonspecific manner. A possible brain mechanism
could be related to some neuromodulators released bysuccessful performance of the task modulating PL.
Although these learning signals have been previously
considered to have broader spatial extent, the present
results may provide evidence that these learning signals
may be more focused than previously thought.
Another possibility is that the learning signal itself isbroad but another process, such as attention, interacts withthis to produce a spatial restriction of learning. For
instance, attention may operate to enhance activity tostimuli presented in proximity of the task-relevant stimuli
or may suppress activity related to more distal stimuli.
Such a possibility seems likely given that attention is wellknown to evoke spatially restricted effects (Eriksen & St.
James, 1986; LaBerge, Carlson, Williams, & Bunney,
1997; Müller, Mollenhauer, Rösler, & Kleinschmidt,
2005; Posner, 1980). In addition, although TIPL ischaracterized by the fact that it does not require thelearners to recognize the learned visual feature, it has beensuggested that attention toward an accompanying task
serves to regulate PL (Seitz & Watanabe, 2005).
We showed the TIPL occurs for static Gabor stimuli.
However, we cannot rule out the possibility that some
aspects of the underlying mechanisms for the current
results are different from the TIPL on motion. Perceptionof Gabors and characters both involve processing oforiented line segments, whereas random-dot motion
perception does not. The interaction between the lettertask and the learning of Gabors found in the present study
could be a result of attentional modulation to such featuralprocessing. If that is the case, testing spatial extent using
motion stimuli could show a different result.
A natural question in evaluating these data is whichaspects can be attributed to attentional processes andwhich are related to reinforcement learning signals? Wehave previously suggested that these potentially disparate
accounts of TIPL via attentional or reinforcement-learning
signals may be reconciled by the observation that attention
is not a singular process, but instead consists of multiple
systems that have different spatial and temporal profiles(Seitz & Watanabe, 2005). For instance, research of
Posner et al. suggests that alerting, orienting, and8executive functions are triply dissociable attentional
subsystems (Fan, McCandliss, Sommer, Raz, & Posner,
2002; Posner & Petersen, 1990). The alerting system
controls a nonspecific arousal state, the orienting system
directs resources to a specific spatial cue or feature, andthe executive system is involved in solving a task
involving conflict. The orienting and executive systems
are suggested to selective to regions of space (spatialattention), individual features (feature-based attention), or
objects (object-based attention) regarded to be taskrelevant items, whereas alerting is a temporally phasic
but featurally nonspecific signal that increases generalprocessing at times important stimuli are thought to bepresent (temporal attention). Interestingly, each of theseattention subsystems has been linked with different
neuromodulatory signals (Fan et al., 2002); orienting withthe acetylcholine system (Davidson & Marrocco, 2000),alerting with the norepinephrine system (Coull, Frith,
Frackowiak, & Grasby, 1996; Marrocco, Witte, &Davidson, 1994; Witte, Davidson, & Marrocco, 1997),
and executive with dopamine (Fossella et al., 2002).
Importantly, acetylcholine, norepinephrine, and dopamine
are known to be involved in learning (Dalley et al., 2001;
Schultz, 2000) and have been proposed to have distinct
roles in reinforcement learning (Dayan & Balleine, 2002;
Dayan & Yu, 2003; Doya, 2002). These findings suggestthat attention and reinforcement-learning signals may besubserved by the same substrate. If this is indeed the case,then the important question in evaluating the present set ofresults is not whether attention or reinforcement-learning
signals are responsible for the restricted spatial-temporalprofile of learning, but rather which attentional/reinforcement signals are responsible and how do they interact inshaping TIPL?
Our results, combined with the previous findings,
indicate that task-irrelevant visual learning is spatiotemporally regulated by brain activity related to successful
detection of task targets. It is not clear what brain
mechanisms underlie this connection between task andtask-irrelevant learning. To clarify this, it is important tomeasure the spatial profile of the signals mediating TIPL.
Our results showed that there is a clear spatial gradient of
the learning although more extensive investigation isnecessary to clarify the overall shape of this learning
function. Further work will be required to specify which
attentional/reinforcement systems are involved in TIPL
and how their spatial and temporal profiles interact toproduce learning.

