stimuli are represented as noisy information channels. Therefore, the accuracy of selection might be predicted by how well neuronal
colliculus (SC) neurons during performance of a color, oddball selection task. We recorded from sets of four neurons simultaneously,
allowing us to explore the relationship between SC neuronal activity and performance accuracy. SC target neurons had higher levels of
discharge than SC distractor neurons in subsets of trials when selection performance was very accurate. In subsets of trials when
associated with larger separations between neuronal activity from targets and distractors as quantified by the receiver operating char-
acteristic (ROC) area and d? (an index of discriminability). Poorer performance was associated with less separation of target and
increased as saccade onset approached. Together, the results indicate that SC buildup neuronal activity signals the saccadic eye move-
A challenge to motor systems is the need to select a single action
from among multiple options. Evidence implicates the superior
colliculus (SC), a topographically organized map of rapid eye
movements (saccades) located within the midbrain, in the pro-
cess of selection for action (Apter, 1945; Robinson, 1972; Basso
and Wurtz, 1997, 1998; Horwitz and Newsome, 1999, 2001;
Krauzlis and Dill, 2002; McPeek and Keller, 2002, 2004; Carello
and Krauzlis, 2004), although exactly how SC contributes to se-
lection for action is unknown. Traditionally, experiments in SC
were performed with single spots of light as targets for saccades.
more complex stimulus displays that include targets and distrac-
tors (Ottes et al., 1987; Glimcher and Sparks, 1993; Basso and
Wurtz, 1998; Edelman and Keller, 1998; McPeek and Keller,
2002; Port and Wurtz, 2003). The stimulus displays are some-
times adopted from those used in psychophysical experiments
designed to explore visual search (Treisman and Gelade, 1980;
Egeth and Yantis, 1997; Schall and Thompson, 1999; Palmer et
al., 2000; Wolfe and Horowitz, 2004). In the simplest arrange-
ment, an array of visual stimuli appears and one member of the
array differs along a single feature dimension such as color or
orientation. The task is to select the oddball stimulus and make a
saccadic eye movement to it. Analyses based in signal detection
of neurons made one at a time, are used to determine whether
and at what time neurons discriminate between targets and dis-
tractors (Schall et al., 1995; Thompson et al., 1996; McPeek and
iments indicate that saccade target selection likely results from a
distributed network of activity across populations of neurons
within at least frontal eye field (FEF), lateral intraparietal area
(LIP), and SC (Hanes and Schall, 1996; Thompson et al., 1996;
Schall and Hanes, 1998; Schall and Thompson, 1999; Hanes and
Wurtz, 2001; Pare ´ and Wurtz, 2001; McPeek and Keller, 2002;
Thomas and Pare ´, 2007).
In this study, we apply an SDT approach to further our un-
derstanding of how the SC contributes to selection. Our ap-
used as a statistical tool to determine when neurons distinguish
the presence of a target in their response field (RF) relative to
saccade latency (McPeek and Keller, 2002). Our approach was
more similar to that used in extrastriate cortical areas to explore
This work was supported by National Institutes of Health Grant EY13692 (M.A.B.). We also acknowledge the
TheJournalofNeuroscience,March19,2008 • 28(12):2991–3007 • 2991
the relationship between neuronal activity
and perceptual accuracy (Britten et al.,
al., 2001). In this latter approach, individ-
ual trials are sorted based on performance
istic (ROC) curves are computed to deter-
dict the behavioral choice based on the
1996; Shadlen et al., 1996; Parker and
al., 2004). In both applications of SDT,
responses of neurons to the preferred
stimulus (or when the stimulus is a target)
are compared with responses of the same
neurons to a nonpreferred stimulus (or
when the stimulus is a distractor). This
comparison is referred to as the neuron,
anti-neuron assumption (Britten et al.,
tween SC neuronal activity and selection
study we also address the timing of selec-
tion. Four stimuli appeared in the visual
field. Importantly, we recorded from sets
of four neurons simultaneously: one neu-
ron representing each of the four SC neu-
ronal populations underlying the selec-
tion. Therefore, we did not need the neuron, anti-neuron
assumption. By performing ROC analysis and computing d?, an
index of discriminability, we found that the separation and dis-
criminability of the neuronal activity representing targets and
distractors scaled with performance accuracy. The scaling of dis-
criminability with performance accuracy occurred in the neuro-
nal activity measured even 20 ms before the onset of a saccade.
ity signals a decision variable for saccadic eye movements.
Physiological and eye movement monitoring procedures. For electrophysi-
ders, and eye loops were implanted in three rhesus monkeys (Macaca
2005). We recorded from 176 neurons within the intermediate layers of
monkey m, we recorded 13 sets (n ? 52). In monkey c, we recorded 17
up/prelude (McPeek and Keller, 2002; Li and Basso, 2005), except six,
Li and Basso, 2005). Neurons were recorded with four independently
moveable, tungsten microelectrodes (FHC, Bowdoin, ME) with imped-
ances between 0.3 and 1.0 M? measured at 1 kHz. Four electrodes were
aimed at one SC and two were aimed at the other SC. Action potential
waveforms were filtered and amplified by a differential amplifier (Alpha
Omega, Nazareth, Israel; MCP-Plus) and then sampled and digitized
(Measurement Computing, Norton, MA; PCI-DAS4020/16). The digi-
program (Mex) allowing the experimenter to sort waveforms in real
as waveforms and sorted off-line to confirm the adequacy of the on-line
in Delphi 5.0) that sorted spikes based on time-voltage criteria.
Using the magnetic induction technique (Fuchs and Robinson, 1966)
(C.N.C. Engineering, Seattle, WA), voltage signals proportional to hori-
zontal and vertical components of eye position were filtered (8 pole
Bessel ?3 dB, 180 Hz), digitized at 16-bit resolution and sampled at 1
kHz (National Instruments, Austin, TX; PCI-6036E). The data were
saved for off-line analysis using an interactive computer program (Dex)
designed to display and measure eye position and calculate eye velocity.
We used an automated procedure to define saccadic eye movements by
applying velocity and acceleration criteria of 50°/s and 5000°/s2, respec-
by the experimenter.
All experimental protocols were approved by the University of Wis-
consin, Madison, Institutional Animal Care and Use Committee and
complied with and generally exceeded the standards set by the Public
Health Service policy on the humane care and use of laboratory animals.
acquisition and visual stimulus generation system (Rex, Vex and Mex,
developed and distributed by National Institutes of Health) (Hays et al.,
position and four channels of neuronal data. Trained monkeys sat in a
custom designed primate chair with head stabilized during the experi-
mental session (typically 3–5 h). Visual stimuli were rear-projected onto
OR) with a native resolution of 1024 ? 768 and operating at 60Hz. A
photocell secured to the screen sent a transistor–transistor logic pulse to
the experimental personal computer (PC) providing an accurate mea-
sure of stimulus onset. The fixation spot at the center of the screen had a
(mean of three measurements) luminance of 1.52 cd/m2. Visual stimuli
each had luminance values of 5.8 cd/m2m(mean of three measurements
each). The background luminance was 0.58cd/m2(mean of three mea-
surements). The PC for the visual stimulus display was a slave device to
the PC used for experimental control and data acquisition.
