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The Journal of Neuroscience, March 1, 2002, 22(5):1994-2004
Attentional Modulation of Behavioral Performance and Neuronal
Responses in Middle Temporal and Ventral Intraparietal Areas of Macaque
Monkey
Erik P.
Cook and
John H. R.
Maunsell
Howard Hughes Medical Institute and Division of Neuroscience,
Baylor College of Medicine, Houston, Texas 77030
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ABSTRACT |
Although many studies have demonstrated that neuronal responses are
modulated by attention, the significance of this modulation for
behavior is poorly understood. We recorded from neurons in the middle
temporal (MT) and ventral intraparietal (VIP) areas in the visual
cortex of two macaque monkeys while they performed a motion detection
task under two conditions of spatial attention. The ability of the
animals to detect the motion was reduced when they withdrew attention
from the stimulus. Withdrawing attention also reduced neuronal
responses to the motion in both the MT and VIP areas. To compare the
neuronal and behavioral effects of attention, the amount of attentional
modulation was expressed in units of stimulus strength. On average,
attention modulated neuronal responses in MT less than needed to
account for the attentional effect on behavior. The opposite was
observed in VIP, where the average effect of attention on neuronal
responses was greater than that needed to account for behavior. Similar
results were obtained when the effects of attention on neuronal
response and behavioral performance were compared using a parametric
function of stimulus strength. Across neurons in both areas,
attentional modulation of neuronal responses was more variable than,
and uncorrelated with, attentional modulation of behavioral
performance. These findings suggest that attention can alter the
average relationship between neuronal activity in visual cortex and
behavioral performance. Where this relationship is preserved may
indicate which cortical regions are most closely associated with the
behavior in a given task.
Key words:
attention; macaque monkey; MT; VIP; vision; motion
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INTRODUCTION |
Directing attention to a specific
region in space improves stimulus detection at that region relative to
others (Eriksen and Hoffman, 1972 ; Posner, 1980; Downing, 1988).
Spatial attention also affects the responses of neurons in visual
cortex (Bushnell et al., 1981; Motter, 1993; Desimone and Duncan, 1995;
Luck et al., 1997). How the behavioral and neuronal effects of
attention are related is poorly understood and was the focus of our experiments.
One possibility is that the effect of attention on the responses of
visual cortical neurons can fully account for its effect on behavioral
performance. This hypothesis arises from several observations. First,
the neuronal modulation that occurs when attention is directed to a
stimulus (Spitzer et al., 1988) or when effort is increased (Spitzer
and Richmond, 1991) is typically an enhancement, which is consistent
with behavioral improvements. Second, a rough correspondence exists
between behavioral performance and the ability of individual neurons to
discriminate among or detect stimuli (Parker and Hawken, 1985; Barlow
et al., 1987; Britten et al., 1992; Geisler and Albrecht, 1997; Prince
et al., 2000). Because these studies are likely to have spanned a range of attentional states, it is possible that the relationship between neuronal activity and behavioral performance persists across different attentional conditions. Third, the correspondence between neuronal activity and behavioral performance persists during improvements in
performance that occur with practice (Zohary et al., 1994).
If attention alters behavior without affecting the link between
neuronal activity and behavioral performance, then attention must act
in a manner similar to varying stimulus strength. That is, behavioral
performance should follow changes in neuronal responses, whether those
changes arise from stimulus differences or changes in behavioral state.
This idea is supported by recent studies which show that attention
alters neuronal responses in a multiplicative manner without changing
stimulus selectivity (McAdams and Maunsell, 1999; Treue and Martinez
Trujillo, 1999). Multiplicative scaling is similar to the way changes
in stimulus strength affect neuronal responses (Tolhurst, 1973; Sclar
and Freeman, 1982). Directing attention to a stimulus may therefore
have the effect of multiplicatively enhancing specific representations
in sensory cortex, thereby improving detection.
We wanted to know whether the attentional enhancement of neuronal
activity and behavioral performance is equivalent to the enhancement
expected from increasing stimulus strength. Were this the case, it
would support the idea that the effect of attention on neuronal
responses in sensory cortex accounts for the attentional modulation of
behavioral performance. We designed an experiment in which we could
simultaneously measure behavioral performance and responses of
individual neurons while spatial attention and stimulus strength were
varied. We used a motion detection task and recorded in the middle
temporal (MT) and ventral intraparietal (VIP) areas, two regions of the
visual cortex involved in motion processing. We found that the average
effect of attention on neuronal responses in MT was usually less than
needed to account for changes in behavioral performance. In contrast,
the average effect of attention on the VIP area responses was much
greater than that needed to account for changes in behavioral performance.
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MATERIALS AND METHODS |
Behavioral tasks. Data were collected from two male
rhesus monkeys (Macaca mulatta) while they performed a
spatially cued motion detection task (see Fig. 1A).
Each monkey sat in a primate chair during training and recording
sessions, which lasted 2-5 hr. While the animal pressed a lever and
fixated on a central point, two patches of dynamic random dots were
presented on opposite sides of the fixation point. Both patches started
with no net motion (0% coherent), and the animal's task was to
release the lever within 750 msec after either patch began moving in a
coherent manner. Coherent motion started at a random time between 500 and 8000 msec. Having at least 500 msec of 0% random motion before the
coherent motion occurred minimized any effect the static cue may have
had on neuronal responses. The coherent motion onset was exponentially
distributed with a mean of 1300 msec. However, for the first 21 MT
cells recorded in monkey 1, the coherent motion times were uniformly
distributed, which resulted in a slight increase (4%) in the number of
correct responses for coherent motion occurring toward the end of trials.
The diameter and location of one patch of dots was adjusted to fill the
receptive field (RF) of the neuron under study, and the coherent motion
in either patch was in the preferred direction and speed of the neuron.
The coherent motion was present until the monkey released the lever or
the end of the 750 msec reaction time window was reached. The strength
of the coherent motion signal varied randomly from trial to trial among
preset values to produce a range of behavioral performances.
Spatial attention was controlled by presenting a cue of static dots in
one patch at the beginning of each trial. This cue indicated which
patch would contain the coherent motion signal. A key element of this
task was that the cue was valid 80% of the time (valid trials).
