Please cite this article in press as: Yoshida et al., Residual Attention Guidance in Blindsight Monkeys Watching Complex Natural
Scenes, Current Biology (2012), doi:10.1016/j.cub.2012.05.046
Current Biology 22, 1–6, August 7, 2012 ª2012 Elsevier Ltd All rights reserved

DOI 10.1016/j.cub.2012.05.046

ReportResidual Attention Guidancein Blindsight Monkeys
Watching Complex Natural Scenes
Masatoshi Yoshida,1,2,6 Laurent Itti,3,4,6,* David J. Berg,4Takuro Ikeda,1,7 Rikako Kato,1 Kana Takaura,1,2,8
Brian J. White,5 Douglas P. Munoz,5 and Tadashi Isa1,2
1Department of Developmental Physiology, National Institutefor Physiological Sciences, Okazaki 444-8585, Japan
2School of Life Science, The Graduate University for Advanced
Studies, Hayama 203-0193, Japan
3Computer Science Department
4Neuroscience Graduate Program
University of Southern California, Los Angeles, CA 90089, USA
5Centre for Neuroscience Studies, Queen’s University,
Kingston, ON K7L 3N6, Canada

Summary
Patients with damage to primary visual cortex (V1) demonstrate residual performance on laboratory visual tasks
despite denial of conscious seeing (blindsight) [1]. Aftera period of recovery, which suggests a role for plasticity[2], visual sensitivity higher than chance is observed in humans and monkeys for simple luminance-defined stimuli,
grating stimuli, moving gratings, and other stimuli [3–7].
Some residual cognitive processes including bottom-up
attention and spatial memory have also been demonstrated[8–10]. To date, little is known about blindsight withnatural stimuli and spontaneous visual behavior. In particular, is orienting attention toward salient stimuli during
free viewing still possible? We used a computationalsaliency map model to analyze spontaneous eye movements
of monkeys with blindsight from unilateral ablation of V1.
Despite general deficits in gaze allocation, monkeys weresignificantly attracted to salient stimuli. The contributionof orientation features to salience was nearly abolished,
whereas contributions of motion, intensity, and color
features were preserved. Control experiments employing
laboratory stimuli confirmed the free-viewing finding thatlesioned monkeys retained color sensitivity. Our results
show that attention guidance over complex natural scenes
is preserved in the absence of V1, thereby directly challenging theories and models that crucially depend on V1 tocompute the low-level visual features that guide attention.

Results and Discussion
Efficiently guiding attention toward the most relevant parts ofthe visual world is a higher visual function critical for survival,
as supported by many theories of attention [11, 12]. In mosttheories and computational models, V1 is the central sitewhere visual features are computed that guide attention

6These authors contributed equally to this work
7Present address: Center for Neuroscience

Studies, Queen’s University,
Kingston, ON K7L 3N6, Canada
8Present address: Laboratory for Adaptive Intelligence, RIKEN Brain
Science Institute, Wako 351-0198, Japan*Correspondence: itti@usc.edu

toward salient locations [11–14]. Can blindsight patients oranimals still compute such visual features? Although blindsight patients and animals exhibit significant residual visually
guided behavior with simple laboratory tasks and stimuli, little
is known quantitatively about their spontaneous natural vision(see [15, 16] for qualitative observations). Elucidating thisquestion is important to understanding blindsight and theneural substrates of visual attention, and to possibly help
affected patients better exploit residual visual processing intheir daily life.
Macaque monkeys (n = 6) were trained to fixate and execute
visually guided saccade tasks using simple stimuli. Primaryvisual cortex (V1) was then unilaterally removed in fivemonkeys by aspiration (as described previously [6]). Lesions
covered at least 5 –20 in eccentricity and 630 around thehorizontal direction for all monkeys (see Figure S1A available
online). After the lesion, the presence of residual vision was
confirmed with visually guided saccade tasks, reported previously [6]. Here we examined spontaneous eye movements ofthese monkeys during free viewing of 164 natural movie clips(w70 min). Successful central fixation for 0.5 s triggereda movie clip (4.0–93.8 s/clip), presented either normally or
horizontally flipped to eliminate stimulus-induced biases.
Monkeys did not receive juice reward during free viewing;
thus, movies were not associated with reward. 128,361
saccades were recorded (Figure 1A).
First, we examined whether the basic properties of freeviewing saccadic eye movements were affected by V1 lesion.
With both normal and horizontally flipped movies, distributionsof fixations on the absolute screen area exhibited no strong
left-right bias, for both intact and lesioned monkeys (Figure 1B). Moreover, there was no significant difference in datafrom normal versus flipped movies (Figure 1C), which werethus merged for saliency analysis below. Polar histograms of
relative saccade vector directions (Figure S1B) showed no
obvious left-right bias that might have been induced by lesion(Figure S1C). However, lesion did affect movie viewing. Whenpolar histograms of saccade vectors were restricted to only
the first saccade of each movie clip, they were significantly
biased away from the affected field (Figures S1D and S1E).
Note that such bias did not affect the overall distribution of
saccade vectors because the first saccades comprise only
1% of all saccades (820 out of 75,767). Intact monkeys did
not exhibit such bias (Figure S1E). We also detected effectsof lesion on distributions of saccade amplitude and of peakvelocity (Figures S1F and S1G). These results are consistent
with our previous reports using laboratory stimuli, in that V1
lesion affects saccade dynamics [6, 8, 17].
Taking these results together, our analysis of free-viewing
eye movements demonstrated surprisingly little effect of V1
lesion: lesioned monkeys still made many saccades to targets
in their affected field and explored the stimulus screen area
thoroughly. However, they also exhibited clear sensory deficits, as shown by the first-saccade bias toward the intact field,
which is in agreement with previous studies using laboratory
stimuli [4, 6].
To better understand how guidance of gaze was affected by
V1 lesion in natural free viewing, we employed a computational

Please cite this article in press as: Yoshida et al., Residual Attention Guidance in Blindsight Monkeys Watching Complex Natural
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Current Biology Vol 22 No 15
2

A

Monkey’s# ofnamefixationsCategory

HK
U

21,891
19,481
11,222

Left
V1

A
T
G

11,84916,26813,568

Right
V1

H
U

23,02911,053IntactmonkeyLesionedmonkeyNumber of fixations

B(Normal movies) (Flipped movies)