2992 • J.Neurosci.,March19,2008 • 28(12):2991–3007 KimandBasso•DecisionSignalsinSuperiorColliculus
After fixating on a centrally located spot (0.2° diameter) for a random
time of 1800–2300 ms, four spots (0.5° diameter) appeared and the
central spot disappeared. Each spot was located in the center of each
empirically defined RF of the four SC neurons (see Fig. 1a). The task
required monkeys to choose the differently colored target within ?300
ms by making a saccade to the differently colored spot immediately after
the disappearance of the fixation spot (simultaneous with the array on-
set). The target could be either red among green distractors or green
among red distractors. The color arrangement of the display was fixed
each day of recording but varied across recording days. After making a
spot was randomized among the four possible locations. On interleaved
1a,b). Two spots appeared in each hemifield although the exact location
the SC. Further details are provided below.
Data analysis. All statistical analyses were performed using Matlab
(MathWorks, Natick, MA). We performed statistical comparisons when
appropriate using the nonparametric test of median differences, Wil-
coxon rank sum (Keppel, 1991). We computed ROC curves based on
SDT (Green and Swets, 1966; Cohn et al., 1975; Bradley et al., 1987;
Britten et al., 1992; Thompson et al., 1996). For each trial, we convolved
each spike in a spike train with a Gaussian having a ? ? 4 ms (McPeek
ms Gaussian kernel. We divided neurons from each data set by perfor-
groups. One was ?75% and ?75% accuracy rates (see Fig. 6); a second
was 35, 65, 75, 85, and 100% accuracy rates (see Fig. 7); and a third was
correct trials versus error trials in which the same saccade was made (see
Fig. 12). Data were also sorted by target distance from the fixation point
(see Fig. 8a) and saccade velocity (see Fig. 8b). To compute ROC curves,
we computed the probability that the discharge rate exceeded a criterion
criterion was incremented from the minimum to the maximum dis-
rate/100). A probability value was computed for each criterion. A single
and the entire ROC curve was generated from all the criteria. The area
tions (target and maximum distractor neuronal activity) and provides a
measure of the probability that a random draw from each of the two
distributions would yield a value that is larger for the target than for the
distractor when the monkey correctly selects the target. An ROC area of
0.50 indicates that the two distributions overlap completely. To quantify
the discriminability of the target and distractor neuronal activity distri-
butions, we computed d?, the ratio of the differences between the means
of the distributions to the sum of the SDs of the distributions d? ?
9–11), we computed the mean discharge rate during the 100 ms time
interval preceding saccade onset for the target neuronal activity. The
distractor with the highest discharge rate during the same interval was
determined on each trial. In so doing, the distractor neurons contribut-
application of a max rule for every trial. For the time course analysis
shown in Figure 11, we performed the ROC analysis as described above
but across 1 ms intervals, forward in time beginning at the onset of the
stimulus array and ending at saccade onset. Whereas for the stationary
ROC analysis we selected the distractor activity with the maximum dis-
distractor neuron activity used for analysis was determined dynamically
by selecting the distractor with the largest activity (spikes per second) at
each millisecond interval of each trial.
Trained monkeys made saccades in a task requiring the selection
of a single red target from an array of one red and three green
stimuli (or vice versa) (Fig. 1a). We recorded from four neurons
simultaneously (n ? 44 sets of four) from three monkeys using
class of neuron were assessed empirically and statistically using a
as buildup/prelude neurons (see Materials and Methods) (Mu-
2002). For the selection task, we arranged the array of stimuli so
that each stimulus fell within the center of one RF of each of the
on the positioning of each of the four electrodes within the SC,
performance ?75% correct. b, Data from two monkeys selected for ?75% correct perfor-
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 2993
the stimulus positions were not always perfectly symmetric
within the visual field. Although for all experiments, two stimuli
of any of the RFs. For analyses and clarity of display, we normal-
ized the stimulus positions to 45, 135, 225, and 315°.
When a single stimulus appeared in the visual field, monkeys
made saccades to the correct target location invariably, not sur-
prisingly (Fig. 1a,c). In contrast, when an array of stimuli ap-
peared, monkeys made saccades as quickly to the differently col-
ored target but sometimes made errors and selected the wrong
on some experimental days, however, performance was less ac-
curate (?75%) (Fig. 1e). For the example set of data shown in
Figure 1, when the target was located at the 315° position, the
monkey sometimes made saccades to one of the green stimuli
even though the red stimulus was the target. When the monkey
selected a green stimulus it did not receive a reward. In some
the target altogether, choosing a distractor stimulus instead (Fig.
1f). These trials also were not rewarded. Figure 1 shows just one
example data set from one monkey. Across our three monkeys
the positions of the target varied within experimental sessions.
Whether there was a green target among red distractors or a red
target among green distractors varied across experimental days.
As indicated above, the positions of the stimuli in the display
This led to asymmetric visual displays and as a result, variability
in the monkeys’ performance. This turned out to be useful, be-
cause it meant that we could explore the relationship between
tion) remained the same. This tack is similar to that used when
choice accuracy is compared with neuronal activity during per-
2002) or in a random dot motion discrimination task when the
1992, 1996). We first describe the behavioral performance of our
monkeys. Then we show examples of activity from target and
distractor neurons in the task. We then present the SDT analysis
We sorted the 44 data sets based on performance into three
three monkeys performed the task similarly and reasonably well
(monkeys c and m), whereas monkey w performed the task par-
from 26 recording sessions. For the analysis described below,
there were 3199 correct trials and 1054 errors trials from these
two monkeys. Because monkey w performed the task less well
data from monkey w were accumulated from 14 recording ses-
sions and include 1391 correct trials and 2862 error trials. Figure
2 shows the performance across all experimental sessions and all
performance was good (?75%). For two monkeys, the majority
of trials were performed correctly and the monkeys made sac-
cades to the target (Fig. 2a, black bars). Although in some cases,
they performed less well and made saccades to the distractors
instead of the target (Fig. 2b, gray bars). On a minority of the
trials, monkeys made errors and looked only at the distractors
key chose each of the distractor locations with equal probability
(Fig. 2c, inset), it overall made more errors than the other two
monkeys (Fig. 2, compare a,b, insets).
Because it is known that increasing the distance between tar-
gets and distractors can influence performance (Meinecke, 1989;
that the variability in the monkeys’ selection accuracy may result
from differences in the location of the target relative to the fovea
and relative to the other distractors. Because our stimulus con-
figurations were constrained by the sites of electrode penetra-
tions, we considered this possibility. Note, however, that two
opposite hemifield for all cases. Nevertheless, as it turned out,
monkeys tended to make more errors because of the position of
the stimuli within the array relative to the fixation point. We
found that as the distance of the target from the fovea increased,
performance accuracy decreased (Fig. 3a). There was a statisti-
cally significant correlation between the ratio of correct to total
trials performed and the distance of the target from the fixation
point (r ? ?0.61, p ? 0.001) as measured by target distance (in
degrees) ??x2? y2, where x was the horizontal position of the
fixation point( )
saccade latency (ms)
r = -0.61
r = -0.49
correct ratio (%)
position relative to the fixation point in degrees. The amplitude of the target position was
associated with ?80% accuracy as indicated schematically by the gray ellipse. b, Ratio of
correct to total trials (percentage) is plotted against saccade latency. As the percentage of
correct choices increased, saccade latency decreased. The solid black line is the best fit linear
Saccade choices vary with target locations in the selection task. a, The ratio of
2994 • J.Neurosci.,March19,2008 • 28(12):2991–3007 KimandBasso•DecisionSignalsinSuperiorColliculus
This result suggests that the monkeys found the task easier when
the target was closer to the fixation point, although we did not
explicitly manipulate variables to make the task easier for the
correct ratio and saccade latency was statistically significant (r ?