In 20% of the trials, the coherent motion signal occurred in the
uncued patch (invalid trials). The idea was that the animal would
devote most of its attention to the cued patch of dots, because this
patch was most likely to contain the coherent motion signal. This
paradigm of valid/invalid cueing has been used successfully to measure
the behavioral effects of spatial attention in humans (Posner,
1980).
The monkey received a reward for releasing the lever between 200 and
750 msec after the start of the coherent motion signal (correct trial).
Failure to release the lever or late releases was not rewarded (missed
trial). Both correct and missed trials were scored as completed trials.
Trials in which the monkey released the lever early either during the
0% coherent motion or <200 msec into the coherent motion (false
alarm) or did not maintain fixation within 1° of the fixation point
(fixation break) were not counted as complete or analyzed.
Experiments were run in a block mode in which the cue was presented at
the same location for 15 completed trials (either correct or missed).
Valid and invalid cues were balanced between the two patch locations.
Thus, for each block the monkey had 12 valid trials in which the
coherent motion occurred in the cued patch and 3 invalid trials in
which the coherent motion occurred in the uncued patch. Trials in which
the cue and the coherent motion were both in the RF of the neuron will
be referred to as "attend in" because the monkey was directing its
attention toward the RF. Trials in which the cue was outside the RF and
the coherent motion occurred inside the RF will be referred to as
"attend out" because the monkey's attention was directed away from
the RF. Only trials in which the coherent motion occurred in the patch centered on the RF were used in this analysis.
Behavioral performance was measured as the proportion of correct
trials. Four levels of motion coherence were usually measured, including three levels of motion coherence (low, medium, and high) and
0% (catch trials). The values of non-zero motion coherence were
adjusted for each stimulus configuration to produce a range of
behavioral performances for the animal. The average behavioral performance was 50, 92, and 99% correct for low, medium (validly cued), and high coherence trials. No reward was given during the 0%
catch trials.
The monkeys were also trained to perform a standard memory delayed
saccade task (White and Sparks, 1986). In this task, the monkey fixated
on a central point while a peripheral target (0.25° diameter)
appeared for 500 msec. To get a reward, the monkey had to remember the
target location for 500-2500 msec and then, after the central fixation
point was extinguished, saccade to within 2.5° of its location within
300 msec. Neuronal responses were analyzed only from correctly
completed trials.
Random dot stimulus. The animals sat 62 cm from a computer
monitor (±17 × ±13° of visual angle, 1600 × 1200 pixels, 75 Hz refresh). The stimuli consisted of two patches of white
dots (each 0.25° diameter, 78 cd/m2) on
a dark gray background (12 cd/m2) with a
dot density of 2.1 dots/degree2. Each patch
of dots was updated every other frame (37.5 Hz) using the following
procedure. The dots were evenly divided into two groups. On each
update, one group was replaced with new, randomly positioned dots,
while dots in the other group were displaced by a fixed
distance. The dots in this latter group determined the motion
coherence. For 0% coherence, all of the dots in this group moved a
fixed distance in a random direction. For coherent motion greater than
zero, a proportion of the dots moved with a fixed distance in the same
direction. This proportion determined the strength of the coherent
motion. On the next update, the groups were switched. This arrangement
ensured that all the dots had a lifetime of two updates (26.6 msec)
before they were replaced and that there would be no changes in the
apparent dot density associated with the onset of coherent motion.
Because half of the dots are always randomly replotted regardless of
the proportion of dots moving coherently, our motion had a maximum
strength of 50% coherence. For example, at 25% coherent motion, half
of the dots are randomly replotted, one-fourth are moving with the same fixed distance and direction, and one-fourth are moving with the same
fixed distance in a random direction.
Neuronal recording and data collection. Using standard
extracellular recording techniques (Gibson and Maunsell, 1997), we recorded from single neurons in MT and VIP areas in both animals. When
a neuron was isolated, the receptive field was mapped using a manually
controlled bar while the animal fixated on a central spot. The diameter
of the receptive fields ranged from 3.9 to 10.7° (median 7.4°) for
the MT area and 5 to 10.6° (median 8.2°) for the VIP area.
Receptive field center eccentricities ranged from 3.9 to 11.1°
(median 7.9°) for the MT area and 3.9 to 11.0° (median 8.1°) for
the VIP area. The preferred speed was also judged using a bar moved by
hand. The directional tuning of the neuron was determined using the
motion detection task described above with 50% coherent motion
presented in eight directions. For most cells, once the receptive field
location, size, preferred direction, and speed were determined, the
memory saccade task was run with the targets at the centers of where
the random dot patches would be located. Five to 30 (median 12)
correctly completed trials were collected for this task. The motion
detection task was then run, and we recorded from the neuron as long as
possible. The number of completed trials per coherence level for the
motion detection task ranged from 15 to 175 (median 35). The monkey's performance varied with patch location, size, and motion speed, which
were determined by the response properties of the neuron under study.
Consequently, different neurons were tested with different coherence
levels. The animal's eye position was measured every 5 msec using a
scleral search coil (Robinson, 1963; Judge et al., 1980), and the
occurrence of action potentials was recorded to the nearest millisecond.
Analysis. Standard statistical methods were used for most
analyses. The exception was the analysis in Figure 5, in which a bootstrap procedure (Efron and Tibshirani, 1993) was used to determine whether the neuronal and behavioral effects of attention observed in a
single neuron were significantly different. The bootstrap procedure has
the advantage of requiring no assumptions regarding the distribution of
the null hypothesis. For this analysis, trials from each neuron were
randomly resampled with replacement to form new bootstrap
samples. This was repeated to produce 1000 total bootstrap samples in
which each bootstrap sample had the same number of trials as the
original data set. For each bootstrap sample, sigmoidal and linear fits
were performed as a function of motion strength on the behavioral and
neuronal data, respectively. The difference between the neuronal and
behavioral effects of attention was computed from these fits, forming a
distribution of differences. If the 95% confidence interval of this
distribution of attentional differences contained zero, then it was
concluded that there was no difference between the neuronal and
behavioral effects of attention (see Fig. 5, open symbols).
