articleseidemannlab.cps.utexas.edu/~eyal/publications/chen... · 2012-05-11 · neuron article...

Neuron

Article

Attentional Modulations Relatedto Spatial Gating but Not to Allocationof Limited Resources in Primate V1Yuzhi Chen1 and Eyal Seidemann1,*1Center for Perceptual Systems, Department of Psychology and Section of Neurobiology, University of Texas at Austin, Austin,

TX 78712, USA

*Correspondence: [email protected] 10.1016/j.neuron.2012.03.033

SUMMARY

Attention can modulate neural responses in sensorycortical areas and improve behavioral performanceinperceptual tasks.However, the nature andpurposeof these modulations remain under debate. Herewe used voltage-sensitive dye imaging (VSDI) tomeasure V1 population responses while monkeysperformed a difficult detection task under focal ordistributed attention. We found that V1 responses atattended locations are significantly elevated relativeto actively ignored or irrelevant locations, consistentwith the hypothesis that an important goal of atten-tion in V1 is to highlight task-relevant information.Surprisingly, these modulations were indistinguish-able under focal and distributed attention, suggest-ing a minor or no role for attention as a mechanismfor allocating limited representational resources inV1. The response elevation at attended locations isadditive, is widespread, and starts shortly beforestimulus onset. This elevation could contribute tospatial gating by biasing competition in subsequentprocessing stages in favor of attended stimuli.

INTRODUCTION

Behavioral performance in perceptual tasks such as detection

and discrimination can be significantly improved by prior knowl-

edge regarding the stimulus’s location and features (e.g., Dun-

can, 1980; Posner, 1980). This improvement is thought to be

mediated by top-down attentional mechanisms that modulate

sensory representations based on task demands. Consistent

with this possibility, experiments using single-unit recordings in

behaving monkeys (e.g., Moran and Desimone, 1985; Haenny

et al., 1988; Motter, 1993; Treue and Maunsell, 1996; McAdams

andMaunsell, 1999; Seidemann and Newsome, 1999; Treue and

Martınez Trujillo, 1999; Reynolds et al., 2000; Bichot et al., 2005)

and fMRI in human subjects (e.g., Kastner et al., 1999; Ress

et al., 2000; Buracas and Boynton, 2007) revealed that task

demands can significantly modulate neural responses in visual

cortical areas. However, the purpose of these modulations is still

under debate.

It is commonly assumed that sensory systems have limited

representational resources and that the goal of attention is to

allocate these resources based on task demands (e.g., Broad-

bent, 1958). Selection, however, is necessary even under condi-

tions in which the sensory system’s ability to represent multiple

stimuli is not limited, because the task may require the subject

to use only a subset of the available stimuli and ignore others.

Therefore, another possible goal of attention is to gate task-

irrelevant stimuli in order to limit their access to circuits that

control behavior (e.g., Allport, 1993). These two possible goals

of attentions are distinct and, as discussed below, have different

predictions regarding the expected physiological effects of

attention. However, these two forms of attention are not mutually

exclusive and could both operate in the same cortical area.

To illustrate the differences between these two attentional

mechanisms, consider the following toy example (Figure 1).

You are presented with four coins. On half of the trials all four

coins are tails, and on the other half three are tails and one is

a head. Your task is to report whether a head is present, and if

so, where it is located. What makes the task difficult is that

instead of getting direct access to the coins, you observe a

‘‘noisy sensory representation’’ of each coin; consequently,

there is a probability that the observed coin face is different

from its true value. The fidelity of the sensory representation is

represented by the ‘‘probability of spontaneous flip’’ (pf, indi-

cated by the red bar near each coin).

Consider the following two versions of the task. In the focal-

attention version, you are cued in advance as to the only possible

coin location where the headmay have occurred (Figures 1B and

1D; cue indicated by blue square). In the distributed-attention

version, all four coin locations are cued, and therefore, the head

couldhaveoccurredat anyof these locations (Figures1Aand1C).

Now compare two scenarios, one in which your sensory repre-

sentation is limited (Figures 1C and 1D), and one in which it is

unlimited (Figures 1A and 1B). When the sensory representation

has limited resources, attention allocates these resources ac-

cording to the task, and the fidelity is high under focal attention

(pf = 0.1) and lower under distributed attention (pf = 0.15).

When the sensory representation is not limited, the fidelity under

both focal and distributed attention is the same (pf = 0.1).

Consider first the no-resource-limit case. Intuitively, even in

this case, the task is more difficult under distributed attention

than under focal attention. To see this, consider an example in

which the bottom right coin is a head that has not flipped.

Neuron 74, 557–566, May 10, 2012 ª2012 Elsevier Inc. 557

mailto:[email protected]

http://dx.doi.org/10.1016/j.neuron.2012.03.033

Figure 1. Toy Example of a Behavioral Task Illustrating the Signatures of Two Forms of Attentional Modulations: Allocation of Limited

Representational Resources and Gating of Task-Irrelevant Information

The goal of the task is to report whether Washington’s head is present and, if so, where. In half of the trials there is no head and in the other half there is one head.

Each coin has a spontaneous flip probability (red bar) that represents the probability that the observed coin face is different from the true coin face.

(A and C) Distributed attention.

(B and D) Focal attention.

The cue (blue square) indicates where the head could occur.

(A and B) No resource limit.

(C and D) Resource limit.

The number in each panel indicates the expected accuracy of an observer that uses the optimal strategy to perform this task. The optimal strategy for flip

probabilities% 0.2 is as follows: report ‘‘no head’’ if all observed coins are tails; otherwise, report ‘‘head’’ and select any of the observed head locations. See text

for additional details.

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Attentional Modulations in Primate V1

However, one of the other three coins has flipped and it is also

a head. In the distributed-attention case, you have to guess

which one of the two observed heads (if any) was originally

a head. On the other hand, in the focal-attention case, you

know that the only location where the head could have occurred

is the bottom right and, therefore, have a higher chance of re-

porting correctly that this location contains the head. Hence,

despite the equal fidelity of the representation in focal and

distributed attention, behavioral accuracy under distributed

attention will be lower. The numbers in each panel show the ex-

pected accuracy of an observer that uses an optimal strategy to

perform this task. The accuracy of this observer is reduced by

19% in the distributed attention task versus the focal attention

task. This example illustrates that a difference in accuracy

between focal and distributed attention is not, by itself, evidence

in favor of limited representational resources.

Now consider the case in which the sensory representation

has limited resources and the probability of spontaneous flip is

higher under distributed attention than under focal attention. It

is easy to see that this drop in fidelity must lead to an additional

drop in accuracy under distributed attention. In our toy example,

the accuracy of the optimal observer drops from 90% in the

focal-attention case to 60% in the distributed-attention case,

which is lower than the accuracy in the distributed-attention

case under the unlimited resource scenario.

558 Neuron 74, 557–566, May 10, 2012 ª2012 Elsevier Inc.

Our toy example shows that the signature of attention as a

mechanism for allocation of limited resources is enhanced neural

sensitivity to attended stimuli under focal attention versus

distributed attention (by increased signal and/or by reduced

noise).