?0.49, p ? 0.001). The distance of the target from the fixation
point, however, was not the sole factor responsible for variations
in choice accuracy because monkeys were able to perform with a
high level of accuracy for all target distances on at least some of
the trials. This is indicated schematically by the gray ellipse
around the data shown in Figure 3a.
In light of these behavioral observations, we reasoned that we
could capitalize on the variation in behavioral performance to
provide insight toward understanding the relationship between
neuronal activity in SC and saccade choice accuracy. Below we
neuronal activity predicts saccade choice in the oddball selection
Figure 4 shows one example from four SC neurons recorded
simultaneously. Each neuron had a single stimulus within its RF
and, therefore, each neuron had a discharge of action potentials
associated with the onset of the visual stimulus. After the initial
visual response, the activity of the neurons representing the dis-
tractors decreased over time (Fig. 4a–c, green traces) and the
activity of the neuron representing the target increased (Fig. 4d).
Ultimately, the target neuron showed the characteristic saccade-
related burst of activity (Wurtz and Goldberg, 1972; Sparks,
1975) (Fig. 4d, red traces). For all of the trials shown in Figure
4a–d, the monkey made the correct saccade to the target located
these target trials was 160.40 ms and is indicated in Figure 4a–d
on the abscissa by the filled red circle.
For each experimental session, the target positions were ran-
domized among the four possible locations. An example of the
the subset of trials in which the target was located in the 45°
position is shown in Figure 4e–h. For this target position the
monkey performed with 67.3% accuracy. The monkey made the
correct saccade on 35 of 52 trials. The mean saccade latency on
correct target trials was 182.40 ms, indicated on the abscissa in
Figure 4e–h by the filled red circle. Despite the fact that these
trials were also correct, the mean saccade latency was longer for
this target position than for saccades made to the 315° position.
The difference between the saccade latency in the 100% accuracy
subset of trials (160.40 ms) and the saccade latency in the 67.3%
accuracy subset of trials (182.40 ms) was statistically significant
(Wilcoxon rank sum, p ? 0.01). It is unclear what exactly caused
the monkey’s poor choices or the increase in latency in the 45°
position trials compared with the 315° position trials but the
observation is consistent with an increase in difficulty on these
trials. What might cause the increase in difficulty could be a
change in the activity of the neurons representing the targets
(Basso and Wurtz, 1997, 1998; Dorris and Munoz, 1998). But,
perhaps also a change in the activity of neurons representing the
rons might be associated with the certainty monkeys had regard-
ing the target and therefore, might be related to selection
upward arrow (array onset). The square in the center of the four panels show the stimulus
a–d show the neuronal activity when the monkey accurately performed all trials with the
marks the average saccade latency made to the correct target for reference. e–h show the
45° position. The monkey performed the 45° position trials less well (35 of 52 trials; 67.3%
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 2995
when performance accuracy varied, we compared directly the
neuronal activity for correct trials when the saccade was made to
the 315° target with the neuronal activity for correct trials when
shows the spike density functions (SDFs) from the 100% correct
trials and the 67.3% correct trials superimposed. The gray traces
related increase in activity associated with correct trials in the
neuron coding the 45° target (Fig. 4j, black trace) and the 315°
target (Fig. 4l, gray trace) appeared as expected, but is blunted
because of the vertical scaling. The neuronal activity associated
with the distractor positions, however, was different in these two
subsets of trials. This was particularly evident for the distractor
located down and to the left (Fig. 4k, compare gray and black
traces). Note that the difference in the activity of these two neu-
rons shown in Figure 4, i and k, occurred despite the fact that the
of trials (Fig. 4i,k, shaded rectangle). When the monkey per-
formed the selection task with 100% accuracy, the activity of
these distractor neurons was lower than when the monkey per-
gray and black traces). This observation suggests two important
things. First, SC neuronal discharge coding targets and distrac-
tors contributes to saccade selection in this task. Second, it is the
relative level of activity between the target neurons and the dis-
tractor neurons that determines selection accuracy.
To quantify the relationship between target and distractor neu-
ronal activity and selection performance, we adopted an SDT
SDT is that sensory stimuli are represented as random variables
that are noisy and independent from one another. Therefore, on
each trial, each stimulus in the array ought to activate a popula-
tion of neurons with a level of activity that varies about some
mean level. To select the saccade target, the mean level of activity
of neurons representing the target should be higher than the
more, the accuracy of the selection should be predicted by the
disciminability between the target and distractor neuronal activ-
ronal activity representing the target and the distractors are
widely separated, such as would occur with large mean differ-
In contrast, if the distributions of neuronal activity representing
the target and distractors are highly overlapping, then selection
accuracy should be poor. Therefore, we first measured the mean
and SD of activity from target and distractor neurons across all
SC neurons recorded in correct and error trials. After combining
all of the data, we computed the probability of obtaining a par-
ticular discharge rate on a trial-by-trial basis for target and dis-
tractor neurons. We then sorted all the neurons using only the
correct trials into two groups based on performance accuracy.
Figure 5 shows the probability distributions of neuronal ac-
target neurons and the distractor neurons. The neuronal activity
measured in the target neurons and the distractor neurons in
was low, with a mean discharge rate of 85.82 spikes/s and a SD of
65.86 spikes/s (Fig. 5a, black lines) across all the data from two
monkeys (n ? 30 sets of four; 120 neurons from monkey m and
c). In contrast, the mean activity measured in the target neurons
The probability of measuring a particular discharge rate in target and distractor neurons is
calculate the distractor neuron activity. The black lines show the probability distribution of
is the same as in a. c shows the same as b, but for trials in which monkeys performed with
2996 • J.Neurosci.,March19,2008 • 28(12):2991–3007 KimandBasso•DecisionSignalsinSuperiorColliculus
was much higher. We measured a mean discharge rate of 248.75
spikes/s and an SD of 112.93 spikes/s (Fig. 5a, gray bars).
The results obtained when four stimuli appeared and mon-
keys correctly selected the target with an accuracy ?75% is illus-
trated in Figure 5b. The mean discharge rate across all neurons
from monkeys c and m (n ? 30 sets of four; 120 neurons) mea-
with ?75% accuracy was 183.90 spikes/s (Fig. 5b, black lines).
The SD of the distribution of discharge rates was 93.80 spikes/s.
The mean discharge rate measured from target neurons when
target and distractor neurons in the ?75% performance condi-
217.13 spikes/s (Fig. 5c, black lines) whereas the mean discharge
SD values of the distributions of discharge rates in the target and
keys c and m there was a decrease in the neuronal activity associ-
ated with the target (262.07–241.24 spikes/s) and an increase in
neuronal activity associated with the distractors (183.90–217.13
The median differences between these two conditions were sta-
tistically significant (Wilcoxon test, p ? 0.001). Although the SD
values for the target neuron activity decreased from 111.13 to
89.55 spikes/s when performance decreased, this difference was
not statistically significant (Levene’s test, p ? 0.76). In contrast,
distributions when performance went from ?75 to ?75%
(93.80–107.90 spikes/s). This increase in SD was statistically sig-
nificant (Levene’s test, p ? 0.01).
These results show that the probability of obtaining a level of
discharge in target and distractor neurons within the SC varies
with performance accuracy. When performance accuracy is high
rons is high. When performance accuracy is low, the difference
between the discharge of target and distractor neurons is low.
results are consistent with the hypothesis that when the popula-
ronal activity are less discriminable, performance will be poor.