Monkey A0

2 (log10)

1

LR bias offixation on screenC(L bias)LR bias (Flipped movie)

100%

50%
Intact monkey
Left V1
Right V10%

(R bias) 0%

50%(R bias)100%(L bias)LR bias (Normal movie)
Figure 1. Basic Eye Movement Properties(A) Number of saccades sampled in the free-viewing task for each of threemonkey categories. In two monkeys (H and U), data were acquired both
before and after lesion.
(B) Number of fixations on screen (monkey A) for normal (left) and horizontally flipped (right) presentations.
(C) Ratio of fixations on the left half of the screen to the total number offixations (defined here as the LR bias) for normal movies (horizontal axis)
versus horizontally flipped movies (vertical axis). Each symbol denotes
data for a different monkey. The difference between the LR bias of normal
movies and horizontally flipped movies was not significant (p = 0.65, pairedt test, n = 8). See also Figure S1.

model of visual saliency to quantitatively titrate the nature ofvisual targets that monkeys looked at. Briefly, the saliency
model [18, 19] decomposed incoming video inputs along
several simple visual features at multiple spatial scales.
Center-surround contrast operators for six center andsurround scale combinations gave rise to feature maps thathighlighted locations that differed from their neighbors ineach feature. Finally, all feature maps were combined intoa single saliency map to emphasize conspicuous visual

locations in a feature-independent manner (Figure 2A). Thismodel provides a flexible framework for predicting saliency
maps from low-level feature maps, without necessarily
committing to the exact origin or nature of the feature maps.
Five features were used, each thought to contribute significantly to visual search in humans [20]: luminance, two chromatic contrasts (in the Derrington-Krauskopf-Lennie [DKL]
color space derived from retinal ganglion cells [21]), orientation (V1-like Gabor filters in four orientations [22]), and motion(spatiotemporal energy model in four directions [23]). We
quantified saliency-guided eye movements (Figure 2B) using
receiver operating characteristic analysis of saliency values
at endpoints of monkey saccades compared to random
endpoints (Figure 2C) (see Figure S2 for random endpoint
sampling scheme). This resulted in an area under the curve(AUC) score (0.5 indicates chance performance, i.e., eyemovements are not guided by saliency, whereas the best
expected score, from interobserver correlation analysis, mightreach w0.7; see Supplemental Experimental Procedures).
AUC scores were significantly above chance for all monkeys(Figure 2D), indicating that lesioned monkeys were still significantly attracted toward salient targets in their affected field.
At the population level, V1 lesions significantly reduced but
did not abolish the tendency of monkeys to gaze towardsalient targets (intact monkeys: AUC = 0.627 6 0.002; lesioned
monkeys: AUC = 0.601 6 0.003 in affected field, AUC = 0.627 6
0.003 in normal field; see Figure 2D for statistical analysis).
To investigate the possibility that some of the saccades into
the affected field might be memory driven as opposed to truly
visually guided, we duplicated the AUC analysis using only
pure discovery saccades, i.e., saccades aimed toward screen
locations that had never entered the intact field. The same
pattern of results was observed (Figure S2C), excluding thepossibility that memory was a dominant factor in directing
saccades to the affected field.
Can we quantitatively explain differences in visual processing and saliency computations between normal and affected
fields in term of features? To investigate this, we modified
the model to examine relative contributions of the different
basic features to gaze guidance. First, AUC scores were
calculated for variants of the saliency model reduced to using
only any one of our five features (‘‘single-feature model’’).
All scores were significantly above chance, indicating thatsaliency in each feature taken separately still predictedmonkey gaze above chance (Figure 3A). Because different
features are often correlated in the natural visual world—e.g.,a colorful object may also be brighter than the background,posing the question of whether color or brightness attracted
attention (Figure 3B)—we sought to isolate the nonredundant
contribution of each feature to saliency. To this end, we used
an optimization procedure followed by a leave-one-featureout approach (Supplemental Experimental Procedures). The
optimization algorithm adjusted the weights of the fivefeatures to maximize the model’s ability to predict monkey
eye movements, separately for each of three different saccadegroups (intact monkeys, affected field of lesioned monkeys,
and normal field of lesioned monkeys, as defined in Figure 2D)(Figure S3). In the leave-one-feature-out approach, applied
separately to each group, the AUC score of the optimized‘‘full’’ model incorporating all features and the scores of eachsimilarly optimized model incorporating all but one feature(‘‘minus-one’’ model) were compared (Figure 3C). A nonredundant contribution index was defined as the AUC score difference between the full model and a reduced model divided

Please cite this article in press as: Yoshida et al., Residual Attention Guidance in Blindsight Monkeys Watching Complex Natural
Scenes, Current Biology (2012), doi:10.1016/j.cub.2012.05.046
Residual Attention Guidance in Blindsight Monkeys
3A

Presented image

Feature maps

Saliency map

Eye position

Luminance
L-M(red - green)
S-Lum(blue - yellow)
Orientation
MotionB

Saliency map

D

n.s.

Monkey A
Left
normal field

AUC score

Monkey A
Right
affected field

0.65

**

**

0.60

0.55

# of saccades

1000

Monkey A
Affected field
AUC score = 0.58(= chancelevel)

Intactmonkey0Normal
field

0.50

C

Affected
field

Movie clip

Lesionedmonkey01Normalized saliency
Figure 2. Residual Saliency-Guided Eye Movements after V1 Lesion(A) Saliency model (see Supplemental Experimental Procedures for detailed definition of the features).
(B) Example movie frames (left) and saliency maps (right) with trajectory of an eye movement. Top: first saccade of a normally presented movie clip (movie
003, frame 5) directed leftward (normal field), toward a salient colorful object. Bottom: 91st saccade of the same clip (frame 1243) directed rightward (affected
field), toward a salient moving object.
(C) Quantitative analysis of saliency-guided eye movements. Saliency values at each monkey saccade endpoint (green) and for random endpoints (magenta)
were sampled (left) and histogrammed (right). Receiver operating characteristic analysis of the histograms yielded an area under the curve (AUC) score. AUC
scores were computed separately for leftward and rightward saccades.
(D) AUC scores for three groups: ‘‘intact monkey,’’ data for left and right directions (six hemifields) for the three intact monkeys; ‘‘affected field’’ and ‘‘normal
field,’’ data for the five lesioned monkeys. Error bars indicate SE. In all cases, AUC scores were significantly above chance (0.5) (p < 0.05, two-tailed t test). In
group comparisons, ** indicates significant group mean difference (p < 1029, Wilcoxon signed-rank test after Bonferroni correction); n.s. indicates not
significant (p > 0.10, Wilcoxon signed-rank test after Bonferroni correction).
See Figure S2 for consideration of sampling scheme.