As discussed above, another possible goal of attention is to

limit the behavioral impact of task-irrelevant stimuli by selectively

blocking irrelevant signals. In our toy example, we represent

this gating mechanism as the color of the coin. Coins at cued

locations are gold colored, while coins at uncued (and therefore

irrelevant) locations are silver colored. The color of the coin is

analogous to a neural bias that highlights task-relevant locations

and allows subsequent processing stages to selectively gate

task-irrelevant signals.

Our toy example shows that the signature of attention as

a mechanism for gating task-irrelevant information is a response

bias in favor of relevant (attended) versus irrelevant (ignored)

locations. This bias signal may be associated with enhanced

neural sensitivity at attended locations, but as long as the

enhanced sensitivity is the same under focal and distributed

attention, it would be inconsistent with a limited resource

mechanism.

To study these two forms of attention experimentally, we used

VSDI to measure V1 responses while monkeys performed a

detection task analogous to our toy example above. This task

Time

500ms 500ms 500~1200ms 300ms 300ms 300ms

Saccade to Target

Imaging with VSD

Fixation

Single-cue

Multiple-cues

Fixation Dimming

Mask Only

Mask+Target

A

EDCB

Figure 2. Task and Behavioral Performance under

Different Attentional Conditions

(A) Sequence of events within a single trial. After initial

fixation, either a single cue appeared at one of the four

possible locations (single cue) or four cues appeared at all

four locations (multiple cues). After an additional variable-

duration fixation period, dimming of the fixation point

provided a temporal cue that 300 ms later the visual

stimulus will appear. In half of the trials the visual stimulus

was composed of four 10% contrast masks of one orien-

tation (‘‘mask only’’). In the remaining trials, a low-contrast

(3.5%–4.5%) orthogonal target was added to one of the

masks (‘‘mask-plus-target’’). In target-present trials the

monkey was required to shift gaze to the target location as

soon as it detected the target. In target-absent trials the

monkey was required to maintain fixation.

(B) Average percent correct (PC) of the twomonkeys under

focal attention (single cue, red) and distributed attention

(multiple cues, blue). The percent correct in single-cue

trials was computed only for trials without a distracter.

(C) Average across experiments of median reaction times

in hit trials in the two attentional conditions.

(D) Same as (C) but in false-alarm trials.

(E) Average probability that the monkey would shift gaze to

the distracter location (PD) (the location opposite to the

cue) in trials with (magenta) and without (cyan) a distracter.

Here and in all other figures: N, the number of experiments

for each monkey; p, the p value of a paired t test across

experiments; error bars represent SEM.

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(described below) allowed us to measure simultaneously the

behavioral and neurophysiological effects of both forms of atten-

tion. In single isolated neurons, VSDI signals are linearly related

to membrane potential across the entire physiological dynamic

range (e.g., Salzberg et al., 1973). In the primate cortex, recent

results suggest that the VSDI signal at any given location is

proportional to the summed membrane potential of a population

of neurons, integrated over a Gaussian-shaped area with stan-

dard deviation (SD) of �230 mm (Chen et al., 2012). Therefore,

attentional modulations measured with VSDI are likely to reflect

the inputs that V1 neurons receive from top-down circuits rather

than the attentional modulations of the spiking output of V1

neurons. Recent VSDI studies in behaving primates demonstrate

that VSDI is highly sensitive and can provide reliable information

about visual stimuli even below the subject’s behavioral detec-

tion threshold (Chen et al., 2006, 2008a). Therefore, VSDI al-

lowed us to studywhether, and how, these two forms of attention

modulate neural population responses over a large V1 region

with high sensitivity and at high spatial and temporal resolution.

RESULTS

We trained twomonkeys to detect a low-contrast oriented target

(analogous to ‘‘head’’ in our toy example) that appeared on half of

the trials on top of one of four evenly spaced higher-contrast

masks of orthogonal orientation (Figure 2A). We first used VSDI

to determine the layout of the retinotopic map in the imaged

area (Yang et al., 2007). We then positioned the stimuli so that

one of the four stimulus locations fell inside the receptive fields

of V1 neurons at the center of the imaged area. To report detec-

tion, themonkey had to shift gaze to target location within a short

time window following target onset. As in our toy example, at the

beginning of each trial, a cue indicated to the monkey whether to

attend to one of the four possible locations (single-cue, ‘‘focal-

attention’’) or to all four locations (multiple cues, ‘‘distributed-

attention’’). To ensure that themonkeywas ignoring the irrelevant

locations in focal attention trials, in half of those trials, a distracter

identical to the target could appear at the location opposite to the

cue. Themonkey had to ignore this distracter. Finally, blank trials

with no cue and no visual stimulus, and control trials with cue(s)

but no visual stimulus, were randomly intermixed with all other

trial types (see Experimental Procedures for additional details).

This task allowed us to measure V1 responses to the same

physical visual stimuli under three attentional states: when only

the location corresponding to the imaged area was cued (focal

attention, ‘‘attend-in’’), when it was one of four cued locations

(‘‘attend-distributed’’), and when another location was cued

and the imaged location had to be ignored (focal attention,

‘‘attend-out’’) (Figure 3A, top row). As illustrated in our toy

example (Figure 1), this task allowed us to examine the two

possible forms of attentional effects in V1. By comparing

responses in ‘‘attend-in’’ and ‘‘attend-distributed’’ states, we

tested the hypothesis that attention allocates limited representa-

tional resources in V1. By comparing responses in ‘‘attend-in’’

and ‘‘attend-out’’ states, we tested the hypothesis that attention

in V1 helps to spatially gate task-irrelevant signals.

Attentional Modulations of Behavioral PerformanceAs expected, the monkeys’ behavioral performance was signifi-

cantly better in terms of accuracy (Figure 2B) and reaction times


σmajor

σminor

AB

C D E F

Figure 3. Spatial Patterns of V1 Population Responses under Three Attentional States in Monkey 1

(A) Top row shows a schematic diagram of the three attentional states. The black circle represents the receptive field (RF) of the neurons at the center of the

imaged area, while thewhite circles represent the cue. Att-in: the receptive field location is cued. Att-out: the cued location is outside of receptive field. Att-dstr: all

four locations are cued. The bottom two rows show the average response maps in mask (middle row) and mask-plus-target (bottom row) trials under the

attentional state indicated by the diagram in the top row. At each location the responses are averaged over the first 200ms after stimulus onset. The average in the

100 ms before stimulus onset is then subtracted from the amplitude at each location. The ellipsoidal region at the center of the imaged area shows the area

directly activated by themask or mask-plus-target. The response is anisotropic due to the anisotropy in themap of visual space in V1. The response is dominated

by the higher-contrast mask and is therefore very similar in mask and mask-plus-target trials. Note that in attend-out state the overall activity level in the area

surrounding the active region is lower than in attend-in and attend-distributed states.