Therefore, we conclude that it is the relative level of target and
distractor neuronal activity in buildup neurons of the SC that
predicts performance accuracy. We quantify this using ROC
In contrast to monkeys c and m, monkey w performed the
selection task overall less well (Fig. 2, insets). For this reason, we
presented the data from monkey w separately. The insets in Fig-
ure 5 show the probability of discharge rates measured in target
and distractor neurons sorted by performance for monkey w.
When only a single target appeared, the mean and SD of the
discharge rates across all neurons (n ? 14 sets of 4; 56 neurons)
for distractor neurons were 143.93 and 132.09 spikes/s, respec-
tively (Fig. 5a, black lines, inset). The mean and SD of the dis-
charge rate for the target neurons were 253.00 and 124.23
spikes/s, respectively (Fig. 5a, gray bars, inset).
When four stimuli appeared and monkey w performed with
?75% accuracy the mean and SD of the discharge rates for dis-
tractor neurons were 216.57 spikes/s (85.21 spikes/s) (Fig. 5b,
inset, black lines). The mean and SD of the discharge rates across
all neurons for target neurons when monkey w performed the
task with ?75%, were 211.24 and 104.52 spikes/s (Fig. 5b, inset,
gray bars). This is a mean ?5 spikes/s difference between the
target and distractor neuronal activity. The median difference
was 17.2 spikes/s and, although slight and in the opposite direc-
tion, the median differences were statistically significant (Wil-
performance was ?75% accurate, the mean and SD of the distri-
butions of distractor neuron discharge rates were 261.58 and
98.49 spikes/s, respectively, whereas for the target neurons the
mean and SD of the discharge rate distributions were 272.74 and
130.56 spikes/s, respectively. Although the mean difference was
?11 spikes/s, the median difference was 14.56 spikes/s between
target and distractor neuronal activity and in the correct direc-
tion (i.e., target ? distractor). These differences, however, were
not statistically significant (Wilcoxon test, p ? 0.25). Neverthe-
was greater than the relative difference in activity for the ?75%
accurate trials. The data from this monkey show an extreme case
formance in the selection task was exceptionally poor. Despite
correlated with the level of performance even in this monkey
whose performance was overall very poor (51%).
As a step toward quantifying the discriminability of the target
selection task, we computed the area under the ROC curves and
the discriminability index d?, in the usual manner (see Materials
imum distractor neuronal activity across all the neurons from all
monkeys, for the subset of data in which monkeys performed
0.56 (Fig. 6a, black curve, inset for monkey w) (ROC area, 0.47;
d?, ?0.31). The ROC area 0.66 was significantly different from
performance was ?75% accurate, the ROC area was 0.57 and d?
was 0.24 (Fig. 6a, gray curve, inset for monkey w) (ROC area,
0.53; d?, ?0.02). This area also was significantly different from
0.50 (permutation test, p ? 0.01). The two ROC areas obtained
dition were significantly different from one another (permuta-
tion test, p ? 0.01).
To show the relationship between selection performance and
individual SC buildup neurons, we plotted the number of neu-
performance accuracy subset of trials (Fig. 6b) and for each neu-
ron in the ?75% performance accuracy subset of trials (Fig. 6c).
the number of neurons having a particular choice probability,
also referred to as a sender operating characteristic (Newsome et
2003; Uka and DeAngelis, 2004; Purushothaman and Bradley,
obtained an ROC area. These 120 neurons were associated with
behavioral performance that was either ?75 or ?75% accurate
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 2997
monkey w, 18 of 23 (78.26%) of the neurons had a statistically
significant CP (Fig. 6b, inset filled black bars) (permutation test
the subsets of trials in which performance accuracy was ?75%.
Twenty-nine of 41 (70.73%) neurons had a statistically signifi-
these, 18 of 29 (62%) had a CP ?0.50. The inset in Figure 6c
illustrates monkey w’s results. It is apparent from the illustration
that the range of CP (ROC areas) shifts toward higher values as
performance accuracy are predicted by the relative level of target
and distractor neuronal activity in SC buildup neurons. Consis-
tent with an SDT approach to visual search at the level of SC
neuronal activity was associated with better performance. De-
neuronal activity revealed how neurons in the middle temporal
(MT) area of extrastriate cortex signaled the direction of visual
motion within a display and how these signals predicted choice
Newsome, 1998). ROC analysis showed that the area under the
ROC curve determined from MT neuronal activity correlated
with the quality of sensory information used for the choice and
with choice accuracy. When the motion signal was strong, the
ROC area approached 1 and choices were highly accurate (New-
some et al., 1989a,b; Britten et al., 1992).
We applied a similar logic to assess the relationship of the
behavior of SC neurons in the four stimulus task to the selection
accuracy of the monkeys. It is important to make two points
regarding our application of this. First, in other analyses the ac-
tivity of neurons is compared with the activity of idealized “an-
tineurons” (neurons that are assumed to have an equal and op-
we did not have to make that assumption because we recorded
from neurons representing the distractor stimuli directly and at
the same time as we recorded neurons representing the target
stimulus. Therefore, the comparisons reported here arise from
what happens in the brain in real time. Second, in the current
analysis, the sensory information on which the selection was
made did not vary; monkeys always made a color discrimination
to select the target and SC neurons are insensitive to color (Ottes
et al., 1987). Therefore, our analysis is most comparable with
previous experiments in which CPs were determined on trials in
which the same motion coherence appeared (Newsome et al.,
1989a,b; Roitman and Shadlen, 2002; Mazurek et al., 2003), or
single stimulus led to two possible percepts, so-called bistable
images (Dodd et al., 2001; Krug et al., 2004). Together, we rea-
would indicate that the SC contributes information related to a
decision variable rather than signaling purely sensory or motor
information (Shadlen and Newsome, 2001; Roitman and
We pooled all of the trials from monkey m and c (separately
pooled monkey w trials), collapsed the correct and error trials
together to determine the percentage of total trials, and then
sorted all of the data based on the percentage of trials with selec-
tion accuracies ranging from 35 to 100%. Note that we only an-
was ?75% accurate (gray line). b, Distribution of ROC areas (CPs) for individual SC neurons
2998 • J.Neurosci.,March19,2008 • 28(12):2991–3007KimandBasso•DecisionSignalsinSuperiorColliculus
alyzed data from correct trials. We only included error trials to
We address the activity of neurons on error trials below.
the target neuronal activity and the distractor neuronal activity.
For each set of four neuron recordings we used the activity of the
distractor neurons with the highest level of discharge measured
d?, respectively (Fig. 7a, gray to black lines). For the 35% correct
subset, the ROC area was 0.55 and d? was 0.20. For the 65%
subset, the ROC area was 0.62 and d? was 0.41. For the 75% and
the d? values were 0.48 and 0.55, respectively. For the trials in
which monkeys performed with 100% accuracy, ROC area was
maximal at 0.70 and d? was maximal at 0.67. For monkey w, as
shown in the insets of Figure 7a, the ROC areas were 0.57, 0.51,
0.45, 0.48, and 0.43. Whereas the d? values were, 0.05, 0.33, 0.21,
?0.31, and ?0.43 for the 35, 65, 75, 85 and 100% conditions,
(Fig. 7b, solid line) whereas the ROC area (CP) increased from
of the discriminability between target and distractor neuronal
activity with performance (Fig. 7a, inset).