Acknowledgments
This study was funded by grants from NIH (R01
EY015980 and R21 EY017737), NSF (BCS-0345746,
BCS-0549036, and BCS-PR04-137 Center of Excellence
for Learning in Education, Science, and Technology), andJournal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

the Human Frontier Science Program Organization(RGP18/2004) to T.W., and by a grant from the Human
Frontier Science Program Organization (RGP0074/2003-C)
to M.K.
Co-first authors S.N. and A.R.S. contributed equally tothis work.
Commercial relationships: none.
Corresponding author: Shigeaki Nishina.
Email: nishina@bu.edu.
Address: 64 Cummington St., Boston, MA 02215, USA.

References
Ahissar, M., & Hochstein, S. (1993). Attentional controlof early perceptual learning. Proceedings of the
National Academy of Sciences of the United Statesof America, 90, 5718–5722. [PubMed] [Article]
Amitay, S., Irwin, A., & Moore, D. R. (2006). Discrimination learning induced by training with identicalstimuli. Nature Neuroscience, 9, 1446–1448. [PubMed]
Ball, K., & Sekuler, R. (1982). A specific and enduring
improvement in visual motion discrimination. Science, 218, 697–698. [PubMed]
Brainard, D. H. (1997). The Psychophysics Toolbox.
Spatial Vision, 10, 433–436. [PubMed]
Coull, J. T., Frith, C. D., Frackowiak, R. S., & Grasby, P. M.
(1996). A fronto-parietal network for rapid visual
information processing: A PET study of sustained
attention and working memory. Neuropsychologia, 34,
1085–1095. [PubMed]
Dalley, J. W., McGaughy, J., O’Connell, M. T., Cardinal,
R. N., Levita, L., & Robbins, T. W. (2001). Distinct
changes in cortical acetylcholine and noradrenaline
efflux during contingent and noncontingent performance of a visual attentional task. Journal of Neuroscience, 21, 4908–4914. [PubMed] [Article]
Davidson, M. C., & Marrocco, R. T. (2000). Local infusionof scopolamine into intraparietal cortex slows covert
orienting in rhesus monkeys. Journal of Neurophysiology, 83, 1536–1549. [PubMed] [Article]
Dayan, P., & Balleine, B. W. (2002). Reward, motivation,and reinforcement learning. Neuron, 36, 285–298.
[PubMed] [Article]
Dayan, P., & Yu, A. J. (2003). Uncertainty and learning.
IETE Journal of Research, 49, 171–182. [Article]
Dinse, H. R., Ragert, P., Pleger, B., Schwenkreis, P., &Tegenthoff, M. (2003). Pharmacological modulationof perceptual learning and associated cortical reorganization. Science, 301, 91–94. [PubMed]
Dosher, B. A., & Lu, Z. L. (1998). Perceptual learningreflects external noise filtering and internal noise