(B) Two-dimensional (2D) fit to the spatial patterns of V1 population response. The responses were fitted with a 2D Gaussian plus a uniform baseline. The space

constants along the major and minor axis of the Gaussian are indicated. Top: 2D Gaussian fit to response to mask in attend-in state. Bottom: one-dimensional

horizontal slice through the two-dimensional fit, indicated by the black line in the top panel. The baseline, amplitude, and space constant (s) are indicated. For

comparison, the spatial response profile in a 1-mm-wide strip along this slice is indicated by a dashed line. The shaded area indicates ± 1 SEM across

experiments. Right: same as bottom but along a vertical slice.

(C–F) Mean and SEM of the fitted parameters across 11 experiments in monkey 1. The color represents the attentional state as indicated in the top row of (A).

Asterisks indicate significant differences (p < 0.05 based on paired t test across experiments). The only significant differences are between the magnitude of the

baseline in attend-out and the other two attentional states.

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(Figures 2C and 2D) under focal attention than under distributed

attention (see also Figure S1 available online). If these differ-

ences in behavioral performance are mediated, at least partially,

by top-downmodulations in V1, and if target representation in V1

is a limited resource, wewould expect the VSDI-measured target

sensitivity to be higher under focal attention than under distrib-

uted attention.

In addition, inmost trials themonkeys successfully ignored the

distracter. However, the probability that themonkeys would shift

gaze to the location opposite to the cue in focal attention trials

was significantly higher in the presence of the distracter (Fig-

ure 2E), indicating that the monkeys were incapable of fully

ignoring the distracter. If selective gating of signals from ignored

locations is mediated, at least partially, by top-down modula-

tions in V1, we would expect the VSDI-measured V1 responses

to be biased in favor of attended versus ignored locations. Our


next stepwas therefore to examine V1 responses under the three

attentional states.

Spatial Distribution of Attentional Modulations in V1We used VSDI to measure V1 population responses while the

monkeys performed the detection task. Figure 3A shows the

average spatial patterns of V1 population responses for each

of the two visual stimuli under the three attentional states in

monkey 1 (after subtracting the average responses in blank

trials). Consistent with our previous results (Chen et al., 2006,

2008a; Palmer et al., 2012), the visual stimuli activated a localized

ellipsoidal region that subtended multiple mm2 in V1. Because

target contrast (3.5%–4.5%) was lower than mask contrast

(10%), the response was dominated by the mask, consistent

with single-unit masking results (e.g., Busse et al., 2009)

and with the detrimental effect of the mask on the monkeys’

C

B

D

A

Figure 4. Summary of Attentional Effects in the Two MonkeysAverage and SEM of the normalized amplitudes of the Gaussian and baseline

components under the three attentional states. Because attentional modula-

tions were stimulus independent (see text), results from target-absent and

target-present trials were combined before fitting the spatial response maps.

The amplitudes of the Gaussian and baseline components were normalized by

the average amplitude of the Gaussian.

(A) Normalized amplitude of 2D Gaussian in monkey 1 is not affected by

attentional state.

(B) Normalized amplitude of baseline is significantly higher in attend-in and

attend-distributed than in attend-out in monkey 1.

(C and D) Same as (A) and (B) but for monkey 2. The overall quality of the VSDI

signals in monkey 2 was lower than inmonkey 1, which could have contributed

to the lower attentional effect in this monkey.

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detection threshold. However, peak responses in target-present

trials were significantly higher than in target-absent trials (one-

tailed paired t test, p < 0.01 for both monkeys; combined across

all three attentional states). The spatial profile of the response

was similar in the three attentional states. However, the activity

over the entire imaged area was elevated in attend-in and

attend-distributed trials (Figure 3A, note the lighter colors in

attend-in and attend-distributed conditions). To quantitatively

analyze the attentional effects, we fitted the responses with

a two-dimensional (2D) Gaussian plus a spatially uniform base-

line (Figure 3B). These two spatial components provided

a good fit to the observed responses (r2 > 0.9 for all stimulus/

cue combinations in both monkeys).

The attentional state significantly modulated the spatially

uniform baseline component (Figure 3F) but had no significant

effect on the amplitude or the shape of the Gaussian component

(Figures 3C–3E). The baseline was elevated in attend-in and

attend-distributed conditions relative to attend-out condition,

which was indistinguishable from the baseline in blank condition

(trials with no cue and no visual stimulus). We obtained similar

results in monkey 2 (Figure S2).

To test whether the attentional state affected the target-

evoked response (difference between target-present and

target-absent response), we performed paired t tests on the

amplitudes of the target evoked response in the three attentional

states. None of the test showed a significant effect (p > 0.13).

We therefore combined the responses across the two visual

stimuli.

A summary of the results from the two monkeys (combined

across the two visual stimuli; Figure 4) reveals robust attentional

modulations of the baseline response which are several-fold

larger than the response evoked by the target. In both monkeys,

baseline activity was indistinguishable between attend-in and

attend-distributed (paired t test, monkey 1, p = 0.95; monkey

2, p = 0.57), but significantly lower in attend-out relative to

the other two attentional states (paired t test, monkey 1, p <

0.0052 for both tests; monkey 2, p < 0.015 for both tests); base-

line activity in attend-out and blank conditions was indistinguish-

able (paired t test, monkey 1, p = 0.29; monkey 2, p = 0.39), and

this was true at the location where the distracter could appear

(opposite to the cue) as well as the two other unattended

locations (Figure S3). The Gaussian amplitude was independent

of attentional state (paired t test, p > 0.094 for all tests). To

quantify this effect, we normalized all responses by the average

amplitude of the Gaussian component. The average normalized

amplitude of the attentional baseline elevation was 23% in

monkey 1 and 12% in monkey 2, while the average normalized

target-evoked response (additional response evoked by the

target in the presence of the mask) was only 4.7% in monkey 1

and 7.1% in monkey 2.

Attentional Modulations of Response VariabilityWhile responses under focal and distributed attention are the

same on average, it is still possible that attention enhances

neural sensitivity under focal attention by modulating neural

noise (Cohen and Maunsell, 2009; Mitchell et al., 2009). To

examine this possibility, we computed the SD of the response

amplitude across trials and the spatial correlations of the

response variability (Chen et al., 2006). Neither the SDs nor the

spatial correlations varied significantly with attentional state

(Figure 5), suggesting that in our task, attention does not lead

to significant changes in these noise properties at the population

level in V1.

Time Course of Attentional Modulations Relatedto Spatial GatingTo determine when the attentional modulations are initiated and

how they evolve over time, we compared the dynamics of the

baseline component in the three attentional states (see Experi-

mental Procedures). Our results show that the attentional

modulations start to build up about 100 ms before the stim-

ulus-evoked response (compare Figures 6A and 6D with Figures

6C and 6F) and about 200 ms after fixation point dimming.

Similar results were obtained in control trials in which no visual

stimulus was presented after the cue(s) (Figures 6B and 6E).

These modulations, therefore, are stimulus independent, are

preparatory in nature, and are timed to occur shortly before

stimulus onset.