To determine whether the relationships between the ROC
area values, d? values, and the correct ratio were statistically sig-
nificant, we sampled the subsets of data for each of the accuracy
conditions and computed the ROC area 100 times. We then de-
termined the slopes of each of the relationships to obtain a dis-
tribution of slopes. One distribution was obtained for the d? to
correct ratio relationship and one distribution was obtained for
the ROC area to correct ratio relationship. Comparing the actual
slopes (obtained using the method of least squares) to the per-
muted slopes revealed slopes that were significantly different
from 0 in both cases (Fig. 7c) (ROC, r ? 0.97, p ? 0.01; d?, r ?
0.96, p ? 0.01).
Because we measured the discharge of neurons that included
activity close to the initiation of the saccade (100 ms before sac-
cade onset) we explored two other possible explanations for the
activity with performance accuracy. First, does discriminability
scale with saccade target amplitude? One possibility is that there
is a systematic difference in the level of discharge for target and
distractor neurons when the distance of the target relative to the
fixation point varies. To explore this possibility, we resorted the
data by the distance of the target from the fixation point and
recalculated the ROC curves. Figure 8a shows the result. The
ROC area values had a range that was similar to the range ob-
0.72). These values, however, did not scale with increasing target
distance as would be expected if discriminability between target
lack of relationship is further evident from the plot in Figure 8c.
(Fig. 8c, dashed line) (r ? 0.13; p ? 0.84). Similarly, d? and
distance of the target from the fixation point were uncorrelated
(Fig. 8c, solid line) (r ? 0.21; p ? 0.73).
ity between target and distractor neuronal activity scaled with
saccade velocity. It is possible for example that saccade velocity
decreased systematically with poor accuracy. There is some evi-
dence that the level of SC discharge is associated with saccade
velocity (Edelman and Keller, 1998). Therefore, if the scaling of
ROC area with performance resulted from systematic variations
in saccade velocity, we should see a similar relationship between
and performance accuracy. To explore this, we resorted the data
based on saccade velocity and recalculated the ROC curves (Fig.
8b,d). As obtained for the amplitude analysis, the range of ROC
areas was similar (0.55–0.69), but ROC area did not scale with
saccade velocity. This was most clear when the ROC area and d?
values were plotted against the peak velocities of the saccades
mance. The inset shows the entire data set for monkey w. All trials across all neurons were
level. Light gray indicates poorest performance (35% of trials performed correctly), and the
(solid line) are plotted on the ordinate. The abscissa shows the ratio of correct to total trials
Separation and discriminability of target and distractor neuronal activity scale
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 2999
(Fig. 8d). The ROC area showed no corre-
lation with peak velocity (Fig. 8d, dashed
lines) (r ? 0.34; p ? 0.96). d? also showed
solid lines) (r ? 0.15; p ? 0.98). Based on
these findings, we conclude that the sepa-
ration and discriminability of target and
distractor neuronal activities in SC scales
with saccade selection accuracy. The rela-
tionship cannot be explained by parame-
ters of the saccades nor can it be explained
by the sensory information leading to se-
lection because SC neurons are insensitive
to color. Therefore, we conclude that the
activity within the SC signals the saccadic
eye movement decision.
target probability showed that the delay
period of SC buildup neurons was modu-
lated by probability. The neuronal activity
immediately before the saccade in con-
trast, appeared identical regardless of the
probability (Basso and Wurtz, 1997, 1998;
Dorris and Munoz, 1998). More recently,
saccade-related activity within the FEF
movement neurons was reported to scale
with task difficulty (Thompson et al.,
2005). This result combined with the re-
sults shown in Figure 7 led us to ask
whether the relationship between perfor-
mance accuracy and target and distractor
neuronal disciminability would remain or
disappear as saccade onset approached. If
is a movement command, discriminability should not scale with
performance and therefore, there should be no relationship be-
tween ROC area (CP), d?, and correct ratio. If, however, the ac-
tivity of buildup neurons immediately before the saccade signals
the decision, the scaling of the relationship between correct ratio
and ROC area (CP) as well as d? should remain.
We performed the same ROC analysis as described above but
initiation of the saccade (Fig. 9). At 100 ms before the onset of a
(Fig. 9a, blue dashed lines) as shown as a black dashed line in
Figure 7b. As the discharge rate measurement interval ap-
proached the time immediately before the saccade (?20–0 ms),
ROC area continued to scale with performance accuracy, al-
though not as steeply (Fig. 9a, solid black line). Note also that
larger than that measured 100 ms before the saccade (Fig. 9a,
compare blue dashed line, solid black line).
area and performance accuracy over time, we normalized the
data by subtracting the ROC area obtained in the 35% accuracy
effectively a fractional change in CP with performance accuracy
(Fig. 9b). For the 100 ms epoch, the area under the ROC curve
to 100% (Fig. 9b, blue line). For the 80 ms epoch, the area under
the ROC curve also increased by 14% when performance accu-
racy increased from 35 to 100% (Fig. 9b, light gray line). For the
60 ms epoch, the area under the ROC curve increased by 12%
(Fig. 9b, gray line). For the 40 ms epoch, the area under the ROC
epoch, the area under the ROC curve increased by 7% when
performance accuracy increased from 35 to 100% (Fig. 9b, black
it was not as strong as earlier in time.
Quantifying the discriminability of the probability distribu-
tions using d? revealed the same trend (Fig. 9c). As performance
noted for the ROC area, by 20 ms before saccade onset, d? was
maximal across all accuracy conditions (Fig. 9c, compare blue
and black lines).
Given that we found a relationship between performance ac-
curacy and target and distractor neuronal discriminability even
20 ms before the onset of a saccade, we concluded that this activ-
the saccade decision. Figure 9 shows the relationship between
approached. Note, however, that the duration of the time over
which we measured the discharge was longer for the ?100 ms
3000 • J.Neurosci.,March19,2008 • 28(12):2991–3007 KimandBasso•DecisionSignalsinSuperiorColliculus
condition and the ?20 ms condition. Because this analysis ma-
nipulated both the time and the length of the epoch used, we
performed a second analysis using 20 ms duration epochs for
distractor neurons. Figure 10a shows the relationship between
the area under the ROC curve and performance accuracy for five
different 20 ms measurement epochs. When performance accu-
racy increased from 35 to 100%, the ROC area at 100 ms before
area (Fig. 10a,b, medium gray line). At 60 ms before saccade
onset, the ROC area ranged from 0.40 to 0.57, a 17% increase in
ROC area (Fig. 10a,b, gray line). By 40 ms before saccade onset
the ROC area increased from 0.60 to 0.77 when performance
increased from 35 to 100% accurate (Fig. 10a, darkest gray line).
gray line). Interestingly, at 20 ms before saccade onset, ROC area
increased from 0.79 to 0.89 as accuracy increased from 35 to
100% (Fig. 10a, black line). This is a 10% increase in ROC (Fig.
10b, black line).
Using the same bootstrapping procedure as used to analyze
the results shown in Figure 8, we sampled the data to generate
of the five measurement epochs, 100–80, 80–60, 60–40, 40–20,
and 20–0 ms before the initiation of the saccade. We then per-
formed a one-way ANOVA to compare the slopes across the
different measurement epochs. This analysis revealed a statisti-
cally significant main effect (Fig. 10b) (ANOVA, F(4,499)?