9

reduction through channel reweighting. Proceedings of
the National Academy of Sciences of the United Statesof America, 95, 13988–13993. [PubMed] [Article]
Doya, K. (2002). Metalearning and neuromodulation.
Neural Networks, 15, 495–506. [PubMed]
Eriksen, C. W., & St. James, J. D. (1986). Visual
attention within and around the field of focal
attention: A zoom lens model. Perception & Psychophysics, 40, 225–240. [PubMed]
Fahle, M., & Poggio, T. (2002). Perceptual learning.
Cambridge, MA: MIT Press.
Fan, J., McCandliss, B. D., Sommer, T., Raz, A., &Posner, M. I. (2002). Testing the efficiency andindependence of attentional networks. Journal of
Cognitive Neuroscience, 14, 340–347. [PubMed]
Fiorentini, A., & Berardi, N. (1980). Perceptual learning
specific for orientation and spatial frequency. Nature,
287, 43–44. [PubMed]
Fossella, J., Sommer, T., Fan, J., Wu, Y., Swanson, J. M.,
Pfaff, D. W., et al. (2002). Assessing the molecular
genetics of attention networks. BMC Neuroscience, 3,
14. [PubMed] [Article]
Herzog, M. H., & Fahle, M. (1999). Effects of biased
feedback on learning and deciding in a Vernier
discrimination task. Vision Research, 39, 4232–4243.
[PubMed]LaBerge, D., Carlson, R. L., Williams, J. K., & Bunney,
B. G. (1997). Shifting attention in visual space: Testsof moving-spotlight models versus an activity-distribution model. Journal of Experimental Psychology:
Human Perception and Performance, 23, 1380–1392.
[PubMed]Ludwig, I., & Skrandies, W. (2002). Human perceptual
learning in the peripheral visual field: Sensory thresholds and neurophysiological correlates. BiologicalPsychology, 59, 187–206. [PubMed]
Marrocco, R. T., Witte, E. A., & Davidson, M. C. (1994).
Arousal systems. Current Opinion in Neurobiology,
4, 166–170. [PubMed]
McKee, S. P., & Westheimer, G. (1978). Improvement inVernier acuity with practice. Perception & Psychophysics, 24, 258–262. [PubMed]
Müller, N. G., Mollenhauer, M., Rösler, A., & Kleinschmidt,
A. (2005). The attentional field has a Mexican hat
distribution. Vision Research, 45, 1129–1137.
[PubMed]Pelli, D. G. (1997). The VideoToolbox software for visual
psychophysics: Transforming numbers into movies.
Spatial Vision, 10, 437–442. [PubMed]
Poggio, T., Fahle, M., & Edelman, S. (1992). Fast
perceptual learning in visual hyperacuity. Science,
256, 1018–1021. [PubMed]Journal of Vision (2007) 7(13):2, 1–10

Nishina, Seitz, Kawato, & Watanabe

Polley, D. B., Steinberg, E. E., & Merzenich, M. M.
(2006). Perceptual learning directs auditory cortical
map reorganization through top-down influences.
Journal of Neuroscience, 26, 4970–4982. [PubMed][Article]
Posner, M. I. (1980). Orienting of attention. Quarterly
Journal of Experimental Psychology, 32, 3–25.
[PubMed]Posner, M. I., & Petersen, S. E. (1990). The attention
system of the human brain. Annual Review of Neuroscience, 13, 25–42. [PubMed]
Schoups, A., Vogels, R., Qian, N., & Orban, G. (2001).
Practising orientation identification improves orientation coding in V1 neurons. Nature, 412, 549–553.
[PubMed]
Schultz, W. (2000). Multiple reward signals in the brain.
Nature Reviews, Neuroscience, 1, 199–207. [PubMed]
Seitz, A., Lefebvre, C., Watanabe, T., & Jolicoeur, P. (2005).
Requirement for high-level processing in subliminal
learning. Current Biology, 15, R753–R755. [PubMed][Article]
Seitz, A., & Watanabe, T. (2005). A unified model forperceptual learning. Trends in Cognitive Sciences, 9,329–334. [PubMed]

10

Seitz, A. R., Náñez, J. E., Holloway, S. R., Koyama, S., &Watanabe, T. (2005). Seeing what is not there shows
the costs of perceptual learning. Proceedings of the
National Academy of Sciences of the United States ofAmerica, 102, 9080–9085. [PubMed] [Article]
Seitz, A. R., & Watanabe, T. (2003). Psychophysics: Is
subliminal learning really passive? Nature, 422, 36.
[PubMed]Shiu, L. P., & Pashler, H. (1992). Improvement in lineorientation discrimination is retinally local but
dependent on cognitive set. Perception & Psychophysics, 52, 582–588. [PubMed]
Tsushima, Y., Sasaki, Y., & Watanabe, T. (2006). Greater
disruption due to failure of inhibitory control on an
ambiguous distractor. Science, 314, 1786–1788.
[PubMed]Watanabe, T., Náñez, J. E., & Sasaki, Y. (2001).
Perceptual learning without perception. Nature, 413,
844–848. [PubMed]
Witte, E. A., Davidson, M. C., & Marrocco, R. T.
(1997). Effects of altering brain cholinergic activity
on covert orienting of attention: Comparison ofmonkey and human performance. Psychopharmacology, 132, 324–334. [PubMed]