Possible Confounding EffectsOur results suggest that top-down mechanisms can modulate

neural population responses in V1 based on stimulus relevance,


B

D

A

C

Figure 5. Attentional State Does Not Change Properties of theVariability at the Level of Neural Populations in V1

(A) Standard deviation across trials of VSDI responses is not affected by

attentional state in monkey 1. VSDI response was averaged for each trial

during the 200 ms after stimulus onset in a 1 3 1 mm2 region centered at the

peak of the fitted 2D Gaussian. The standard deviation was normalized by the

fitted average peak response.

(B) Spatial correlations across trials between two locations as a function of

their spatial separation are not affected by attentional state in monkey 1. One

of the two locations was in the central 1 3 1 mm2 region; the other was

anywhere in the region-of-interest.

(C and D) Same as (A) and (B) but for monkey 2.

In (A) and (C), error bars represent SEM. In (B) and (D), shaded areas represent

SEM.

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but before we can conclude that the elevated baseline reflects

a genuine top-down attentional signal, we have to rule out

several confounding effects.

First, it is possible that the observed baseline modulations are

due to direct visual response to the cue. This seems unlikely

because the interval between cue offset and stimulus onset

(800–1500 ms) was long and variable and because cue diameter

(3�) was much larger than the receptive fields of the neurons in

the imaged area (<1�). Nevertheless, to rule out this possibility,

we examined the time course of the baseline modulations

time-locked to cue offset. These time courses were indistin-

guishable between the three attentional states (Figure S4),

demonstrating that the attentional modulations (which are

time-locked to stimulus onset) are not due to direct visual

responses to the cue.

Second, systematic differences in fixational eye movements

between the attentional states could have contributed to the

observed variations in V1 responses. This possibility seems

unlikely given that the attentional modulations start before the

stimulus-evoked responses (Figure 6). Nevertheless, to rule out

this possibility, we compared several eye position statistics in

the three attentional states. Our results reveal no significant

differences in these statistics depending on the attentional

condition (Figure S5), providing further support for the top-

down nature of the observed modulations.


DISCUSSION

What is the purpose of the observed attentional modulations?

One possible goal of attention is to allocate limited representa-

tional resources based on task demands (e.g., Broadbent,

1958). Another possible goal of attention is to limit the access

of task-irrelevant stimuli to circuits that control behavior (e.g., All-

port, 1993). If the representation of multiple visual targets in V1

was a limited resource that could be controlled by attention,

we would have expected V1 target sensitivity at attended loca-

tions to be higher under focal attention than under distributed

attention (Figures 1C and 1D). Our finding that V1 population

responses at attended locations are indistinguishable under

focal and distributed attention suggests that in our task, and at

the level of neural populations, target sensitivity in V1 may not

be a limited resource that can be enhanced by focal attention.

We find that behavioral performance is improved under focal

attention relative to distributed attention (Figures 2B–2D). As

illustrated by our toy example (Figure 1), behavioral improvement

under focal attention is expected even if V1 target sensitivity is

not limited and is identical in focal and distributed attention. A

simple analysis based on signal detection theory shows that

the observed behavioral improvement in accuracy under focal

attention is consistent with no changes in neural sensitivity under

focal and distributed attention (Suppl. Figure 6; see also Eckstein

et al., 2000; Palmer et al., 2000; Pestilli et al., 2011). This analysis,

therefore, provides further support to the hypothesis that in our

task, target sensitivity is not a limited resource that can be

enhanced by focal attention.

While our physiological and behavioral results appear to be

inconsistent with attention as a mechanism for allocating limited

resources in V1, we cannot rule out the possibility that such

a mechanism operates in V1 in other tasks. Similarly, we cannot

rule out the possibility that such a mechanism operates in V1 but

affects a small subset of the neurons, or enhances the responses

of some neurons while suppressing the responses of others,

leading to a population response that is unaltered. However,

what makes our negative result compelling is the contrast with

the robust gating-related attentional modulations obtained in

the same experiments. Therefore, we conclude that in our

task, and at the level of neural populations in V1, attentional

effects related to allocation of limited resources either are absent

or are much weaker than effects related to gating of irrelevant

stimuli.

Previous studies in behaving monkeys have demonstrated

that increased task difficulty can lead to enhanced neural

responses in area V4 (Spitzer et al., 1988; Boudreau et al.,

2006). Because our distributed attention trials weremore difficult

than the focal attention trials, increased vigilance in distributed

attention trials could have led to enhanced responses in V1

that masked a reduction in response in distributed versus focal

attention trials. However, it seems unlikely that such opposing

effects precisely canceled each other to produce indistinguish-

able responses in focal and distributed attention. In addition, it

is not clear whether similar effects of vigilance are present in

V1. Finally, in our task target contrast was near detection

threshold even in focal attention trials and the two trial types

were randomly intermixed. Therefore, it seems less likely that

B

E

A

D

C

F

Figure 6. Time Course of Attentional Modulations

and Stimulus-Evoked Responses

Time courses of the amplitudes of the Gaussian and

baseline components were normalized by the peak

amplitude of the Gaussian in the mask-plus-target

condition.

(A) Time courses of the normalized baseline component in

attend-in and attend-distributed trials in monkey 1. The

time course of the normalized baseline component in

attend-out trials was subtracted from the time courses in

the other two attentional states.

(B) Time course of normalized baseline component in

control trials with cue but no visual stimulus. In the

experiments in monkey 1 we only included control trials

with multiple cues. Therefore, we show the time course in

multiple-cue control trials after subtracting the time course

in blank trials.

(C) Time courses of the normalized Gaussian component

in mask (cyan) and mask-plus-target (magenta) in

monkey 1.

(D–F) Same as (A) and (B) but for monkey 2. In the exper-

iments in monkey 2 we included control trials for all three

attentional states, so in (E) we show the time course in

attend-in and attend-distributed control trials after sub-

tracting the time course in attend-out control trials. The

shaded areas represent ± 1 SEM.

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differences in attentional load between focal and distributed

attention affected our results.

The observed differences between V1 responses at attended

and ignored locations are consistent with the hypothesis that an

important goal of attention in V1 is to limit the behavioral effect of

task-irrelevant visual stimuli. The elevated baseline at attended

locations could contribute to this selective spatial gating by

biasing competition in subsequent processing stages in favor

of task-relevant stimuli (Desimone and Duncan, 1995). If this

top-down signal itself was a limited resource, we would have ex-

pected to see differences between attentional modulations in

focal and distributed attention. However, the baseline elevation

is indistinguishable between focal and distributed attention,

demonstrating that the top-down mechanism mediating this

effect is not a limited resource (at least when the number of

possible locations is four).

Additive versus Nonadditive Attentional EffectsThe observed attentional modulations are additive and stimulus

independent. Because VSDI signals measure changes in

membrane potentials, this result implies that in our task, the

top-down input that V1 neurons receive is stimulus independent.