Otherwise, it was concluded that attention had a significantly
different effect on the behavior and neuronal response
(p < 0.05) (see Fig. 5, filled
symbols). For the marginal distributions in Figure 5, the effects
of attention on the neuronal response and behavioral performance were
assessed separately using the same bootstrap procedure. However, in
this case, distributions were computed separately for neuronal and
behavioral data and tested if the 95% confidence interval for the mean
contained zero.
Sigmoidal curves were fit to behavioral performance using a nonlinear
fitting function in MATLAB (The Mathworks). Because we fit four data
points with sigmoids containing two free parameters, the fits where
predictably very good. For all experiments, the minimum correlation
coefficient between the measured behavior and fitted sigmoid was 0.97 (median 0.99). The use of a sigmoid to describe behavioral performance
as a function of motion coherence was based on days when we measured
the animal's behavioral performance with more than four levels of
coherent motion (data not shown). Neuronal responses as a function
motion coherence were described using a linear function. We chose this
because it has been shown that MT responses increase
linearly with motion coherence (Britten et al., 1993). Linear fits of
neuronal responses were evaluated using standard regression analysis (F
test for slope = 0; p < 0.05).
Histology. A vertical approach was used with recording
chambers implanted dorsal to the superior temporal and intraparietal sulci. A histological reconstruction of recording sites was made only
for monkey 1 (monkey 2 is to be used in further experiments). For
monkey 1, electrolytic lesions (10 µA for 10 sec) were made at a few
recording sites in the MT and VIP areas a few days before the end of
recording. The extent of the MT area was mapped using myelin-stained
sections (Van Essen et al., 1981). Of 56 neurons recorded in the
superior temporal sulcus in monkey 1, 9 were not unequivocally within
the MT area and were excluded from analysis. Sections of the
intraparietal cortex revealed that we recorded from neurons located in
the ventral portion of the lateral bank. Recordings from the lateral
bank of the intraparietal sulcus were within 3 mm of the fundus, which
has been identified as VIP (Colby et al., 1993). The Horsely-Clark
coordinates of the MT area recordings ranged from 4 to 7 mm posterior
and 15 to 18 mm lateral. The VIP coordinates ranged from 1 mm posterior
to 2 mm anterior and from 10 to 14 mm lateral.
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RESULTS |
We recorded from 93 MT cells and 104 VIP cells in two monkeys
performing a motion detection task. Of these, 11 MT and 15 VIP neurons
were excluded from the analysis on the basis of their lack of
responsiveness to the coherent motion (see below).
Directional selectivity in MT and VIP
The MT area projects to several parts of the parietal cortex
including the VIP area (Maunsell and Van Essen, 1983), which is
a later stage of processing in the parietal stream (Ungerleider and
Mishkin, 1982). Both areas contain many neurons that are strongly directionally selective (Van Essen et al., 1981; Colby et al., 1993).
The directional selectivity of the neurons that we recorded is shown in
Figure 2, A and B. Data from the two monkeys were combined because directional selectivity was similar for both animals.
The average directional tuning curve in the left panels was based on
responses to 50% motion coherence presented using the motion detection
task. Directional selectivity was measured only while the animals were
attending to the stimulus in the RF of the neuron.
Individual tuning curves were rotated to bring the preferred direction
of each neuron to the top, and responses to different directions were
then averaged. The mean firing rates for the 0% and 50% coherent
motion were computed using two 300 msec periods just before and after
the onset of the coherent motion (Fig.
1B). We chose these
intervals on the basis of the monkeys' reaction times, which were
>300 msec 99% of the time. The spontaneous firing rate was computed
using the 250 msec period just before the 0% coherent motion
began.
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Figure 1.
A, The motion detection task. Each
trial began with the presentation of the fixation point and static cue
indicating which patch of dots would most likely contain the coherent
motion. After fixation and pressing a lever, both patches of dots began
moving randomly with 0% coherent motion. At a random time, one patch
began moving coherently, and the animal had a reaction time window of
200-750 msec to release the lever to obtain a reward. The strength of
the coherent motion was varied from trial to trial to produce a range
of behavioral performances. Illustrated here is an example of a validly
cued trial in which the coherent motion occurred in the cued location.
In 20% of trials, the cue was invalid and the coherent motion occurred
in the uncued location. All cueing was balanced between the two
locations. B, Schematic of neuronal responses during the
motion detection task. Neuronal responses were quantified using the
absolute response to the 0% coherent motion (a),
the absolute response to the coherent motion (b),
and the driven response (c), which was the
difference between the response to the coherent and 0% motion.
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MT neurons were more directionally selective than VIP neurons, and most
were inhibited relative to the 0% coherent response by motion in the
null direction. The right panels in Figure
2 show the distributions of directional
selectivity expressed as a directionality index (DI). This index is the
normalized vector sum of the firing rates for different directions. DI
was calculated by first normalizing the average firing rates for each
motion direction by the sum of all the average firing rates, referred to as Nd, where d
represents one of eight directions. If all
Nd are summed as vectors, with each
vector pointing in the direction of motion, the result is a vector with
a magnitude that is DI. For a cell that has no directional tuning, in
which all directions of motion produced the same response, DI = 0. For a cell that only responded to motion in the preferred direction,
DI = 1. The mean DI (Fig. 2, dashed lines) for
MT neurons was slightly greater than that for VIP neurons, but both
areas contained a large proportion of cells that could contribute to
motion detection.
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Figure 2.
Directional selectivity of MT and VIP neurons.
A, On the left is a directional tuning
plot of the average response of MT neurons to 50% coherent motion in
eight different directions. Responses from each neuron were aligned
with the preferred direction pointing up. The
solid circle is the spontaneous rate in response to the
static cue. The dashed circle is the average response to
the 0% coherent motion that preceded the 50% coherent motion. Error
bars indicate SEM. To the right is the distribution of
the directionality index (DI), which is the normalized vector sum of
the response for each direction (see Results). The dashed
line is the mean of the distribution. B,
Directional tuning and DI for VIP area.