716.42; p ? 0.001). Post hoc paired comparisons (Tukey–
Kramer) revealed that the 20 ms epochs beginning at 80 ms and
60 ms differed significantly from the 20 ms epoch beginning at
100 ms. The 20 ms epochs beginning at 40 and 20 ms differed
significantly from the 100 ms epoch. Together these results lead
to interesting conclusions. First, the largest scaling of ROC area
(CP) relative to performance accuracy occurs between 40 and 80
ms before saccade initiation. Second, earlier than 80 ms before
area (CP) and performance accuracy. Likewise, within 40 ms of
ms and 60–40 ms before the saccade was initiation, we conclude
that the minimum time needed to integrate the discharge from
target and distractor neurons to predict saccade choice in this
task is at least 20 ms.
An additional way we explored the dynamics of the discrim-
inability between the target and distractor neuronal activity was
to perform the ROC analysis on a millisecond by millisecond
basis beginning at the time of array onset and lasting for 300 ms.
Because this is a common procedure for analyses performed by
others, we could also compare our results with those published
previously for FEF (Thompson et al., 1996, 2005), SC (McPeek
and Keller, 2002) and LIP (Thomas and Pare ´, 2007). We per-
formed the ROC analysis on data sorted by performance (?75%
accurate and ?75% accurate) as we had done for previous anal-
yses also to determine whether the time course varied with selec-
Figure 11 illustrates the result of the ROC analysis performed
over time. When the array appeared, the area under the ROC
curve was variable and even smaller than 0.50 indicating that the
time as saccade selection evolved, the ROC area increased and
reached the arbitrary, 0.75 area by 125 ms when performance
accuracy was ?75% (Fig. 11, black line). The same trend was
activity as the saccade evolves. a, ROC area computed from target and distractor neuronal
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 3001
observed on trials when performance was less accurate. Discrim-
inability between target and distractor neuronal activity took
longer to evolve: 149 ms after the array appeared (Fig. 11, gray
the hypothesis that monkeys found these trials more difficult.
and other saccade-related areas such as FEF (Thompson et al.,
1996) and LIP (Thomas and Pare ´, 2007).
To determine whether the time of neuronal discrimination
predicted the time of the saccade, for each neuron, we deter-
area of 0.75 (McPeek and Keller, 2002). Neurons that did not
19 of 120 neurons are illustrated for the ?75% accuracy condi-
tion. For the same trials we measured the latency of the saccade.
We then sorted the trials into three saccade latency ranges and
dium, 118.8–191.1 ms; and long, 137.8–222.3 ms; ?75%, short,
132.8–194.4 ms; medium, 144.2–229.3 ms; and long, 154.6–243
ms). A plot of the neuronal discrimination time against saccade
increasing relationship between discrimination time and latency
indicating that neuronal activity predicted the time of the sac-
cadic eye movement. Indeed, although the saccade latency was
shifted systematically toward longer times, the same trend oc-
correct trials (Fig. 11b, gray lines). Figure 11c shows the distribu-
tion of slopes obtained for all of the data shown in Fig. 11b.
task was variable. That allowed us to explore the relationship
between variations in activity among SC neurons representing
targets and distractors and variations in selection accuracy. We
ity between target and distractor neurons also increased. Simi-
larly when performance was poor, discriminability between tar-
get and distractor neurons was poor. The discriminability
between target and distractor neuronal activity took time to
evolve once the selection array appeared. The evolution of dis-
criminability, as measured by the time course of the area under
the ROC curve, was faster when performance was better. To-
gether these results indicate that the activity of SC buildup neu-
rons carries more information than simple movement com-
mands. This conclusion is in line with previous evidence
suggesting that SC neurons encode the location of a target as a
movement goal, independent of the movement (Basso et al.,
Wurtz, 2003). Although we do not think it is useful to argue
whether these signals are “sensory or motor,” we do think it is
fruitful to distinguish whether SC neurons signal something
higher order like a decision variable (Shadlen and Newsome,
2001; McPeek and Keller, 2002, 2004; Mazurek et al., 2003; Rat-
cliff et al., 2003, 2007). To address this, we reasoned that if SC
than the saccade itself or the stimulus itself, then the relative
activity of neurons representing the saccade endpoints (regard-
less of whether they were experimentally defined as targets or
distractors) and neurons representing the other possible loca-
conditions. In this way, the activity would indicate the level of
65 85 10 0
m onkey W
correct rati o ( %)
65 75 85
- 10 0 - 8 0m s
-8 0 - 6 0m s
-6 0 - 4 0m s
-4 0 - 2 0m s
-2 0 - 0ms
65 75 85
normalized ROC are a
65 75 85
65 85 100
m onkey W
ROC are a
interval beginning at ?100 and ending at ?80 ms before saccade onset. Shades of gray
plies. Note that the low values of ROC area early in the trial indicate that one of the three
Separation and discriminability of target and distractor neuronal activity in-
3002 • J.Neurosci.,March19,2008 • 28(12):2991–3007 KimandBasso•DecisionSignalsinSuperiorColliculus
confidence monkeys had regarding their decision. If, however,
the activity signals the movement output then the neuronal ac-
tivity on error and correct trials should be indistinguishable.
Up to now we have been referring to neurons as target neu-
rons based on the cue location. For the correct trials the cue and
saccade locations are the same. For error trials the cue and the
saccade locations are different. So, we will now refer to target
neurons as those that either contain the cue (correct) or indicate
the saccade location (errors). Figure 12a shows the comparisons
of the target neuronal discharge rates obtained in the correct
(?75% accuracy, black lines) and the error (0% accuracy, cyan
lines) trials. The mean level of discharge in target neurons in
correct trials was 262.07 spikes/s, whereas the mean level of dis-
paring the medians of each distribution indicated that the differ-
ences were not statistically significant (Wilcoxon test, p ? 0.93)
(Fig. 12a, compare thick black and cyan lines). If we left the
ity predicts the movement, because the activity was the same
regardless of the cue that instructed the movement. Comparing
the level of activity from the other neurons, however (distractors
in correct trials and nonselected target or distractors in error
trials), revealed that the mean level of discharge was 183.90
The median differences were statistically significant (Wilcoxon
test, p ? 0.01) (Fig. 12a, thin cyan lines are shifted slightly right-
by the illustration shown in Figure 12c. The mean discharge rate
for target neurons in ?75% correct and error trials was similar
(Fig. 12c, compare left black and cyan dots). The mean discharge
other words, greater suppression of distractor activity was asso-
ciated with better performance.
Performing the same comparisons, but now for the trials in
which performance accuracy was ?75%, showed a different
trials was 241.24 spikes/s, whereas in error trials the mean level
indicated that the differences were statistically significant (Wil-
coxon test, p ? 0.01) (Fig. 12b, compare thick orange and cyan
lines). This difference suggests that the target neuronal activity
may signal some level of confidence the monkey has in its deci-
sion. Interestingly, the level of activity from the other neurons
(distractors in correct trials and nonselected target or distractors
217.13 spikes/s in ?75% accuracy trials and 199.34 spikes/s in
error trials. The median differences were statistically significant
(Wilcoxon test, p ? 0.01) (Fig. 12b, thin orange lines are shifted
slightly rightward of the cyan lines). Again this interaction is
better appreciated by the illustration in Figure 12c. Comparing
the orange and cyan dots shows that the target activity was
of target activity and an enhancement of distractor activity were
associated with poor performance, but not with errors. It is as if
the relative level of target and distractor neuron activity reveals
the level of confidence the monkeys have in their saccade deci-
observe with single-electrode recordings.