by the AUC score of the full model minus 0.5 (Figure 3C). The
index reflects how much a particular feature contributed togaze guidance, beyond what could already be explained by
the other four features. Figure 3D summarizes the contribution
indices obtained. The pattern of feature contributions in intactmonkeys resembled that in the normal field of lesioned
monkeys (Spearman’s rank partial correlation r = 0.90, p =0.09, n = 5), in which the contribution of motion is highest
and those of color, orientation, and luminance follow (indecreasing order). After V1 lesion, however, that order shiftedto motion, luminance, color, and orientation. This pattern did
not resemble others (r = 0.55, p > 0.2 for affected field oflesioned monkeys versus intact monkeys; r = 20.39, p > 0.2
for affected field versus normal field of lesioned monkeys;

Please cite this article in press as: Yoshida et al., Residual Attention Guidance in Blindsight Monkeys Watching Complex Natural
Scenes, Current Biology (2012), doi:10.1016/j.cub.2012.05.046
Current Biology Vol 22 No 15
4AB

AUC score for single-feature model
Intact monkey

Feature correlation

Lesioned monkey
Affected field

L-M

Normal field

S-Lum
Orientation
Orientation

L-M

Lum

C

Correlation coefficient

Lum
L-M
S-Lum
Orientation
Motion

0.50

Lum
L-M
S-Lum
Orientation
Motion

0.55

S-Lum

Motion

Lum
L-M
S-Lum
Orientation
Motion

AUC score

0.60

-1

1

0

D

Nonredundant contribution index (%)
5

0

10

5

0

10

5

0

10

0.6

AUC score

A

Lum
L-M
B

S-Lum

n.s.
n.s.

Orientation
MotionAffected field

Ful

lm

ode
l
Min
us
mo -onedel0.5

Intact monkey

Normal field

Lesioned monkey

Figure 3. Contribution of Saliency for Each Feature(A) AUC scores for single-feature models, for three groups as in Figure 2D, were all significantly above chance (0.5) (p < 0.05, two-tailed t test). Error bars
indicate SE. Feature channels are as in Figure 2A. ‘‘Lum’’ denotes the luminance channel.
(B) Correlation coefficients between features over all movie frames used in the experiments were all significantly higher than zero (p < 1029 after Bonferroni
correction).
(C) Variable-weight model. An optimized full model with all features was compared with a leave-one-feature-out model lacking one feature (‘‘minus-one
model’’). The resulting differences between AUC scores (arrow A) were divided by the AUC score of the full model minus 0.5 (arrow B), which was used
to define the nonredundant contribution index of the feature of interest (here, motion).
(D) Nonredundant contribution index of each feature (0 indicates that the feature of interest did not contribute to gaze guidance in any unique manner beyond
what the other four features could predict). All of the feature contributions, except for those indicated n.s. (not significant), were significantly higher than zero(p < 0.05, paired t test, with Bonferroni correction for 15 simultaneous tests). Note that the contribution index does not add up to 100% (by definition). Error
bars indicate SE.

Spearman’s rank partial correlation). Our analysis thus shows
an interesting pattern of differences between intact andlesioned monkeys (Figure 3D): the contribution of orientation
was decreased, luminance was increased, and motion andcolor remained relatively unchanged (see also the section‘‘Consideration of Previous Results’’ in the Supplemental
Information).
In Figure 3D, the finding that the contribution of color was
not abolished in the affected field was surprising given
contrasting results from previous laboratory experiments[3, 24–26]. This may be specific to our natural free-viewing
paradigm. Hence, we designed a control laboratory experiment to verify the model’s prediction.
Two lesioned monkeys were tested with a visually guided
saccade task using equiluminant chromatic stimuli (Figure 4A).

Lesioned monkeys detected, above chance, two types of
equiluminant chromatic stimuli tuned to different ganglion
cell types (Figure 4B). Although performance with chromatic
stimuli was below that with high-contrast achromatic
stimuli (positive control, confirming residual vision for luminance-defined shapes), it was better than for low-contrast
achromatic stimuli (negative control, indicating that slightluminance differences between chromatic stimuli and background are unlikely to have contaminated chromatic processing). This was further confirmed in one monkey for arange of slight luminance variations (Figure 4C; see figurelegend for detail). We further tested the monkey with color
detection tasks using either mosaic stimuli or colored
Gaussian stimuli to exclude possible contributions of edge
artifacts and luminance differences (Figure S4). In sum, ourPlease cite this article in press as: Yoshida et al., Residual Attention Guidance in Blindsight Monkeys Watching Complex Natural
Scenes, Current Biology (2012), doi:10.1016/j.cub.2012.05.046
Residual Attention Guidance in Blindsight Monkeys
5

Normalfield

A

Fixation(0.9-1.4 s)

Figure 4. Direct Evaluation of Residual Chromatic Processing after V1 Lesion

Affected
field(A) Visually guided saccade task using equiluminant chromatic stimuli. A single target stimulus
was presented in the affected hemifield, either
above or below the horizontal meridian. Correct
forced-choice saccade to the target yielded fruit
juice reward. Performance was evaluated by
calculating the correct ratio as the number oftrials with saccades toward the same quadrant
as the target stimulus divided by the total numberof trials with successful fixation before stimulus
presentation.
(B) Performance for two lesioned monkeys (T and
A). Lum (high contrast), achromatic stimuli witha luminance contrast of 2.3; Lum (low contrast),
achromatic stimuli with a luminance contrast of
0.02 and 20.02 for monkey T and 0.04 and20.04 for monkey A; L-M, equiluminant chromatic stimuli with isolated L-M channel stimulation; S-Lum, equiluminant chromatic stimuli withisolated S-Lum channel stimulation. Error bars
denote 95% confidence interval. For Lum (highcontrast), L-M, and S-Lum, but not for Lum (lowcontrast), performance was significantly above
chance (0.5).
(C) Correct ratio for stimuli with small luminancedifference added (horizontal axis) to account forpossible contribution of deviations from exact
equiluminance. Color coding is as in (B). Error
bars denote 95% confidence interval. Data formonkey T are shown. Correct ratio remained
significantly above chance for chromatic stimuli
even over the range where it dropped to chance
for luminance-defined stimuli, hence confirmingthat observed performance with chromatic
stimuli was not due to contamination fromluminance. Right: parameter of stimuli used inthis experiment is depicted as dotted circles
in the Derrington-Krauskopf-Lennie (DKL) color
space.