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Attentional modulation in MT and VIP
Figure 3 shows the responses of a
typical MT neuron recorded while the monkey performed the motion
detection task. The proportion of trials in which the monkey released
the lever in response to the coherent motion signal is shown in Figure
3A for the two attentional states. The filled
points in Figure 3A correspond to trials in which the
monkey directed his attention to the patch of dots in the RF
(Attend in). When the strength of the motion signal was strong (30%), the monkey correctly detected the coherent motion on
almost every trial. As the strength of the coherent motion was reduced,
the monkey's ability to detect the motion signal decreased. At 0%
coherent motion there was no signal, and a behavioral performance
greater than zero indicates a false alarm or guessing rate by the
animal. For both animals the false alarm rate averaged <5%,
indicating that they used a conservative criterion for detecting coherent motion.
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Figure 3.
A representative MT cell. A,
Behavioral performance as a function of motion coherence in the
attended (filled circles) and unattended
conditions (open square). Only trials in which the
coherent motion occurred in the RF of the neuron were included. The
unattended trials only contained the 22.5% coherent motion. The
solid curve is the fit sigmoidal function to the
attended performance. The dotted lines and
arrow indicate the equivalent motion coherence (13.5%)
in the attended condition that produces the same behavioral performance
as the unattended condition. B, The time course of the
response of the MT neuron to the three levels of coherent motion above
0%. Responses are aligned to the onset of the coherent motion
(vertical line), and the coherence level is indicated
above each response. The response in the unattended condition
(open histogram) is shown below the valid cueing
condition. C, Average driven response for the first 300 msec of coherent motion. Driven rates were computed using the
difference between the two 300 msec periods before and after the onset
of the coherent motion signal. The solid line is the
best linear fit. The dotted lines and
arrow indicate the equivalent motion coherence (12.5%)
in the attended condition that produced the same neuronal response as
the unattended condition. Error bars indicate SEM.
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To assess the behavioral effect of attention, we measured the
behavioral performance on trials when the animal was attending to the
patch of dots outside the RF (Fig. 3A, Attend
out). In these trials, the cue was presented outside the RF of the
neuron, whereas the coherent motion occurred in the RF. Thus, these
trials had invalid cues. Only the medium strength coherent motion was presented during invalidly cued trials (which for this cell was 22.5%). The effect of withdrawing attention on behavioral performance is shown in Figure 3A (open square) by the poorer
performance when the monkey directed its attention away from the patch
of dots containing the coherent motion.
We did not sample invalidly cued trials at other motion coherence
levels because of the limited time available for electrophysiological recordings of single neurons. Because the invalid cues occurred on only
20% of the trials, adding another invalid cueing level would double
the number of trials required for a complete data set. We measured
behavioral performance using invalid cues with several motion coherence
levels on days when we did not record neuronal data. The resulting
psychophysical curves for the detection of the motion when the animal
was not attending was always consistent with a rightward shifted
version of the performance curve for when the animal was attending
(data not shown).
To compare the behavioral and neuronal effects of attention, we
expressed both in units of stimulus strength (percentage motion coherence). A sigmoid was fit to the behavioral performance in Figure
3A (see Materials and Methods). Using the sigmoidal fit, the
effect of withdrawing attention in units of motion coherence is shown
by the dotted lines. For the invalid cueing condition at
22.5% motion coherence, withdrawing attention reduced the monkeys behavioral performance from 0.6 to 0.3. This was equivalent to the
behavioral performance at the 13.5% motion coherence level in the
attended condition. Thus the behavioral effect of withdrawing attention
in units of motion coherence is 13.5-22.5% =  9% and represented a
significant change in behavior (bootstrap; p = 0.0; see
Materials and Methods).
The time course of the response of the MT neuron is shown in Figure
3B aligned to the onset of the coherent motion stimulus (vertical line). The response to the coherent motion was
greater for stronger motion signals. The effect of attention on the
neuronal response can be seen by comparing the histograms for the two
attentional conditions at 22.5% coherent motion stimulus. The response
of the neuron was reduced on trials when the monkey was attending to
the patch of dots outside the RF (Attend out). We used the increment in driven rate (Fig. 1B, c) to
quantify the strength of the neuronal response. This was computed as
the mean firing rate produced by the coherent motion signal minus the
mean firing rate produced by the 0% coherent motion using the two 300 msec periods just before and after the onset of the coherent motion. The mean driven response above the 0% background for this MT neuron is
plotted in Figure 3C. The driven rates of firing were
typically small, but this is not surprising because the motion signals
were set to be close to detection threshold.
We described the response of each neuron as a function of motion
coherence using a linear relationship (see Materials and Methods). Of
93 MT cells, 11 were not well described by a linear relationship and
were excluded from further analysis. Using the linear fits, we computed
the effect of withdrawing attention in units of motion coherence, which
is shown by the dotted line in Figure 3C. For
this neuron, withdrawing attention was equivalent to changing motion
coherence in the valid cueing condition by the amount of 12.5-2.5% = 10%. This change, however, was not significant (bootstrap;
p = 0.09).
In this example, withdrawing attention reduced both behavior and
neuronal activity by similar amounts. This was not true for all cells
in either MT or VIP. A different effect of attention is illustrated for
an example VIP neuron in Figure 4. The
monkey's behavioral performance during the motion detection task is
illustrated in Figure 4A. In this case, the reduction
in behavioral performance that occurred while the monkey directed
attention away from the stimulus in the RF was equivalent to 9% in
units of motion coherence (bootstrap; p = 0.0). The
neuronal responses to the low, medium, and high coherent motion levels
are shown in Figure 4, B and C. This cell, like
many VIP neurons, was strongly suppressed while the monkey directed
attention away from the stimulus in the RF. In Figure 4C,
the mean driven neuronal response above the 0% coherent background is
shown. The responses of VIP neurons were also reasonably linear as a
function of motion strength. Of the 104 VIP neurons, 15 were excluded
from analysis because of poor linear fits as a function of coherent
motion (see Materials and Methods). For this cell, withdrawing
attention from the RF during the invalid cueing trials was equivalent
to a 16.5% change in units of motion coherence (bootstrap;
p = 0.0). Thus withdrawing attention had a larger
impact on the neuronal response than on the behavioral performance.