?75% accurate. The gray line shows the ROC area when the monkeys’ performance was
The time course of the evolution of target and distractor neuron separability
KimandBasso•DecisionSignalsinSuperiorColliculus J.Neurosci.,March19,2008 • 28(12):2991–3007 • 3003
If the relative level of activity between
target and distractor neurons tells us
something about the confidence monkeys
had regarding their decision, we predicted
that ROC area between correct and error
trials should scale in a manner similar to
what we observed in Figure 7. It was pos-
sible however that the ROC area for error
for the very poor performance (recall the
35% accuracy condition had an ROC area
ure 12a–c, we suspected that the monkeys
had some level of confidence in their deci-
sion despite what the task instructed.
Therefore, we expected to see the ROC
rate and poor performance.
Figure 12d illustrates the result of the
ROC analysis when the data were sorted
?75% accuracy conditions. The orange
and black lines shown for the ?75 and
lines. The cyan line shows the ROC result
the ?75% trials was 0.66 and for the
?75% trials was 0.57. Each ROC area dif-
fered significantly from each other (per-
mutation test and ANOVA, F(2,2999)?
42320.77, p ? 0.001, post hoc Tukey–
Kramer). Monkey w, whose performance
overall was very poor, had no such rela-
tionship (error trial ROC area, 0.34).
Therefore, we conclude that the relative
activity of target and distractor neurons
within the SC signals not only the saccade
decision but the level of confidence mon-
keys had in that decision.
In this study, we described the results of an experiment in which
we recorded from four SC neurons simultaneously while mon-
keys performed a simple, oddball selection task (Schall, 1995;
McPeek and Keller, 2002). We found that when the discrim-
selection was likely to be accurate. When the discriminability
between the target and distractor neuronal activity was reduced,
conclusions. First, the combined activity of target and distractor
neurons contributes to the selection of a saccade. Second, the
relative level of buildup neuronal activity signals the saccade
choice and not the saccade characteristics. This relationship be-
tween target and distractor neuronal activity and performance
accuracy held true even immediately before the saccade onset,
ity. Importantly, we recorded the neurons representing targets
and distractors at the same time. Therefore, in contrast to previ-
ous work, the signals we measured were available to the brain in
real time, as the selection occurred. Below, we first discuss the
of the SC in selection and decision making.
Previous experiments in the SC in which the probability of a
particular target was manipulated and delay-period activity
was measured showed that the activity of buildup/prelude
neurons scaled with the probability of selecting a particular
saccade target (Basso and Wurtz, 1997, 1998; Dorris and Mu-
noz, 1998). Importantly, at the time the saccade occurred, the
discharge of buildup neurons did not vary. This indicated that
by the time of the saccade, the output of SC buildup neurons
signaled a movement command. The results described here
are in contrast to this. We found that even 20 ms before the
onset of a saccade, the discharge of buildup neurons scaled
with performance accuracy (Figs. 7, 9, 10). One possible ex-
task demands between the previous and the current experi-
ments. Previously, long delays (800–1200 ms) were imposed
between the time the array appeared, the time the target was
identified and the time the cue to move appeared (Basso and
Wurtz, 1998). In the current task, there were no delays. As a
target neurons (or neurons representing the stimulus chosen by the monkeys in the case of error trials), and the thin lines
3004 • J.Neurosci.,March19,2008 • 28(12):2991–3007KimandBasso•DecisionSignalsinSuperiorColliculus
result, monkeys performed with very high levels of accuracy in
the previous tasks and with varying levels of performance in
the current task. Furthermore, in the previous experiments
(Basso and Wurtz, 1998; Dorris and Munoz, 1998), the posi-
tions of the stimuli in the display were constrained by the
position of only one electrode whereas in the current experi-
ment the stimulus arrangement was constrained by the posi-
metric displays. We suspect that the emphasis on the speed of
selection combined with the asymmetric visual display led to
greater uncertainty regarding the target location even at the
time of the saccade and despite the constant sensory informa-
tion (color difference). As such, we think that the current
results are consistent with the previous results. They show that
SC buildup activity signals the certainty of the saccade choice.
But they extend this finding to include the activity right up
until the time of the saccade. We suspect that given more time
the uncertainty would have declined and performance would
have improved in the current task. Indeed, a decline in uncer-
tainty over time is consistent with models that rely on the
accumulation of evidence (Mazurek et al., 2003; Ratcliff et al.,
consistent with these ideas (Figs. 9, 10).
A second salient finding we report here is that correct target
selection depended on the level of discriminability between the
target and distractor neuronal activity. This indicates that the
relative level of activity among target and distractor neurons de-
termines which saccade is made. This result is reminiscent of a
(Ratcliff et al., 2007). On trials in which the brightness discrimi-
nation was easy the activity of SC neurons representing the cor-
rect saccade target was high and the activity of the SC neurons
representing the incorrect saccade target was low. When the
representing the correct saccade target was also high, but the
activity of the SC neurons representing the incorrect target was
higher than on easy trials. Although they sorted their data based
on task difficulty as defined by the experimenter (differences in
quality of the sensory information) and not by the performance
their result, nevertheless, is similar to what we report.
The results reported here may also shed light on previous
introduced into small regions of SC while monkeys performed
the same task as used here (McPeek and Keller, 2004). When a
albeit saccades occurred with longer latencies (McPeek and
to the distractors. Based on our results, we believe we can inter-
pret the muscimol results in an SDT framework. In the presence
level of activity would be reduced in the presence of muscimol,
but would remain highly discriminable from the activity of the
rest of the SC. When multiple possible targets appear, there are
multiple points of activation across the SC map. A focal musci-
inability between the target and distractor neuronal activities. As
we show here, the reduced discriminability would lead to errors
of saccade selection.
two dimensions, color and shape, recordings in the FEF revealed
distractor items were more similar to the target items compared
of the task as well as the performance (Bichot and Schall, 1999;
Bichot et al., 2001; Thompson et al., 2005). This result is very
similar to what we observed in the SC, although again, they ma-
nipulated the sensory information and we did not. Nevertheless,
the results suggest that in both the FEF and SC, as the discim-
inability between target and distractor neuronal populations in-
creases, selection is more likely to be accurate. Furthermore, the
activity associated with distractors in FEF movement neurons
occurring immediately before a saccade (?30 ms) was higher in
difficult compared with easy trials (Thompson et al., 2005). This
buildup neurons in SC are likely to be output neurons (Moscho-
vakis et al., 1988a,b; Sommer and Wurtz, 2000; Rodgers et al.,
2006). Second, it is generally considered that the activity time-
locked to the saccade is the command to initiate a saccade
(Sparks, 1975, 1986). Third, neuronal recordings made in the SC
and FEF during performance of a task in which the planning of
saccades is interrupted occasionally by the appearance of a stop
mediately before a saccade reaches a fixed threshold which when
crossed determines saccade onset (Hanes and Schall, 1996; Pare ´
and Hanes, 2003).
The results we report here (as well as those recently described
in FEF) are inconsistent with the fixed threshold hypothesis.