Saccade to target

B

Monkey A

Monkey T

Correct ratio

1.0

S-Lum

L-M

Lum
(high contrast)Lum(low contrast)

L-M

S-Lum

Lum
(high contrast)Lum(low contrast)

0.5

C
1.0

Lum

Correct ratio

S-Lum

L-M
0.5

-1

-.1

-.01
.01
Luminance contrast.1

1

control experiments using simple stimuli directly confirm
residual guidance toward purely chromatic information, as
predicted by our saliency model and free-viewing experiment.
More broadly, color information was hence used during bothforced-choice and spontaneous behavior in our blindsightanimals.
In summary, for the first time, using a computational saliency
model together with eye movement recording during freeviewing of natural video stimuli, we observed sophisticatedgaze orienting toward salient stimuli in blindsight. Our
approach, combining computational modeling and freeviewing, successfully allowed us to titrate the impact of V1
lesions on processing of visual features and the spontaneous

guidance of attention. Our resultscomplement and extend previous laboratory experiments in a manner that ismore relevant to daily life. It should beemphasized that our experiments andresults concern detection and attention/gaze guidance, but not discrimination or identification. Thus, when wefind that monkeys look toward stimuli
in their affected field that are salient,e.g., in the color domain, this does
not necessarily imply that the monkeys
are capable of identifying or discriminating colors (see Supplemental Information).
Our study clearly shows that, after recovery, there is more
to visual attention and saliency than the pathway through V1.
We succeeded in pinpointing the features that guide residual
vision, by expanding our original computational model toallow differential contributions of visual features. We believethat the use of natural movie stimuli is an important featureof the present study that places our results into a context
more relevant to everyday life [27]. An important question for
future research is whether the attention processes that wehave shown to be active in postrecovery blindsight also
contribute significantly to saliency, attention, and gaze even

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Current Biology Vol 22 No 15
6

in the normal brain. Our study also shows that computationalmodels of attention cannot rely exclusively on V1 as theprimary center for saliency computation [13], and that theyshould also consider how alternate pathways may provide critical feature information to the primate attention and saliency
mechanisms.
Experimental Procedures
Six Japanese monkeys (Macaca fuscata; three male and three female, bodyweight 5–9 kg) were implanted with scleral search coils and a head holder(see Supplemental Experimental Procedures). All experimental procedures
were performed in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals and were approved by
the Committee for Animal Experiment at the National Institutes of Natural
Sciences. After pretraining, V1 was surgically removed by aspiration underanesthesia in five monkeys. Free-viewing task was performed 15, 9, 28, 9,
and 19 months after V1 lesion for monkeys A, H, T, U, and G, respectively.
As a control, monkeys H and U were also tested before lesion, and intactmonkey K was also tested. Monkeys watched 164 video clips that varied
in duration and semantic content. Saccadic eye movements were determined using an algorithm that combined smoothed velocity measurementswith a windowed principal components analysis [18]. A validated computational model of visual attention was used to predict individual eye movements (Figure 2A) [14, 19]. Two lesioned monkeys (T and A) were also tested
with a visually guided saccade task with equiluminant color stimuli. Target
stimuli were circular spots whose color properties were derived from the
DKL color space [21].
Supplemental Information
Supplemental Information includes four figures and Supplemental
Experimental Procedures and can be found with this article online at
doi:10.1016/j.cub.2012.05.046.
Acknowledgments
This work was supported by the Human Frontier Science Program, theNational Science Foundation, the Army Research Office, the Defense
Advanced Research Projects Agency, Grants-in-Aid for Scientific Research(KAKENHI; 21500377 and 22220006), the Core Research for Evolutionary
Science and Technology program of the Japan Science and Technology
Agency, and the Excellent Young Researchers Overseas Visit Program of
the Japan Society for the Promotion of Science. The views, opinions, andfindings contained in this article are those of the authors and should not
be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency
or the Department of Defense.
Received: March 18, 2012
Revised: May 1, 2012
Accepted: May 23, 2012
Published online: June 28, 2012
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Current Biology, Volume 22

Supplemental Information
Residual Attention Guidancein Blindsight Monkeys
Watching Complex Natural Scenes
Masatoshi Yoshida, Laurent Itti, David J. Berg, Takuro Ikeda, Rikako Kato, Kana Takaura,
Brian J. White, Douglas P. Munoz, and Tadashi Isa

Supplemental Inventory
Figure S1 (related to Figure 1). This figure shows results of properties of vision and saccades, which has
no space to display in Figure 1.
Figure S2 (related to Figure 2). This figure shows plots of the AUC values in different sampling schemes
to demonstrate robustness of the finding in Figure 2D.
Figure S3 (related to Figure 3). This figure shows plots of weights of variable-weight model in Figure 3D
to describe the summary of the procedure.
Figure S4 (related to Figure 4). This figure shows results of other color tasks to strengthen the conclusiondrawn in Figure 4.
Supplemental Experimental Procedures
Supplemental ReferencesFigure S1.

Figure S1 (Related to Figure 1). Basic Property of Vision and Saccades(A) Deficit maps. In four lesioned monkeys, visual sensitivity was tested with a visually guided saccadetask. Black indicates the position where reduction of visual sensitivity was detected. Gray indicates the
position where the sensitivity was not fully determined. White denotes control positions in their normal
field. See Experimental Procedures for details.
(B and D) Polar histograms (monkey A) of the number of saccade vectors for all saccades (B) and the firstsaccades of each movie clip (D). The length of the green arrows is scaled to the total number of leftward or
rightward saccades (numbers near the arrows). **, p < 0.01, binomial test. n.s., not significant.
(C and E) Ratio of the number of leftward saccades to the total number of saccades, plotted across threemonkey categories (see Figure 1A). Each symbol denotes data for normal (circle) and horizontally flipped(triangle) movies of each monkey, for all saccades (C) and for the first saccades (E) of each movie clip. **indicates significant deviation from 50% (p < 0.01, based on confidence interval calculated from maximum
likelihood methods); n.s., not significant.
(F and G) Properties of saccades. The amplitude (F), the peak velocity (G) of saccades were plotted forthree groups, in the same manner as in Figure 2D. The circles indicate the median value of saccades toeach direction of each monkey. The error bar indicates 40% and 60% percentile of each population. **,-9significant difference between groups (P < 10 ).