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Figure 4.
A representative VIP neuron. Same format as Figure
3. A, Behavioral performance as a function of coherent
motion. B, Time course of neuronal response.
C, Average driven rate above 0% coherent motion as a function
of motion coherence. Error bars represent SEM.
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Attention had different overall effects on the population of MT and VIP
neurons. Figure 5, A and
B, illustrates the behavioral versus neuronal effects of
attention in MT expressed in units of motion coherence for each animal.
Each symbol corresponds to a single neuron, and the filled
symbols represent neurons for which the behavioral and neuronal
effects of attention were statistically different
(p < 0.05), as determined using a bootstrap
procedure (see Materials and Methods). Withdrawing attention produced
statistically different changes in the behavioral and neuronal activity
in 38% of MT neurons (38% in monkey 1 and 37% in monkey 2). For both animals, the average attentional modulation of the neuronal response in
MT neurons was smaller than the average attentional modulation of
behavior (p < 0.001, monkey 1;
p = 0.05, monkey 2; paired t test). The
gray stars in Figure 5 show the mean attentional
modulations.
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Figure 5.
Comparing behavioral and neuronal attentional
modulation in units of coherent motion for MT and VIP neurons.
A, B, Attentional modulation expressed in
units of equivalent motion coherence for MT neurons for the two
subjects. The dashed line has unity slope. Filled
symbols are neurons that exhibited attentional modulation that
was significantly different from behavioral modulation. Open
symbols are neurons with attentional modulation that was not
significantly different from behavior. Gray stars are
the mean attentional modulation for both neuronal and behavioral
performance. Histograms are the marginal distributions
for the neuronal and behavioral effects of attention. Significant
effects of attention are indicated by the hatched bars.
B, D, Data from VIP neurons.
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On average, VIP neurons had large attentional modulation. Figure 5,
C and D, shows the behavioral and neuronal
effects of attention in units of motion coherence for VIP. The effects
of attention on neuronal responses and behavioral performance were statistically different in 55% of VIP neurons (52% in monkey 1 and
60% in monkey 2; p < 0.05; bootstrap). Unlike MT
neurons, however, the modulation in VIP was usually greater than
the modulation of the behavior. The average attentional modulation
(gray stars) is significantly above the unity-slope
lines for both monkeys (paired t test; p < 0.001). Thus, on average, neurons in the VIP area were more affected by
attention than would be predicted on the basis of behavioral
modulation. In MT, neurons with attentional modulation that was
significantly different from the behavioral modulation (Fig. 5,
filled symbols) were almost exclusively to the right of the
unity-slope lines. In the VIP area, the opposite is true, with most
significant points falling to the left of the unity-slope lines.
Figure 5 also includes the marginal distributions of the neuronal and
behavioral effects of attention expressed in units of equivalent motion
coherence. The hatched bars indicate statistically significant effects of attention (bootstrap; p < 0.05). The mean for every distribution (indicated by the gray
stars) is significantly <0% (t test;
p < 0.05), except for the neuronal distribution for monkey 1 (p = 0.09).
Figure 5 shows that the neuronal modulation by attention was highly
variable within both MT and VIP neurons. The behavioral effects of
attention, however, are much less variable, indicating that the monkeys
may have been in two relatively constant attentional states depending
on the location of the cue. Large variability of neuronal modulation is
commonly observed in studies of attention. The reason for this
variability is not known. Figure 5 suggests, however, that the
variability in neuronal modulation is probably not caused by
variability in the monkeys' attentional state. These plots also show
that there is little correlation between the effect of attention on the
behavior and neuronal responses across recording sessions (in Fig. 5,
correlation coefficients are 0.19 and 0.04 for MT neurons and 0.04 and
0.16 for VIP neurons for monkeys 1 and 2, respectively).
The filled and open symbols of Figure 5 represent
two groups of neurons, those that approximated the behavioral effects
of attention and those that did not. We examined whether these two groups differed in other ways. We found no difference in the
directional selectivity (DI) of the two groups for either MT
(two-sample t test; p = 0.16) or VIP
(p = 0.52) neurons. However, neurons that were
modulated by attention differently than the behavioral modulation (filled symbols) had slightly greater average
responses to the coherent motion compared with other neurons
(open symbols), but this difference was not statistically
significant. For MT neurons, the average driven rates to the high
coherent motion were 16.0 and 20.2 spikes/sec for neurons with the same
and different effects of attention with respect to the behavior
(two-sample t test, p = 0.08). In VIP
neurons, the average driven rates were 13.9 and 18.8 spikes/sec for the
same and different effects of attention with respect to the behavior
(two-sample t test; p = 0.07). The reason
for this difference in average firing rate for both MT and VIP neurons
is most likely caused by the increased noise at lower firing rates.
With more variability at lower response rates, the less likely a
significant difference between neuronal and behavioral effects of
attention will be observed.
A limitation in the analysis of Figure 5 arises if attention affects
the slope of either behavioral performance (Figs. 3A, 4A) or neuronal response (Figs. 3C,
4C). If this were the case, then the amount of attentional
modulation computed would depend on the strength of motion coherence
used for the unattended condition. Figure 5 also depends on accurate
sigmoidal and linear fits to the behavioral and neuronal responses for
each experiment. An alternative way of comparing the average effects of
attention on behavior and neuronal activity that avoids these
limitations is shown in Figure 6. These
plots show the mean behavioral performance and mean neuronal response
plotted as a parametric function of stimulus strength for MT and VIP
neurons in both animals. For this analysis, performance and neuronal
data were averaged across cells at each level of motion coherence (0%,
low, medium, and high) while the monkeys were attending to the stimulus
in the RF. A sigmoidal curve was assumed to describe how the average behavioral performance and neuronal response covaried as a function of
stimulus strength and was fit to the data points. If attention exerted
its influence in a manner that was similar to varying stimulus
strength, then the data point corresponding to the unattended condition
would fall on this line.