This is consistent with our findings that the attentional effect

starts before stimulus onset and can occur even when the visual

stimulus is absent. However, it is important to note that, due to

the nonlinear relationship between membrane potential and

spikes (reviewed in Priebe and Ferster, 2008), the additive

effects observed with VSDI could lead to nonadditive modula-

tions at the level of V1 spiking activity. Specifically, due to the

Vm-to-spikes nonlinearity, the baseline elevation of membrane

potential could have a reduced effect at the level of spiking

activity when the response is weak (e.g., before stimulus onset

or at locations far from stimulus center), and an enhanced effect

when the response is strong (e.g., near the peak of the stimulus

evoked response). The possible effects of such nonlinearity on

the predicted attentional modulations at the level of spiking

activity are illustrated in Figure 7. This nonlinearity could lead

to a small increase in the firing rates of V1 neurons in the absence

of visual stimulation. Such an effect could be difficult to detect

using single unit electrophysiology but may be more prominent

in population responses. Consistent with this possibility, a recent

study of attentional modulations at the level of single neurons in

V1 found no baseline modulations in the absence of the stimulus

or at very low stimulus contrasts (Thiele et al., 2009), while fMRI

results in V1 show clear baseline modulations even when the

stimulus is absent (e.g., Buracas and Boynton, 2007; Murray,

2008; Pestilli et al., 2011).

Temporal Dynamics of Attentional ModulationsThe attentional signals are initiated after fixation point dimming

and shortly before the visually evoked responses (Figure 6).

This result implies that top-down modulations can be anticipa-

tory in nature, consistent with previous studies (e.g., Ghose

and Maunsell, 2002), and do not require extrastriate cortex to

first process the visual stimulus. In fact, the attentional signal

can be present even in the absence of the visual stimulus

(Figures 6B and 6E), consistent with some electrophysiological

(Luck et al., 1997; Reynolds et al., 2000; Williford and Maunsell,

2006) and fMRI (Kastner et al., 1999; Ress et al., 2000) results.

Spatial Scale of Attentional ModulationsThe attentional modulations in V1 operate at a larger spatial scale

than the stimulus-evoked response. Because the baseline eleva-

tion extends beyond our imaged area, we cannot determine the


BA

C

Figure 7. A Schematic Illustration of Possible

Effects of Attention at the Level of Spiking Activity

Given our VSDI Results

The figure shows the spatial profile (A) and temporal

dynamics (B) of attentional effects measured with VSDI

(top panels) and the predicted patterns of modulations at

the spiking level (lower panels) assuming a nonlinear

relation between VSDI and spiking activity similar to the

one observed between membrane potential and spiking

activity in single neurons (C). The attentional effect

(difference between attend-in and attend-out) is indicated

by the dashed black line. An additive effect in space and in

time at the level of membrane potential can lead to a

nonadditive (stimulus dependent) effect at the level of

spiking activity.

Neuron


exact spatial extent of this top-down signal. However, since we

observed no baseline elevation in attend-out trials, the top-down

signal must have a limited spatial extent, at least in focal atten-

tion trials. In distributed attention trials, top-down signals could

activate simultaneously four broad but separate V1 regions

peaked at the representation of the possible stimulus locations,

consistent with a recent finding in humans (Muller et al., 2003).

Alternatively, a larger contiguous region could be activated,

such as the V1 region corresponding to a ring at target eccen-

tricity. The spatial extent and shape of the top-down attentional

signal could be addressed in future VSDI experiments by

systematically shifting the position of the stimuli relative to the

position of the receptive fields in the imaged area.

Relation to Prior Studies of Attentional Modulationsin V1 using Electrophysiology and fMRIOur results are relevant to the ongoing controversy regarding

the magnitude and nature of attentional effects in V1. Single-

unit studies can reveal consistent attentional modulations in

macaque V1 (e.g., Motter, 1993; Luck et al., 1997; Roelfsema

et al., 1998; Ito and Gilbert, 1999; McAdams and Maunsell,

1999; Marcus and Van Essen, 2002; Roberts et al., 2007; Thiele

et al., 2009). However, these effects tend to be weak (but see

Chen et al., 2008b) and delayed and are typically observed

only in the presence of visual stimulation. In contrast, brain

imaging studies using fMRI in human subjects reveal pro-

nounced attentional modulations in V1 (e.g., Kastner et al.,

1999; Ress et al., 2000; Buracas and Boynton, 2007; Pestilli

et al., 2011) that occur even in the absence of visual stimulation.

One possible explanation for this discrepancy is that fMRI BOLD

signals amplify attentional effects by pooling weak modulations

over large populations of neurons. A second possibility is that

attention operates differently in humans and in macaque

monkeys. Finally, it is possible that some attention related

BOLD signals reflect direct modulations of hemodynamic


responses that are independent of local

neural activity (e.g., Sirotin and Das, 2009). The

robust attentional modulations of V1 population

responses reported here are consistent with

the first possibility and provide support to the

general hypothesis that responses that might

be weak and heterogeneous at the level of

single neurons could have a substantial impact at the level of

neural populations (for review, see Seidemann et al., 2009).

Summary and conclusionsIn summary, our results show that despite significant differences

in behavioral performance between focal and distributed atten-

tion, V1 responses at attended locations are indistinguishable

under these two attentional states. These results suggest that

in our task, the representation of visual targets in V1 is not

a limited resource that can be enhanced under focal attention.

However, our results reveal robust elevation of V1 activity based

on stimulus relevance. Responses are elevated over a large

region centered on the attended locations and are maintained

at a default low state at ignored locations. This additive elevation,

which is initiated shortly before stimulus onset, is likely to

contribute to the ability of subsequent processing stages to

selectively gate task-irrelevant sensory signals.

EXPERIMENTAL PROCEDURES

Behavioral Task and Visual Stimulus

Two monkeys were trained to detect a small oriented target that appeared at

one of four fixed locations on top of a background of four orthogonal masks

(Figure 2A). Each trial began when a small bright fixation spot (0.1� 3 0.1�)appeared at the center of the screen. The monkey was required to fixate the

spot for 500ms and thenwas cued for another 500ms to pay attention to either

one of the four locations (single cue) or to all four locations (multiple cues). The

cue was a 0.02� thick bright circular ring with diameter of 3� centered on the

possible target location. After an additional delay of 500–1200 ms after cue

offset, the fixation spot dimmed, indicating that the visual stimuli (mask or

mask-plus-target) would appear 300 ms later. The target was a Gabor patch

with s = 1/6�, spatial frequency = 2.76 cyc/�, phase = 90�, eccentricity =

2.5�–3�, and orientation = 0� for onemonkey and 90� for the other. The contrastof the target was near the monkey’s detection threshold (3.5%–4.5%). The

masks differed from the target only in their orientation, which was orthogonal

to that of the target, and their contrast which was 10%. The target was pre-

sented for 300 ms in half of trials, while the four background masks were pre-

sented at the same time in all trials. In order to receive a reward, the monkey

Neuron


was required to make a saccadic eye movement to the target location in

target-present trials or to hold fixation during a period of 600 ms after stimulus

onset in target-absent trials. In addition, in half of the single-cue trials a dis-

tracter (identical to target) was introduced to the location opposite the cue.

The monkeys were required to ignore the distracter. We also included control

trials in which the cue was presented but no visual stimulus appeared and

blank trials in which no cue and no visual stimulus were presented. The

monkey was required to maintain fixation in these trials.