Figure S2 (Related to Figure 2). Consideration of Sampling Scheme(A and B) AUC scores at the population level are plotted for three groups as in Figure 2D. For calculationof AUC scores, random sampling of saliency values was either (A) uniform or (B) drawn from the
distribution of saccadic endpoints from all saccades made by the monkey group under test over the entire
collection of video clips. The bar plot for (A) is identical to Figures 2D and displayed here for comparison.
In the latter sampling scheme, the analysis of the AUC scores at the population level (B) gave essentiallythe same results as the original one (A); the AUC scores were significantly above chance for all threegroups (intact monkeys: AUC = 0.585±0.003; lesioned monkeys: AUC = 0.552±0.003 in affected field,
AUC = 0.578±0.004 in normal field).
(C) To exclude a possibility that the lesioned monkeys‟ saccades to the affected hemifield are guided bymemory, another sampling scheme („pure discovery saccades‟) was employed. We selected saccades
which go into the affected field, and only counting those saccades which go to a screen location which has
never before been inside the intact field. Note that this is a very strict test and should be interpreted as alower bound of the AUC scores, rather than a quantification of saliency-guided eye movements, sincethose are only saccades to screen regions which have always been in the blind field. The AUC scores
were significantly above chance for all three groups (intact monkeys: AUC = 0.624±0.003; lesioned
monkeys: AUC = 0.580±0.004 in affected field, AUC = 0.630±0.003 in normal field). Error bars indicate
S.E. In all cases, AUC scores were significantly above chance (0.5) (p < 0.05, two-tailed t-test). In group-9comparisons, ** indicates significant group mean difference (P<10 , Wilcoxon signed-rank test after
Bonferroni correction); n.s., not significant (P>0.10, Wilcoxon signed-rank test after Bonferroni correction).

Figure S3 (Related to Figure 3). Weights for the Variable-Weight Models Incorporating All Features
Data is plotted for three groups as in Figure 2D. The weight for the Motion feature was set as one andother weights were expressed as relative values to that of the Motion feature. The error bars denote thestandard errors calculated from the Hessians of the error surfaces (See Experimental Procedures). Notethat the number of free parameters is not five but four since the AUC score depends on the relative valueof weights, not the absolute one. The overall pattern of weights was similar to that of the contribution
indices (Figure 3D), although weights do not take into account possible correlations across features, which
are accounted for by the contribution indices.

Figure S4.

Figure S4 (Related to Figure 4). Color Task Using Mosaic Stimuli (A and B) and Colored Gaussians(C and D)(A) The task sequence. After fixation, identical distracters were presented both top and bottom of theaffected field. After 0.5 sec, they were replaced by a target stimulus (top) with chromatic contrast and a
distracter stimulus (bottom) without chromatic contrast. The monkeys were rewarded by making saccadeto the target stimulus.
(B) The correct ratio was plotted for luminance contrasts of achromatic mosaic stimuli (Lum), for chromatic
contrasts of mosaic stimuli (L-M and S-Lum). Red („Affected‟), the results of sessions where the stimuli
were presented in the affected hemifield. Blue („Normal‟), the results of separate sessions where thestimuli were presented in the normal hemifield. Error bars indicate 95% confidence interval based on
maximum likelihood method. In the bottom, examples of mosaic stimuli and distracters were shown.
(C) The task sequence is identical to the color task in Fig.4A. The only difference is that the stimuli arecolored Gaussians with 10 degree SD.
(D. The correct ratio is plotted for luminance contrasts of achromatic mosaic stimuli (Lum), for chromatic
contrasts of mosaic stimuli (L-M and S-Lum). Error bars indicate 95% confidence interval based on
maximum likelihood method. Data are for monkey T.

Supplemental Experimental Procedures
Animal Preparation
Subjects
Six Japanese monkeys (Macaca fuscata; three male and three female, body weight 5-9 kg) were
implanted with scleral search coils [1] and a head holder. All surgeries were performed under aseptic
conditions as described previously [2]. Anesthesia was induced by administration of xylazine hydrochloride(2 mg / kg, i.m.) and ketamine hydrochloride (5 mg / kg, i.m.) and was maintained with isoflurane (1.0-1.5%). All experimental procedures were performed in accordance with the National Institutes of Health
Guidelines for the Care and Use of Laboratory Animals and approved by the Committee for Animal
Experiment at National Institute of Natural Sciences. The monkeys were allowed to recover for more than
2 weeks before starting the prelesion training. The exact age of the monkeys is not known. Estimating fromthe body weight and the record of breeding history, all were operated at adult age except for monkey U
that was operated at young adult age.
Prelesion Training
The monkeys sat in a primate chair with their heads in a fixed position and were trained to perform afixation task and a visually guided saccade task for a liquid reward as described previously [2].
Unilateral V1 Lesion
To study residual vision after V1 lesion, it is important to exclude the possibility that spared cortex might
contribute to residual vision [3, 4]. Thus lesion should cover all of the corresponding visual field tested(5-25 degrees in eccentricity). This corresponds to the posterior half of operculum, dorsal and ventral leaf
and roof of calcarine sulcus and the most posterior part of the stem of calcarine sulcus [5-7]. Under
anesthesia, these cortices were surgically removed by aspiration with a small-gauge metal suction tube.
After surgery, the monkeys were given penicillin G (80 thousand units / day, i.m.) and cefmetazole (0.5 g /day, i.m.) as an antibiotics and dexamethasone sodium phosphate (0.5 mg / kg, i.m.) to minimize brain
edema. Magnetic resonance images (MRIs) of the brains of these monkeys were acquired before and after
the surgery to confirm anatomical lesion extent.
Postlesion Training
Postlesion training was started one to four weeks after V1 lesion, at which time the monkeys' generalbehavior in the cage appeared normal. Initial recovery after V1 lesion was assessed with the same task as
during the preoperative training, as described previously [2]. Sensitivity for luminance contrast wasassessed in all monkeys except for monkey G (who had not been trained for visually guided saccades
tasks with stimuli with low luminance contrast), as described in [2]. The deficit map was drawn so that if thethreshold for luminance contrast was significantly higher (p < 0.05, logistic regression) from themirror-image position in normal hemifield, it was drawn as black (Figure S1A). For monkey H, threshold forsome positions were not fully determined and the positions are displayed in gray (Figure S1A). The extent
of lesion of monkey G was similar to that of monkey A or monkey U, and its deficit in free-viewing was alsosimilar (Figure 2D and Figure S2B-C). We also tested whether some island of intact vision might haveexisted and might have been driving the main effect. We hypothesized that if such spared island of visionexisted, salient stimuli presented at its eccentricity would more strongly attract saccades than salient
stimuli at other eccentricities. Thus, we repeated our main analysis (Fig. 2D), but restricted it to different
ranges of saccadic amplitudes (<10 , 10..15 , 15..20 , >20 ). For each monkey, scores were remarkably
similar across the different ranges of saccade amplitudes and always remained significantly above chance
for all amplitude ranges (p < 0.05, t-test with Bonferroni correction; data not shown). In conclusion, ourmain effect (Fig. 2D) cannot be explained by a limited population of saccades towards a visual field
location corresponding to a surviving island of cortex.