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Figure 6.
Average behavioral performance and neuronal
response as a parametric function of coherent motion strength for MT
and VIP neurons. A, B, Behavioral
performance plotted against average driven rate above 0% coherent for
each level of motion coherence in MT neurons. Filled
circles are the attended condition. Open square
is the unattended condition. The coherent motion strength is labeled
for each point in A. The solid curve is
the best fit sigmoidal function. C, D,
Data from VIP neurons. Error bars indicate SEM and are smaller than the
symbols where absent.
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Figure 6 confirms the primary observations in Figure 5. For MT neurons
in both monkeys, the unattended point lies to the right of the
sigmoidal line, indicating that the amount of attentional modulation
was less than expected on the basis of the modulation of behavioral
performance (Fig. 6A,B). The effect
of attention was different, however, between the two monkeys. For
monkey 2, the invalid point lies much closer to the sigmoidal line.
Figure 6, C and D, summarizes the effects for VIP
neurons. As with MT neurons, the unattended point for VIP neurons did
not fall on the curve that describes the relationship between neuronal
activity and behavioral performance in the attended condition. On
average, withdrawing attention was not similar to reducing stimulus
strength in VIP. Figure 6 highlights the main difference between these two brain regions. In VIP, the average attentional modulation of the
neuronal signal was too large to account for the behavioral modulations, whereas in MT it was too small.
So far we have assumed that the driven rate in the neuronal response
above the 0% coherent motion (Fig. 1B, c)
corresponds to the animals' detection of the motion stimulus. Although
we believe that this is the most likely way the animals used the neuronal responses, it is possible that the absolute response (Fig.
1B, b) could have been used instead. We
repeated the analysis for Figure 6 using absolute firing rates, and the
results are shown in Figure 7. Use of
absolute rates increased the attentional modulation of the neuronal
response. This increase occurred because attention also affected the
neuronal response to the 0% coherent motion. The results using
absolute rate in Figure 7 are similar to using driven rate in Figure 6.
The exception is for MT neurons in monkey 2 in which the unattended
point now falls on the attended curve, suggesting that attention
affected both absolute neuronal response and behavioral performance as
a change in stimulus strength.
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Figure 7.
Behavioral performance and average neuronal
response as a parametric function of coherent motion strength for MT
and VIP neurons using absolute firing rates to the coherent motion.
Same format as Figure 6.
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Figure 8 compares the effect of attention
on the neuronal responses. We computed the distribution of attentional
modulation using the index of (Rin Rout)/(Rin + Rout), where
Rin is the response while the animals
attended to the stimulus and Rout is the response while the animals attended away from the stimulus. The
corresponding ratio
(Rin/Rout)
is labeled on the top x-axis. The median attentional
modulation corresponded to a 12 and 24% enhancement in MT neurons and
a 195 and 389% enhancement in VIP neurons for monkeys 1 and 2, respectively. Thus monkey 2 had greater attentional modulation of
neuronal responses in both areas compared with monkey 1.
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Figure 8.
Distribution of neuronal attentional modulation
for MT and VIP neurons. A, B, Attentional
modulation for MT neurons expressed as an attentional index. The top of
the x-axis is the equivalent enhancement in terms of an
attentional ratio. The arrows indicate the mean of the
distributions. C, D, Data for VIP
neurons.
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In Figure 8 we calculated the amount of attentional modulation using
the driven neuronal responses relative to the 0% coherent motion. For
some VIP neurons, withdrawing attention produced responses that dropped
below the 0% coherent motion firing rate. For these neurons, the
response to the invalid cue was considered to be zero, producing an
attentional index equal to 1. When absolute firing rates were used to
compute the amount of attentional modulation (Fig.
1B, b), monkeys 1 and 2 had median
attentional enhancements of 12 and 28% for MT and 68 and 94% for VIP.
This places these two monkeys at the low and high end of what has been
observed for MT spatially directed attentional modulation
(Seidemann and Newsome, 1999; Treue and Maunsell, 1999). In these
studies, the animals were trained to entirely ignore the unattended
stimuli. In our task, however, the monkeys directed some attention to
the uncued stimuli, as indicated by their occasional responses to the
invalidly cued patch of dots.
It is possible that on unattended trials, the animals may have shifted
their attention to the coherent motion immediately after the motion
began. This would confound measurements of attentional modulation. To
address this, we also calculated the attentional modulation for the
absolute firing rate of the 300 msec of 0% coherent motion that
preceded the onset of the coherent motion signal (Fig.
1B, a). In this case, the median
attentional modulation in monkeys 1 and 2 was 10 and 24% for MT and 40 and 35% for VIP neurons.
Thus for VIP neurons, the attentional enhancement of neuronal responses
went up appreciably after the coherent motion signal began. This is
surprising because shifting attention toward the coherent motion in the
uncued patch would reduce the attentional enhancement relative to that
immediately before the motion began. To see how attentional modulation
evolved during the trials, we plotted the average neuronal response for
the medium coherent motion combining neurons from both monkeys. Figure
9A shows the mean firing rates
for both attentional states where all trials aligned to the
onset of the coherent motion. Only the first 500 msec of the response
to the coherent motion is shown because as the animals responded, fewer
trials contributed to the average. The ratio of the attentional
modulation is shown in Figure 9B. The effect of attention is
relatively constant for MT neurons. For VIP neurons, however,
attentional modulation increases when the coherent motion begins. The
reason for this increase is unknown.
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Figure 9.
Time course of the effect of attention on neuronal
responses in MT and VIP neurons. A, Average neuronal
response from both monkeys to the medium coherent motion for the
attended (solid line) and unattended (dashed
line) conditions. Trials are aligned on the onset of the medium
coherent motion (vertical line) and smoothed with a 15 msec Gaussian filter. B, Ratio of the attended and
unattended responses shown above for MT (solid line) and
VIP (dashed line) neurons.