Visual stimuli were presented on a gamma-corrected high-end 21 inch color

display (Sony Trinitron GDM-F520) at a fixedmean luminance of 30 cd/m2. The

display subtended 20.5� 3 15.4� at a viewing distance of 108 cm and had

a pixel resolution of 1024 3 768, 30-bit color depth, and a refresh rate of

100 Hz. Behavioral measurements and data acquisition were controlled by

a PC running a software package for neurophysiological recordings from alert

animals (Reflective Computing). Eye movements were measured using an

infrared eye-tracking device (Dr. Bouis Inc.).

Optical Imaging with VSD

Our general experimental procedures for VSDI in behaving monkeys have

been described in detail elsewhere (Chen et al., 2006, 2008a). All procedures

were approved by the University of Texas Institutional Animal Care and Use

Committee and conformed to National Institutes of Health standards. Briefly,

we used oxonol voltage-sensitive dyes (RH 1838 or RH 1691) (Shoham

et al., 1999) to stain the cortical surface and an Imager 3001 system (Optical

Imaging) to image brain activity. Imaging data were collected using resolution

of 5043 504 pixels at 110 Hz and were further binned to 633 63 pixels tomini-

mize the contribution of shot noise, with each final pixel corresponding to

0.25 3 0.25 mm2 cortical area.

Analysis of Imaging Data

We completed 22 VSDI experiments from two hemispheres of monkey 1 and

25VSDI experiments fromonehemisphere ofmonkey2.We selected for further

analysis experiments in which the average amplitude of the peak spatial

response exceeded twice the response standard deviation across trials

(11 and 12 experiments from monkeys 1 and 2, respectively). The remaining

experimentshad lowsignal-to-noise ratios, usually attributable topoor staining.

VSDI analysis followed six steps for each experiment: (1) we removed trials

with aberrant VSDI responses (usually < 1% of total trials). In each trial, we

divided each frame into four quadrants, and average the fluorescence in

each quadrant. A trial was removed if the average fluorescence at any of the

quadrants and frames was out of ± 5 standard deviations across all trials. (2)

We normalized the response at each pixel by the average fluorescence across

all trials and frames. (3)We subtracted the average response in blank trials from

all individual trials. (4) We cropped all frames to an area of 103 8 mm2 with the

responsepeak near the center of the croppedarea. (5)Weestimated the spatial

response maps. In each trial and at each location, we averaged the response

within a 200 ms interval after stimulus onset, and then subtracted the average

response within a 100 ms interval before stimulus onset to obtain a spatial

response map. For each attentional state, we averaged the spatial response

maps across all corresponding trials irrespective of behavioral outcome and

then fitted the average map with a 2D Gaussian function R(x,y) = a*G(x,y) + b,

where G(x,y) was a Gaussian function and a and b were the amplitudes of the

Gaussian and baseline. (6) We estimated the time courses of the Gaussian

and the baseline. Because no significant difference was found in the Gaussian

component across the three attentional states, we defined the spatiotemporal

responses as R(x,y,t) = a(t)*G(x,y) + b(t), where a(t) and b(t) were the time

courses of Gaussian and baseline. We first averaged the spatial response

maps in step 5 across the three attentional states and fitted the average with

a2DGaussian function toobtainG(x,y). Then, for eachattentional state,wepro-

jected G(x,y) and the baseline to each frame to calculate a(t) and b(t). All data

analysis was performed in MATLAB (Mathworks).

SUPPLEMENTAL INFORMATION

Supplemental Information includes six figures and can be found with this

article online at doi:10.1016/j.neuron.2012.03.033.

ACKNOWLEDGMENTS

We than W. Geisler for the suggesting the toy example in Figure 1 and for

helpful discussions. We thank D. Ress, D. Heeger, J. Maunsell, and members

of the Seidemann lab for helpful comments and suggestions and T. Cakic for

technical support. This work was supported by National Eye Institute Grants

EY-016454 and EY-016752. Author contributions: Y.C. and E.S. designed

the experiments, Y.C. carried out the experiments and analyzed the data,

and Y.C. and E.S. wrote the paper.

Accepted: March 15, 2012

Published: May 9, 2012

REFERENCES

Allport, D.A. (1993). Attention and control: have we been asking the wrong

questions? A critical review of twenty-five years. In Attention and

Performance XIV, D.E. Meyer and S. Kornblum, eds. (Cambridge, MA: MIT

Press), pp. 183–218.

Bichot, N.P., Rossi, A.F., and Desimone, R. (2005). Parallel and serial neural

mechanisms for visual search in macaque area V4. Science 308, 529–534.

Boudreau, C.E., Williford, T.H., and Maunsell, J.H. (2006). Effects of task

difficulty and target likelihood in area V4 of macaque monkeys.

J. Neurophysiol. 96, 2377–2387.

Broadbent, D.E. (1958). Perception and Communication (London: Pergamon

Press).

Buracas, G.T., and Boynton, G.M. (2007). The effect of spatial attention on

contrast response functions in human visual cortex. J. Neurosci. 27, 93–97.

Busse, L., Wade, A.R., and Carandini, M. (2009). Representation of concurrent

stimuli by population activity in visual cortex. Neuron 64, 931–942.

Chen, Y., Geisler, W.S., and Seidemann, E. (2006). Optimal decoding of corre-

lated neural population responses in the primate visual cortex. Nat. Neurosci.

9, 1412–1420.

Chen, Y., Geisler, W.S., and Seidemann, E. (2008a). Optimal temporal decod-

ing of neural population responses in a reaction-time visual detection task.


Chen, Y., Martinez-Conde, S., Macknik, S.L., Bereshpolova, Y., Swadlow,

H.A., and Alonso, J.M. (2008b). Task difficultymodulates the activity of specific

neuronal populations in primary visual cortex. Nat. Neurosci. 11, 974–982.

Chen, Y., Palmer, C.R., and Seidemann, E. (2012). The relationship between

voltage-sensitive dye imaging signals and spiking activity of neural popula-

tions in primate V1. J. Neurophysiol., in press. Published online March 14,

2012. 10.1152/jn.00977.2011.

Cohen, M.R., and Maunsell, J.H.R. (2009). Attention improves performance

primarily by reducing interneuronal correlations. Nat. Neurosci. 12, 1594–

1600.

Desimone, R., and Duncan, J. (1995). Neural mechanisms of selective visual

attention. Annu. Rev. Neurosci. 18, 193–222.

Duncan, J. (1980). The locus of interference in the perception of simultaneous

stimuli. Psychol. Rev. 87, 272–300.

Eckstein, M.P., Thomas, J.P., Palmer, J., and Shimozaki, S.S. (2000). A signal

detection model predicts the effects of set size on visual search accuracy for

feature, conjunction, triple conjunction, and disjunction displays. Percept.

Psychophys. 62, 425–451.

Ghose, G.M., and Maunsell, J.H.R. (2002). Attentional modulation in visual

cortex depends on task timing. Nature 419, 616–620.