Free-Viewing Task
Stimulus Set
Monkeys watched 164 video clips (4.0 – 93.8 sec/clip, totaling approx. 70 minutes in duration, played in
random order) that varied in duration and semantic content. The clips were composed of nature and/or
complex movies. Stimuli were collected from television (NTSC source) with a commercial frame grabber(ATI Wonder Pro). The clip database included 15 so-called “monkey-relevant” clips collected at the
Queen's University animal care facility with a consumer grade digital video camera [8], and depictingmonkey facility environments, other monkeys, etc. Previous analysis of these clips compared to othernatural clips revealed no significant difference, thus supporting that monkeys were engaged by all video
clips in our collection [8]. Thus in the present study we used all 164 clips as a single video collection.
Frames were acquired and stored at 30 Hz in raw 640 × 480 RGB555 format and compressed to MPEG-1
movies (640 × 480 pixels). Clips were presented either in their normal orientation or flipped horizontally, toeliminate possible stimulus biases towards left or right.
Task
The task was performed 15, 9, 28, 9, 19 months after V1 lesion for monkeys A, H, T, U, G, respectively.
Visual stimuli were presented on a CRT monitor (21 inch, Mitsubishi RD21GZ, 71.5 × 53.5 cm; 640 × 480
pixels) positioned 28 cm from the eyes. This provided a usable field of view of 61.6° × 48.1°. Stimulus
presentation was orchestrated using a Linux computer running in house-programmed presentation
software (downloadable at http://iLab.usc.edu/toolkit) under SCHED_FIFO scheduling to ensure proper
frame rate presentation [9, 10]. The task and data storage were controlled using computers running areal-time data acquisition system (Reflective computing, Tempo for Windows) with a dynamic link to
Matlab (MathWorks). Trial initiation was self-paced. Each video presentation was preceded by a fixation
point and the next video was initiated when the monkey's eye position remained within a square electronic
window with 5° radius of the central fixation point for 500 ms. The monkeys were not rewarded for doing
this task, but the monkeys quickly learned to fixate in order to initiate the next clip. (In calibration sessions,they were rewarded by fruit juice for correctly making saccades and fixations to simple objects.) After eachclip, a 1.5 sec inter-trial interval followed, and the next trial began as the fixation spot turned on.
Eye-Tracking and Calibration for Saccade Measurement
Monkeys were seated in a primate chair with their heads restrained. Eye movements were recorded usinga magnetic search coil [11] with a resolution of 0.1 degree. Horizontal and vertical eye positions were
sampled at 1 kHz. To calibrate eye position, monkeys performed a fixation task in which targets at ninedifferent horizontal positions and at nine different vertical positions were presented. Monkeys were given aliquid reward if they fixated a target within a circular window for 1 sec. The window was variable acrosseccentricities from the fixation point at the center of the screen, typically, 4 degrees radius at 10 degrees
eccentricity and 7.5 degrees radius at 20 degrees eccentricity. The mean eye position during 750-1000 ms
of the fixation period was used for calibration. In order to control for small non-linearities in the field coil, theweighted average of several visits to each target endpoint was later used to perform an affine transformand thin-plate-spline interpolation [10] on the eye position data collected during free viewing of the video
clips. This calibration session was performed both before and after the free-viewing sessions.
Quantifying Eye Movement Behavior
Saccadic eye movements were determined using an algorithm which combined smoothed velocity
measurements with a windowed principal components analysis (see [8] for details). For analysis with thesaliency model, saccades with short (<80 ms) intervening fixations or smooth pursuits and small
differences in saccadic direction (<45°) were assumed to represent readjustments of gaze en route to atarget, and so were combined into a single saccadic eye movement toward the final target, rather than two
or more separate saccades. Additionally, saccades of <2° in amplitude and <20 ms in duration were
removed in order to decrease the false positive rate of saccade parsing and to focus analysis on eye
movements that more likely reflected a shift of attention to a new target as opposed to minor gazeadjustments on a current target [10]. This saccade parsing algorithm is freely available as part of the
open-source stimulus presentation software.
For the model-free analysis of Figure 1, all 128,361 fixations recorded were used, as detailed in
Figure 1A, under the assumption that whether or not the monkey paid attention to the stimuli was irrelevant