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Even when the animals were not attending to the patch containing the
coherent motion, they still were able to respond to the coherent motion
signal on ~50% of trials. One possibility is that the amount of
attention directed at the stimulus may have been different between
correct and missed trials. We examined this by plotting the average
time course of the neuronal response in MT and VIP neurons (aligned to
the onset of the coherent motion) for correct and missed trials
separately (Fig. 10) (note expanded scales). During the 0% coherent motion, the neuronal responses corresponding to correct and missed trials in the unattended condition were almost identical (thin and thick dashed
lines). After the coherent motion began, the unattended responses
were greater for correct compared with missed trials in both MT and VIP
neurons. For the attended condition, only the responses for correct
trials are shown (solid lines) because there were not enough
missed trials to estimate the average response. These results suggest
that the animals maintained a relatively constant level of attention
and effort before the coherent motion began. Once the coherent motion started, the animals may have quickly reoriented their attention during
detection of the coherent motion in the unattended patch. If so, this
reorientation may have been too weak or slow on missed trials to allow
a correct behavioral response, producing the corresponding weaker
neuronal response (thin vs thick dashed
lines).
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Figure 10.
Time course of average neuronal responses for
correct and missed trials. Average neuronal response from both monkeys
to the medium coherent motion for the attended correct trials
(solid line), unattended correct trials (heavy
dashed line), and unattended missed trials (thin dashed
line). Trials are aligned on the onset of the medium coherent
motion (vertical line) and smoothed with a 15 msec
Gaussian filter.
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Memory delayed saccade activity in MT and VIP neurons
The lateral intraparietal (LIP) area has been implicated in coding
intended eye movements [Andersen et al. (1997); but see Colby and
Goldberg (1999)]. Although neither MT or VIP (Colby et al., 1993)
neurons are thought to be involved in this computation, if such a
signal were present in our motion detection task, it could appear as
pronounced attentional modulation. Because the VIP area is immediately
ventral to the LIP area in the intraparietal sulcus, we were also
concerned that we might have inadvertently sampled neurons that encoded
intended eye movements. Although the existence of robust directional
selectivity (Fig. 2) argued that our recordings were from the VIP area,
for most cells we included a memory delayed saccade task to measure the
effects of intended saccades. This task was done for cells in both the VIP and MT areas.
In this task the monkey had to perform a saccade to a remembered target
location. There were two possible target locations corresponding to the
center of the two patches of dots. Figure 11 shows the average response for all
MT and VIP neurons tested using the memory saccade task. Responses were
aligned to when the target was extinguished (at 500 msec). Although
neurons in both areas gave a transient response to the appearance and
disappearance of the target, there was no appreciable memory delay
activity observed for either MT or VIP neurons. Only 5% of MT neurons
and 19% of VIP neurons showed a significant difference in firing
during the memory delayed period (two-sample t test;
p < 0.05). Thus it is unlikely that either population
of cells was coding for intended eye movements or that we were
recording from the LIP area.
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Figure 11.
Responses to memory delayed saccade task. After
the animal fixated on a central point, a target was presented in one of
two locations (either in or out of the RF) for 500 msec. The animal was
required to maintain fixation until the fixation point was extinguished
(which occurred at a random time) and then make a saccade to the
location where the target had been. Average responses for MT
(top) and VIP (bottom) neurons are shown
aligned to the target offset at 500 msec. Solid lines
are trials in which the target was presented in the RF; dashed
lines are trials in which the target was presented outside the
RF. Responses were smoothed with a 20 msec Gaussian filter.
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DISCUSSION |
Our results suggest that attentional state can alter the
relationship between neuronal response and behavioral performance. Although the psychophysical and neurophysiological effects of attention
have been extensively studied individually, to our knowledge their
interactions have not been directly examined previously. Understanding
how attention alters the processing of visual information is important
because attentional modulations have been seen in every visual cortical
area examined, and in some cases they are a major component of the
activity of a neuron.
We were particularly interested in understanding whether varying
spatial attention was equivalent to varying stimulus strength for both
the neuronal response and the behavioral performance. Withdrawing
attention reduced behavioral performance in our motion detection task
and also reduced neuronal responses in MT and VIP neurons. However, in
MT neurons the average amount of attentional modulation of the neuronal
signals was usually less than the average attentional modulation of
behavioral performance. In contrast, the average attentional modulation
of neuronal signals in the VIP area was greater than the average
attentional modulation of behavioral performance.
Using a reaction time task allowed us to focus on the part of the
neuronal response that most likely contributed to the behavioral performance. An assumption for most of our analysis was that the neuronal signal indicating coherent motion was the driven response above the 0% baseline during the first 300 msec of the coherent motion. One issue is whether a different measure of neuronal
performance would yield qualitatively different results. Many studies
that try to link neuronal response with behavioral performance use a
receiver-operator characteristic (ROC) model of signal detection, which uses a statistical description of the neuronal response to
produce a performance metric (Green and Swets, 1966). To determine whether such a description of the neuronal signal would affect our
results, we constructed an ROC model of the neuronal responses using
the two 300 msec periods before and after the start of the coherent
motion as our noise and signal distributions. The result of the ROC
model was qualitatively identical to the analysis reported here.
Similarly, our results were not sensitive to the absolute firing rate
during the coherent motion, although in this case the average MT
modulation was now much closer to the behavioral modulation (Fig. 7).
Other time windows (i.e., 200 and 400 msec) used to compute neuronal
responses did not affect our results. Also, our results were not
appreciably different when time windows were aligned to the monkey's
response to account for different reaction times on individual trials.
Thus, we believe that the relevant feature of the neuronal response
that contributed to the behavioral performance was captured in our analysis.
The amount of extraretinal enhancement observed in MT neurons ranges
from small (Ferrera et al., 1994; Seidemann and Newsome, 1999) to very
large (Treue and Maunsell, 1999). This wide range of MT area
attentional modulation has been hypothesized to be task dependent,
although the results from our two monkeys suggest that the amount of
modulation also depends on differences between animals. The differences
in the amount of attentional modulation between our two animals
(especially in MT) could be attributable to the fact that the animals
used different strategies to allocate attention. However, the similar
behavioral effects of attention suggest that this was not the case and
emphasize the value of simultaneous neuronal and behavioral
measurements of attention. That VIP neurons exhibited larger modulation
than MT neurons in both animals suggests there is a significant
difference between these areas. The large effect of attention in VIP
neurons seen here is consistent with human imaging experiments that
have demonstrated strong attentional modulation in regions of parietal
cortex (Corbetta, 1998; Kastner and Ungerleider, 2000).