Haenny, P.E., Maunsell, J.H.R., and Schiller, P.H. (1988). State dependent

activity in monkey visual cortex. II. Retinal and extraretinal factors in V4.

Exp. Brain Res. 69, 245–259.

Ito, M., and Gilbert, C.D. (1999). Attention modulates contextual influences in

the primary visual cortex of alert monkeys. Neuron 22, 593–604.


http://dx.doi.org/doi:10.1016/j.neuron.2012.03.033

Neuron


Kastner, S., Pinsk, M.A., De Weerd, P., Desimone, R., and Ungerleider, L.G.

(1999). Increased activity in human visual cortex during directed attention in

the absence of visual stimulation. Neuron 22, 751–761.

Luck, S.J., Chelazzi, L., Hillyard, S.A., and Desimone, R. (1997). Neural mech-

anisms of spatial selective attention in areas V1, V2, and V4 of macaque visual

cortex. J. Neurophysiol. 77, 24–42.

Marcus, D.S., and Van Essen, D.C. (2002). Scene segmentation and attention

in primate cortical areas V1 and V2. J. Neurophysiol. 88, 2648–2658.

McAdams, C.J., and Maunsell, J.H.R. (1999). Effects of attention on orienta-

tion-tuning functions of single neurons in macaque cortical area V4.

J. Neurosci. 19, 431–441.

Mitchell, J.F., Sundberg, K.A., and Reynolds, J.H. (2009). Spatial attention

decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63,

879–888.

Moran, J., and Desimone, R. (1985). Selective attention gates visual process-

ing in the extrastriate cortex. Science 229, 782–784.

Motter, B.C. (1993). Focal attention produces spatially selective processing in

visual cortical areas V1, V2, and V4 in the presence of competing stimuli.


Muller, M.M., Malinowski, P., Gruber, T., and Hillyard, S.A. (2003). Sustained

division of the attentional spotlight. Nature 424, 309–312.

Murray, S.O. (2008). The effects of spatial attention in early human visual

cortex are stimulus independent. J. Vis. 8, 2.1–2.11.

Palmer, J., Verghese, P., and Pavel, M. (2000). The psychophysics of visual

search. Vision Res. 40, 1227–1268.

Palmer, C.R., Chen, Y., and Seidemann, E. (2012). Uniform spatial spread of

population activity in primate parafoveal V1. J. Neurophysiol. 107, 1857–1867.

Pestilli, F., Carrasco, M., Heeger, D.J., and Gardner, J.L. (2011). Attentional

enhancement via selection and pooling of early sensory responses in human

visual cortex. Neuron 72, 832–846.

Posner, M.I. (1980). Orienting of attention. Q. J. Exp. Psychol. 32, 3–25.

Priebe, N.J., and Ferster, D. (2008). Inhibition, spike threshold, and stimulus

selectivity in primary visual cortex. Neuron 57, 482–497.

Ress, D., Backus, B.T., and Heeger, D.J. (2000). Activity in primary visual

cortex predicts performance in a visual detection task. Nat. Neurosci. 3,

940–945.


Reynolds, J.H., Pasternak, T., and Desimone, R. (2000). Attention increases

sensitivity of V4 neurons. Neuron 26, 703–714.

Roberts, M., Delicato, L.S., Herrero, J., Gieselmann, M.A., and Thiele, A.

(2007). Attention alters spatial integration in macaque V1 in an eccentricity-

dependent manner. Nat. Neurosci. 10, 1483–1491.

Roelfsema, P.R., Lamme, V.A.F., and Spekreijse, H. (1998). Object-based

attention in the primary visual cortex of the macaque monkey. Nature 395,

376–381.

Salzberg, B.M., Davila, H.V., and Cohen, L.B. (1973). Optical recording of

impulses in individual neurones of an invertebrate central nervous system.

Nature 246, 508–509.

Seidemann, E., and Newsome, W.T. (1999). Effect of spatial attention on the

responses of area MT neurons. J. Neurophysiol. 81, 1783–1794.

Seidemann, E., Chen, Y., and Geisler, W.S. (2009). Encoding and decoding

with neural populations in the primate cortex. In The Cognitive

Neurosciences IV, M.S. Gazzaniga, ed. (Cambridge, MA: MIT Press),

pp. 419–434.

Shoham, D., Glaser, D.E., Arieli, A., Kenet, T., Wijnbergen, C., Toledo, Y.,

Hildesheim, R., and Grinvald, A. (1999). Imaging cortical dynamics at high

spatial and temporal resolution with novel blue voltage-sensitive dyes.

Neuron 24, 791–802.

Sirotin, Y.B., and Das, A. (2009). Anticipatory haemodynamic signals in

sensory cortex not predicted by local neuronal activity. Nature 457, 475–479.

Spitzer, H., Desimone, R., and Moran, J. (1988). Increased attention enhances

both behavioral and neuronal performance. Science 240, 338–340.

Thiele, A., Pooresmaeili, A., Delicato, L.S., Herrero, J.L., and Roelfsema, P.R.

(2009). Additive effects of attention and stimulus contrast in primary visual

cortex. Cereb. Cortex 19, 2970–2981.

Treue, S., andMartınez Trujillo, J.C. (1999). Feature-based attention influences

motion processing gain in macaque visual cortex. Nature 399, 575–579.

Treue, S., and Maunsell, J.H.R. (1996). Attentional modulation of visual motion

processing in cortical areas MT and MST. Nature 382, 539–541.

Williford, T., and Maunsell, J.H.R. (2006). Effects of spatial attention on

contrast response functions in macaque area V4. J. Neurophysiol. 96, 40–54.

Yang, Z., Heeger, D.J., and Seidemann, E. (2007). Rapid and precise retino-

topic mapping of the visual cortex obtained by voltage-sensitive dye imaging

in the behaving monkey. J. Neurophysiol. 98, 1002–1014.

Neuron, Volume 74

Supplemental Information

Attentional Modulations Related

to Spatial Gating but Not to Allocation

of Limited Resources in Primate V1

Yuzhi Chen and Eyal Seidemann Supplemental Inventory

- Figure S1 is related to Figure 2; it provides additional summary information regarding behavioral

performance

- Figure S2 is related to Figure 3; it is identical to figure 3 but for a second monkey

- Figure S3 is related to Figure 4; it provides additional breakdown of responses in ‘attend out’

trials based on the position of the cue relative to the receptive field of the imaged neurons

- Figure S4 is related to Figure 6; it provides evidence that the observed modulations in V1 are not

due to direct visual response to the cue

- Figure S5 is related to Figure 6; it provides evidence that the observed modulations in V1 are not

due to variations in eye positions

- Figure S6 is related to Figure 1; it shows that behavioral improvement in focal attention can be

explained without invoking a change in neural sensitivity/fidelity due to limited resources (as in

Fig. 1A,B)

Suppl. Fig. 1

A B C

Suppl. Fig. 2

σmajor

σminor

B

C D E F

A

Suppl. Fig. 3

A B

C D

Suppl. Fig. 4

B

A

Suppl. Fig. 5

B A C

D E F

Suppl. Fig. 6

SUPPLEMENTAL FIGURE LEGENDS Figure S1. Differences in behavioral performance between focal and distributed attention.(A) The

percentage of hits in focal attention trials (single-cue) is significantly higher than in distributed attention

trials (multiple-cues). (B) The percentage of false alarms in focal attention trials (single cue) is not

significantly different than in distributed attention trials (multiple-cues) even though there are four

possible target locations in distributed attention trials and only one possible target location in focal

attention trials. This result suggests that the monkeys are adjusting their criterion between focal and

distributed attention trials. (C) Rate of “wrong target” in distributed attention trials. “Wrong target” is

defined as a target-present distributed attention trial in which the monkey selected a location different

from the target location. N, the number of experiments for each monkey; p, the p-value of a paired t-

test across experiments; error bars, SEM.