to basic saccade statistics. For analysis with the saliency model, however, we first eliminated clips andfixations where monkeys were presumably not engaged by the video stimuli, as detailed below. For eachmonkey, clips that contained too few eye movements (fewer than 10 saccades per clip) or excessive
off-screen eye movements, suggestive of sleeping or inattentive behavior (more than 10% of saccades
ending off-screen, or more than 70% of viewing time spent off-screen) were excluded from the analysis.
This procedure reduced the total fixation pool from 128,361 to 99,170. We also removed saccades shorter
than 4 or longer than 25 to focus the analysis onto the part of the visual field which was affected in alllesioned monkeys. In total, 73,453 fixations were used for the analysis that used the saliency model.
Computational Modeling
Implementation
To assess the visually guided behavior of monkeys, a validated computational model of visual attention
were used to predict individual eye movements (Figure 2A). Models were created and run under Linux
using the iLab C++ Neuromorphic Vision Toolkit [12]. We used the saliency model of visual attention
framework (Figure 2A) [13, 14]. The Itti and Koch model computes salient locations by filtering the movie
frames along several feature dimensions (color, intensity, orientation, and motion). Center–surround
operations in each feature channel highlight locations that are different from their surroundings. Finally, thechannels are normalized and linearly combined to produce a saliency map, which highlights screen
locations likely to attract the attention of human or monkey observers. To account for a wide range ofspatial frequencies of oriented stimuli, our model uses 9 spatial scales of Gabor filters, with the following
spatial frequencies in cycles/degree (given the screen size, resolution, and viewing distance): 1.1500,
0.5750, 0.2875, 0.1437, 0.0719, 0.0359, 0.0180, 0.0090, 0.0045. This range includes relatively lower
spatial frequency, for which blindsight has been shown to be sensitive when detecting orientation gratings(e.g., [15]).
Comparing Eye Movements to Model Output
To compute the performance of each model, the map values in a window (1.75 = 16 pixels) aroundsaccadic endpoints were compared to 1200 map values collected from locations randomly chosen.
Random sampling was either uniform or drawn from the distribution of saccadic endpoints from all
saccades made by the monkey under test over the entire collection of video clips. This approach is similar
to the image-shuffled analysis method used by others for static images [16-18] and allows evaluation ofmodel performance while discounting possible effects of center bias. For a particular subject, at the onsetof a saccade we measured the value in each model map at the endpoint of the saccade, i.e., the activity inthe map just before the saccade.
Saliency values at monkey vs. random saccade endpoints were analyzed through ROC analysis,
giving rise to an AUC score as described in the main text. An AUC score of 0.5 indicates that the saliency
map model under study is not able to predict monkey eye movements better than chance. Although intheory the maximum achievable score is 1.0, in practice this cannot be reached because of naturalvariability of eye movements across observers: It is not possible to build a single computational model
which can exactly pinpoint the single location of every monkey saccade, since different animals often do
not look at the same location. In previous studies using the same AUC scoring technique, we have shownthat a practical upper bound on achievable AUC scores given inherent inter-observer variations is around
0.7 [8, 19, 20].
Model Optimization Procedure
To evaluate the unique, non-redundant contributions of different visual features to eye movements, weemployed an optimization procedure (Figure 3). We compared the AUC score of the best-fitting model that
includes all features to that of the best-fitting model that includes all but one features. Optimization was
carried out using a Simplex algorithm [21] which was run several times from randomly chosen starting
points to ensure convergence to the global optimum. The optimization procedure gave rise to an optimum
set of feature weights that maximized AUC for each variant of the model and each monkey population
under consideration. To assign error bars to the best-fitting weights, we computed the local Hessian of the
AUC score with respect to the feature weights, and used this to convert a tolerance of +/- 0.01 on the AUC
value into the corresponding tolerance onto weight values. The procedure is as described in [22]. Thismethod allows us to gauge how tightly constrained a given weight is by the eye movement data, as
reflected by the final error bars on weight values.
Evaluation of Color Sensitivity
Visually Guided Saccade Task with Equiluminant Chromatic Stimuli
At the beginning of each trial, the fixation point (FP) appeared at the center of the screen, and monkeys
were required to move their eyes to the FP. The duration of fixation was varied randomly between 400 and1000 ms. If the eye position deviated more than 1.5 degrees from the FP, the trial was aborted. The
saccadic target appeared in the peripheral visual field concurrently with the offset of the FP. Monkeys wererewarded with fruit juice 200-500 ms after correctly making a saccade to the target and then maintaining
fixation for 100-300 ms in the target window (size 2-3 degrees). Target eccentricity was fixed at 10
degrees. Target direction was either upper 30 degrees or lower 30 degrees for both hemifields. A small
percentage of trials with saccadic reaction times less than 80 ms were considered to be trials with
anticipatory saccades and were omitted from the analysis. Trials with saccadic reaction times greater than
700 ms were rare and were also omitted from the analysis. Inter-trial intervals ranged from 1500 to 2000
ms.
Visual Stimuli
Target stimuli were circular spots whose color properties were derived from the
Derrington–Krauskopf–Lennie (DKL) color space [23, 24]. This color space corresponds closely to the typeof segregation that exists along the geniculostriate pathway in early vision [24]. One pathway sums the
inputs of the long- and middle-wavelength cones (L + M), producing a luminance channel that is mostly
sensitive to stimuli varying along the “black–white” dimension in the DKL space. A second pathway
computes the difference between the inputs of the L and M cones (L - M), and is mostly sensitive to stimuli
varying along the “red– green” dimension in the DKL space. A third pathway computes the difference
between the inputs of the short-wavelength cones (S-cones) with the sum of the L- and M-cones [S - (L +M)], and is mostly sensitive to stimuli varying along the “blue-yellow” dimension in the DKL space. These
three channels form the primary visual-cortical inputs via the magnocellular, parvocellular, andkoniocellular layers of the LGN, respectively.
The monitor phosphors were measured using a photo-spectrometer (PR-650, Photoresearch, CA,USA), and the resulting CIE xyY values were used to convert between RGB and DKL color space by the
Stockman & Sharpe cone fundamental [25]. The Stockman & Sharpe cone fundamental are based onhuman data there is a good match with macaque cone sensitivities (e.g. [26]), although there is a caveatthat relative numbers of cone types may differ [27]. The target color stimuli were presented against an
2isoluminant (25 cd/m ) neutral gray background. The luminance contrast was expressed as Weber
contrast, thus the luminance contrast of the black stimuli was represented as -100%. For chromatic stimuli,
the saturation was expressed as the percentage of maximum achievable stimuli by the CRT monitor for
L-M and S-Lum axis.
The size of the target was 0.45 and 0.90 degrees in diameter for monkey T and A, respectively.
2