One limitation of our study comes from the constraint that we could
only sample the unattended condition at a single stimulus strength. It
is unknown, for example, whether the MT neuron in Figure 4 would also
exhibit nearly the same attentional modulation as the behavior at
several different levels of motion coherence. A single unattended data
point fails to distinguish whether attention produces a shift or change
in slope for neuronal and behavioral performance as a function of
stimulus strength. However, if attention behaved in a manner equivalent
to varying stimulus strength, then the stimulus level used for the
invalid cueing trials would not affect the results. Because we found
that this was not the case for the single invalid point used here, the
addition of other invalid cueing points would not have changed our
conclusions. Thus, even if attention was observed to be equivalent to
varying stimulus strength at other motion coherences, it would not
change the results reported for the motion coherences used here.
Task difficulty or effort has been shown to modulate neuronal response
(Spitzer and Richmond, 1991) and affect attentional modulation
(Boudreau and Maunsell, 2001). It is possible that task difficulty may
have affected our results. It is unlikely, however, that the level of
motion coherence chosen to examine the effects of attention had much
influence on task difficulty. This is because the invalidly cued trials
were only 20% of the total trials. Task difficulty in our experimental
design would likely depend on the range of coherences used for the
validly cued trials (80% of total trials). Because the animals never
knew in advance the strength of the coherent motion on any given trial, they probably maintained a constant level of effort. A constant level
of effort by the animals is supported by the observation that
attentional modulation of the 0% coherent response was equivalent for
correct and missed trials (Fig. 10). How varying task difficulty would
affect our results, however, is unknown and remains to be tested in
future experiments.
The relationship between behavioral and neuronal performance did not
persist across changes in behavioral state for average responses in
either MT or VIP neurons. One interpretation is that the correspondence
between neuronal activity and behavioral performance, observed in other
studies (Britten et al., 1992), exists only for conditions of high
attention. However, we think it is more likely that a correspondence
survives changes in attentional state, but only for those specific
cortical regions with response properties best suited to task demands.
This interpretation is based on the observation that the average
neuronal enhancement by attention increases as a function of cortical
hierarchy. Figure 12 shows this using
data from several previous reports that measured spatial attentional
modulation in more than one cortical area using identical behavioral
conditions. The important observation from Figure 12 is that for each
study that measured spatial attention in more than one area, the amount
of modulation was greater in higher cortical areas.
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Figure 12.
Attentional enhancement of neuronal responses
increases as a function of cortical hierarchy. Average neuronal
enhancement was taken from studies that measured spatial attention in
more than one area in visual cortex. Filled circles are
data from this report using absolute firing rates; open
squares are data from McAdams and Maunsell (1999); open
circles are data from Treue and Maunsell (1999); and
filled squares are data from Ferrera et al. (1994).
Cortical hierarchy levels are as defined by Felleman and Van Essen
(1991).
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Although the reason for greater attentional modulations in later stages
is unknown, it has important implications for the relationship between
neuronal activity and behavioral performance. If stimulus-response
functions are similar for neurons in different cortical areas (e.g., as
they are for MT and VIP neurons), then only certain levels of cortical
processing will have a mean amount of modulation that is consistent
with that needed to account for the attentional modulation of behavior.
Although one might expect that this should occur at the latest stages
of visual cortex, the current results suggest that this is not always
the case. Neurons in the latest stages of cortex often have elaborate
and specific response properties and may not be best suited for
performance in tasks such as the motion detection used here. In our
task, the average attentional modulation in MT and VIP neurons fell to
either side of the average behavioral modulation. This suggests that an
intervening area (perhaps the middle superior temporal area)
would have exhibited the same amount of attentional modulation as seen
in the behavioral response. This raises the intriguing possibility that
the site where attentional modulation of neuronal and behavioral
responses match could indicate which region of visual cortex is most
directly involved in a given perceptual task.
If the behavioral effects of attention closely follow the modulation of
neuronal activity in visual cortex, then the increased attentional
modulation in later stages of the cortical hierarchy would have
specific consequences. Later stages of visual cortex contain neurons
that respond to increasingly complex stimulus attributes. The MT area,
for example, is thought to represent basic features such as translation
and depth (DeAngelis et al., 1998). In contrast, the VIP area contains
neurons that respond to several types of visual and extraretinal
signals, including tactile stimulation of the face, vestibular
stimulation, optic flow, and targets moving in either retinocentric and
head-centered coordinates (Schaafsma and Duysens, 1996; Colby and
Goldberg, 1999). If the nature of the stimulus analysis required by a
perceptual task determines the particular level of cortical
representation used, then a simple perceptual task that depended
primarily on early representations in visual cortex may demonstrate
little behavioral effect of spatial attention, whereas more complex
perceptual tasks may produce much larger behavioral effects of attention.
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FOOTNOTES |
Received June 29, 2001; revised Dec. 18, 2001; accepted Dec. 18, 2001.
This work was supported by National Institutes of Health Grant R01
EY05911. J.H.R.M. is an Investigator with the Howard Hughes Medical
Institute. We thank J. Assad, C. Boudreau, J. DiCarlo, G. Ghose, and T. Yang for helpful discussion on all aspects of this project. We also
thank D. Murray and T. Williford for technical assistance.
Correspondence should be addressed to Erik P. Cook, Division of
Neuroscience, S-603, Baylor College of Medicine, One Baylor Plaza,
Houston, TX 77030. E-mail:
erik{at}sensor.neusc.bcm.tmc.edu.
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T. M. Herrington, N. Y. Masse, K. J. Hachmeh, J. E. T. Smith, J. A. Assad, and E. P. Cook
The Effect of Microsaccades on the Correlation between Neural Activity and Behavior in Middle Temporal, Ventral Intraparietal, and Lateral Intraparietal Areas
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TOP
ABSTRACT
INTRODUCTION