Figure S2. Average spatial pattern of V1 population responses under three attentional states in monkey

2. (A) Top row shows a schematic diagram of the three attentional states. The black circle represents the

receptive field (RF) of the neurons at the center of the imaged area, while the white circles represent

the cue. Att-in: the receptive field location is cued. Att-out: the cued location is outside of receptive

field. Att-dstr: all four locations are cued. Bottom two rows show the average response maps in mask

(middle row) and mask-plus-target (bottom row) trials under the attentional state indicated by the

diagram in the top row. At each location the responses are averaged over the first 200 ms after stimulus

onset. The ellipsoidal region at the center of the imaged area shows the area directly activated by the

mask or mask-plus-target. The response is anisotropic due to the anisotropy in the map of visual space

in V1. The response is dominated by the higher contrast mask and is therefore very similar in mask and

mask-plus-target trials. Note that in attend-out state the overall activity level in the area surrounding

the active region is lower than in attend-in and attend-distributed states. (B) Two dimensional (2-D) fit

to the spatial patterns of V1 population response. The responses were fitted with a 2-D Gaussian plus a

uniform baseline. The space constants along the major and minor axis are indicated. Top – 2-D Gaussian

fit to response to mask in attend-in state. Bottom – one dimensional horizontal slice through the two-

dimensional fit, indicated by the black line in the top panel. The baseline, amplitude and space constant

(σ) are indicated. For comparison, the spatial response profile in a 1 mm wide strip along this slice is

indicated by a dashed line. The shaded area indicates ± one SEM across experiments. Right – same as

bottom but along a vertical slice. (C)-(F) Mean and SEM of the fitted parameters across 12 experiments

in monkey 2. The color represents the attentional state as indicated in top row of (A). Asterisks indicate

significant differences (p<0.05 based on paired t-test across experiments). The only significant

differences are between the magnitude of the baseline in attend-out and the other two attentional

states.

Figure S3. Summary of attentional effects in attend-out trials in the two monkeys. Attend out-trials were

divided into trials in which the cue was opposite to the imaged area (light green; the location where the

distracter could appear) and trials in which the cue was in one of the other two locations (dark green). In

both types of attend-out trials, the baseline is not significantly elevated relative to blank trials and the

stimulus-evoked Gaussian response is indistinguishable from all other attentional conditions. The format

is the same as in Fig. 4.

Figure S4. Time courses of the baseline component time locked to cue offset show no evidence for

direct visual responses to the cue during the imaging period and therefore cannot account for the

observed modulations in V1 responses. In each trial, we used the time course of the baseline

component in the first 300 ms of data acquisition (terminating 100 ms before stimulus onset). We

divided the time courses into 100 ms long bins based on the time from cue-offset to fixation point

dimming. We then averaged the time courses of trials in the same attentional states in each bin. The

figure shows data from three bins that contained most of the trials. Dashed vertical line indicates the

average dimming time relative to cue offset. While in some of the bins the results are noisy, there is no

systematic modulation that is time locked to cue offset. (A) Results from monkey 1. (B) Results from

monkey 2. The shaded areas represent ± one SEM.

Figure S5. Fixational eye movements do not differ significantly between attentional states and therefore

cannot account for the observed modulations of V1 responses. (A) Scatter plot of average eye position

in different condition in monkey 1. Each point indicates the average eye position across trials of the

same condition during the 200 ms before stimulus onset. The four color lines represent the directions

towards the corresponding cued positions. To simplify the display, the three experiments collected from

the left hemisphere in monkey 1 were excluded from this analysis, but the results are the same with

those experiments included. (B) Mean and SEM of the average standard deviation of the eyes during the

same 200 ms interval. (C). Mean and SEM of the peak eye velocity during the same 200 ms interval. (D)-

(F) Same as A-C but for monkey 2. All p values are based on a one-way anova. ‘S-cue’ – single cue; ‘M-

cue’ – multiple cues; ‘B’ – blank; ‘Pos’ – position.

Figure S6. Behavioral improvement in focal attention can be explained without invoking a change in

neural sensitivity/fidelity (as in Fig. 1A,B). To assess the expected behavioral improvement in focal

attention under the assumption of no resource limit, we used a simple model based on signal detection

theory (Green and Swets 1966). The underlying assumption in this model is that subjects base their

decision on a noisy sensory representation, with neural responses that are normally distributed (rather

than binary as in our toy example). The mean neural response in target-present trials is higher than in

target-absent trials, and this difference, in units of response standard deviation, controls the neural

sensitivity and therefore the subject’s accuracy. In the distributed attention case, the responses at all

four locations are assumed to be independent and to have the same signal and noise levels. The

simulation was performed as follows. In simulated focal attention trials, the response from the cued

location was compared to an optimal criterion to determine whether the target was present or absent.

Trials were considered correct if the response exceeded the criterion in target-present trial or was lower

than the criterion in target-absent trial. In simulated distributed attention trials, the maximum of the

responses at the four locations was compared to an optimal criterion. Target-present trials were

considered correct if the response at target location was maximal and exceeded the criterion. Target-

absent trials were considered correct if the maximal response was lower than the criterion. The optimal

criterion was selected to maximize accuracy. For each signal-to-noise ratio, we simulated the same

number of trials as in the real experiment and repeated the simulation 1000 times. The figure shows

percent correct for single-cue trials vs. for multiple-cues trials. The black curve represents the simulation

results, while the symbols represent the experimental results for the two monkeys. The cross represents

mean ± one SD for the experimental results. The shaded areas represent ± one SD for the simulation.

The change in accuracy between focal and distributed attention is not significantly different from the

prediction of the model, indicating that it is possible to account for the behavioral results without

invoking a change in sensitivity between focal and distributed attention. A key difference between this

model and our toy example (Fig. 1) is that the criterion is allowed to vary between focal and distributed

attention. A fixed criterion followed by a random selection between all locations that exceed the

criterions (rather than using the location with the maximum response) would make the two models

equivalent. Our behavioral results provide clear evidence that the monkeys changed their criteria

between focal and distributed attention trials (Supp. Fig. 1). To see this, consider the false alarm rate in

the two attentional states. If the criterion was fixed, we would expect to see a large increase in the false

alarm rate in the distributed attention case. Instead, the false alarm rate is similar in the two attentional

states.

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