Luminance of the background and the target was set to 25 cd/m . The size of the target for monkey T was
set so that the monkey cannot respond to the equal size of achromatic target with high luminance contrast
2(0.9 in Michelson contrast) and with dim background luminance (1 cd/m ) presented in the blind spot, thus
ensuring no contribution of light scattering to residual vision. The size of the target for monkey A was set
so that the performance of achromatic stimuli was comparable to that of monkey T.
For a control experiment in Figure 4C, we varied luminance of the chromatic stimuli. The range ofluminance added to the chromatic stimuli was from -16% to +16%, which is maximal in the DKL spacewhen the saturation of the stimuli is 90% of the maximally attainable value in our monitor.
Analysis of Performance
Performance was evaluated by calculating the correct ratio, as the number of trials with saccades toward
the same quadrant as the target stimulus divided by the total number of trials with successful fixation
before stimulus presentation. Saccades were detected when the peak velocity of the polar component
exceeded 200 degrees/s. Then the onset time of the detected saccade was defined as the time point
preceding the detected saccade at which the velocity exceeded 30 degrees/s. The end point of thesaccades was defined as the spatial position at which the velocity of the saccade declined below 30
degrees/s after the saccadic onset. For statistical analysis, Matlab (MathWorks) was used.
Color Task Using Mosaic Stimuli
The mosaic stimuli are composed of 7 by 7 patches. For distractor (Figure S4), the luminance contrasts of2
49 patches were sampled from uniform distribution with the mean luminance 25 cd/m , identical to the2background luminance, and with a range from 80% to 120% of Weber contrast (20-30 cd/m ). In each trial,
the same 49 patches were chosen but the positions were shuffled. The two distractors presented before
fixation offset were identical. For target, the only difference from distractor was that a constant chromatic
contrast (either L-M or S-Lum) was added to the central 5 by 5 patches. After the fixation point was
extinguished, both the target and the distractor were presented. Both of them had the identical mosaic
pattern and the pattern was different from the pretarget distractors. Therefore, the subject could not use
either the pattern of mosaic nor change detection but chromatic contrast to detect the target stimuli.
Chromatic contrast was calculated as the ratio of L-M or S-Lum value to the maximally attainable L-M or
S-Lum value for our CRT monitor. Hence, the chromatic contrasts of L-M and S-Lum are not perceptuallyequivalent. In separate sessions, stimuli were presented either in the affected (red in Figure S4) or normal(blue in Figure S4) hemifields.
Consideration of Previous Results
Our results provide positive quantitative evidence for some residual saliency computation even when V1 isabsent, and hence when V1 cannot support the computation of the low-level visual features contributing tosaliency. We found that motion was the strongest feature contributing to saliency-guided eye movements(Figure 3D) in normal monkeys, likely because we use dynamic stimuli where motion provides important
clues to interesting objects. Motion remained strong in the affected hemifield of lesioned monkeys,
consistent with laboratory experiments [28] and probably supported by geniculo-extrastriate andretino-tectal pathways [29-33]. That the unique contribution of orientation is nearly abolished in theaffected field is not surprising given that our Gabor filters approximate canonical V1 simple cells [34].
Moreover, while blindsight patients can discriminate orientation of bars (e.g., horizontal vs. vertical),discrimination of the orientation of a grating in a circular aperture, which is a better test for sensitivity inorientation than using a single bar, was at chance level in patient GY, one of the well-studied blindsightpatients [35]. This is consistent with our results that isolated the contribution of orientation. Note, however,that blindsight subjects can detect – in forced-choice paradigms – flickering oriented gratings, withrelatively low spatial frequencies (e.g., [15]) as used in our model (less than 2 cycles/degree), Two
differences may exist: first, ability to detect a feature when forced might not necessarily render that featureimportant in spontaneously guiding attention; second, our orientation saliency model is purely spatial,
while stimuli used in previous experiments also had a temporal component (flickering, onset transients).
The unique contribution of luminance was weak in normal monkeys but stronger in lesioned monkeys(Figure 3D). This is not contradictory to our previously reported decreased sensitivity to luminance [2];indeed our results here simply show that despite decreased sensitivity, lesioned monkeys strategicallyrelied more on luminance to decide where to look. Residual guidance to color stimuli was a more
surprising prediction of our computational analysis [30, 31, 36-38]. The non-zero contribution of chromaticfeatures in the affected field supports non-zero sensitivity for color in blindsight, for both L-M and S-conechromatic features. Furthermore, our model predicts a stronger role for S-cone dependent signals inblindsight compared to control. This may reflect a strategic change in allocating attention given theimpoverished residual feature representations postlesion or retrograde degeneration of retinal ganglion
cells with selective loss of P beta cells depending on the time after the lesion [39-41]. It should be notedthat the P beta cells, which are responsible for color-opponent processing, are not completely eliminated(e.g., 85% loss of P beta cells 8 years after lesions in [39]) and the small number of surviving cells may
contribute to residual guidance to color saliency. Our control experiments confirm that monkeys with V1
lesion are sensitive not only to L-M but also to S-cone signals, and validate our computational prediction. It
is interesting to note that the relatively poorer performance in L-M stimuli than in S-Lum stimuli (Figure
S4A-B and S4C-D) may be explained by the reduced number of P beta cells by the retrograde
degeneration [39-41]. Prior studies found no sensitivity to S-cone stimuli in human blindsight [30, 31] andno color sensitivity in the retino-tectal pathway of normal monkeys [42]. However, our results provide
converging evidence with both free-viewing and laboratory stimuli for retained chromatic processing inblindsight monkeys. That such residual chromatic processing may contribute to preattentive guidance ofattention and gaze is not necessarily contradictory to previous findings that color discrimination or
identification can be abolished in blindsight [43], as detection and discrimination may recruit distinct
mechanisms. In a control task, we varied the luminance of chromatic targets and tested localization
performance. Performance was higher than chance at all possible levels of luminance (Figure 4C),
consistent with a previous report (localization task similar to ours but with reaching instead of saccades)
where performance was higher than chance when stimuli were red or blue [37]. It is still an open question
to determine the pathways for chromatic processing in blindsight, given possible global reorganization ofpathways in the brain, as demonstrated in V1-lesioned animals [44] and patients [45].
In the free-viewing condition, subjects have no alerting cue for when to make saccades. Previous
studies about human and monkey blindsight [46-48] suggest that cueing facilitates residual vision, in a waythat may depend on the age of the lesion. However, we also note that cueing is not necessary forblindsight [49]. Our study shows that our blindsight monkeys spontaneously shifted their attention tonatural, complex stimuli without alerting cues.

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