Whether these differences reflect difference in task properties as
was suggested for FEF (Thompson et al., 2005) remains to be
determined. A second possibility is that the activity of buildup
neurons is read-out by the saccade-related burst neurons in the
SC, which, in turn, rise in activity to a fixed threshold. Although,
no difference in the saccade-related discharge of burst and
ingtask(Pare ´ andHanes,2003).Wedidnotrecordburstneurons
and therefore cannot address this point as yet.
line of investigation suggesting that decisions based on sensory
FEF, and SC (Horwitz and Newsome, 1999, 2001; Kim and
Shadlen, 2002; Ratcliff et al., 2003, 2007). We show that the dis-
inability based on monkeys’ choices and not on differences in
sensory evidence. We also show that ROC area values or CPs
increase as saccade initiation approaches (Figs. 9, 10). This is
consistent with findings in the LIP and FEF in which evidence in
favor of a decision accumulates over time (Gold and Shadlen,
2007). When we computed discriminability in 20 ms epochs as
the saccade evolved (Fig. 10), we obtained an additional result
or elsewhere. The maximum change in CP with performance
occurred between 80 and 40 ms before saccade onset. Later than
this time, scaling appeared, but not as steeply (Fig. 10b). We
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 3005
evidence in favor of a saccade decision is a nonstationary process
The average ROC area values or CPs we obtained ranged be-
tween 0.55 and 0.70. To compare these results with those re-
ported previously we looked at CPs reported for the motion dis-
crimination task when the motion coherence was 0%. The CPs
reported in the FEF (Kim and Shadlen, 1999), LIP (Shadlen and
Newsome, 2001), and previously in SC (Horwitz and Newsome,
1999) are very similar to our findings, ranging from 0.60 to 0.75
(Gold and Shadlen, 2001). In area MT/V5 during reports of the
perception of a bistable image (Parker et al., 2002; Krug et al.,
2004), CPs averaged 0.67. These similar values suggest that the
size of the neuronal pools contributing to the judgment in all of
these tasks is likely to be similar across these varied brain areas
(Britten et al., 1996; Shadlen et al., 1996; Parker et al., 2002). In
this light, a first important and unanswered question is how SC
important question relates to how the pooled activity is then
read-out to produce the saccade. In other words, what is the
decision rule? Evidence in FEF and SC suggest that pools of neu-
rons representing a particular action can be considered individ-
ual processes racing toward a fixed threshold (Hanes and Schall,
1996; Pare ´ and Hanes, 2003; Boucher et al., 2007). Whichever
pool first crosses threshold determines the saccade akin to
winner-take-all schemes (Lee et al., 1999). Previous evidence
between two pools of neurons each representing a possible sac-
in a manner similar to a diffusion process (Ratcliff et al., 2003,
2007). Yet another possibility is that neuronal activity is pooled
(Lee et al., 1988; Groh et al., 1997; Groh, 2001). More recent
decoding schemes suggest that approaches based on likelihood
investigations using multiple neuron recording are investigating
ApterJA (1945) Projectionoftheretinaonsuperiorcolliculusofcats.JNeu-
Basso MA, Wurtz RH (1997) Modulation of neuronal activity by target un-
certainty. Nature 389:66–69.
Basso MA, Wurtz RH (1998) Modulation of neuronal activity in superior
colliculus by changes in target probability. J Neurosci 18:7519–7534.
Basso MA, Krauzlis RJ, Wurtz RH (2000) Activation and inactivation of
rostral superior colliculus neurons during smooth-pursuit eye move-
ments in monkeys. J Neurophysiol 84:892–908.
Bichot NP, Schall JD (1999) Saccade target selection in macaque during
feature and conjunction visual search. Vis Neurosci 16:81–89.
BichotNP,SchallJD (2002) Priminginmacaquefrontalcortexduringpop-
of return. J Neurosci 22:4675–4685.
BichotNP,ThompsonKG,RaoSC,SchallJD (2001) Reliabilityofmacaque
frontal eye field neurons signaling saccade targets during visual search.
J Neurosci 21:713–725.
Boucher L, Palmeri TJ, Logan GD, Schall JD (2007) Inhibitory control in
mind and brain: an interactive race model of countermanding saccades.
Psychol Rev 114:376–397.
Bradley A, Skottun BC, Ohzawa I, Sclar G, Freeman R (1987) Visual orien-
tation and spatial frequency discrimination: a comparison of single neu-
rons and behavior. J Neurophysiol 57:755–772.
Britten KH, Shadlen MN, Newsome WT, Movshon JA (1992) The analysis
of visual motion: a comparison of neuronal and psychophysical perfor-
mance. J Neurosci 12:4745–4765.
BrittenKH,NewsomeWT,ShadlenMN,CelebriniS,MovshonJA (1996) A
relationship between behavioral choice and the visual responses of neu-
rons in macaque MT. Vis Neurosci 13:87.
Carello CD, Krauzlis RJ (2004) Manipulating intent: evidence for a causal
role of the superior colliculus in target selection. Neuron 43:575–583.
Cohn TE, Green DG, Tanner WJ (1975) Receiver operating characteristic
analysis: application to the study of quantum fluctuation effects in optic
nerve of Rana pipiens. J Gen Physiol 66:583–616.
Crist CF, Yamasaki DSG, Komatsu H, Wurtz RH (1988) A grid system and
a microsyringe for single cell recording. J Neurosci Methods 26:117–122.
Deneve S, Latham PE, Pouget (1999) A Reading population codes: a neural
implementation of ideal observers. Nat Neurosci 2:740.
Ditterich J (2006) Evidence for time-variant decision making. Eur J Neuro-
Dodd JV, Krug K, Cumming BG, Parker AJ (2001) Perceptually bistable
three-dimensional figures evoke high choice probabilities in cortical area
MT. J Neurosci 21:4809–4821.
Dorris MC, Munoz DP (1998) Saccadic probability influences motor prep-
aration signals and time to saccadic initiation. J Neurosci 18:7015–7026.
Edelman JA, Keller EL (1998) Dependence on target configuration of ex-
press saccade-related activity in the primate superior colliculus. J Neuro-
Egeth HE, Yantis S (1997) Visual attention: control, representation, and
time course. Annu Rev Psychol 48:269–297.
Fuchs AF, Robinson DA (1966) A method for measuring horizontal and
vertical eye movement chronically in the monkey. J Appl Physiol
Glimcher PW, Sparks DL (1993) Representation of averaging saccades in
the superior colliculus of the monkey. Exp Brain Res 95:429–435.
Gold JI, Shadlen MN (2000) Representation of a perceptual decision in de-
veloping oculomotor commands. Nature 404:390–394.
Gold JI, Shadlen MN (2001) Neural computations that underlie decisions
about sensory stimuli. Trends Cogn Sci 5:10.
Gold JI, Shadlen MN (2002) Banburismus and the brain: decoding the re-
lationship between sensory stimuli, decisions, and reward. Neuron
Gold JI, Shadlen MN (2003) The influence of behavioral context on the
representation of a perceptual decision in developing oculomotor com-
mands. J Neurosci 23:632.
Gold JI, Shadlen MN (2007) The neural basis of decision making. Annual
Rev Neurosci 30:535–574.
Green DM, Swets JA (1966) Signal detection theory and psychophysics.
New York: Wiley.
Groh JJ (2001) Converting neural signals from place codes to rate codes.
Biol Cybern 85:159–165.
Groh JM, Born RT, Newsome WT (1997) How is a sensory map read Out?
Effects of microstimulation in visual area MT on saccades and smooth
pursuit eye movements. J Neurosci 17:4312–4330.
Hanes DP, Schall JD (1996) Neural control of voluntary movement initia-
tion. Science 274:427–430.
HanesDP,WurtzRH (2001) Interactionofthefrontaleyefieldandsuperior
colliculus for saccade generation. J Neurophysiol 85:804–815.
Hays AV, Richmond BJ, Optican LM (1982) A UNIX-based multiple pro-
cess system for real-time data acquisition and control. WESCON Conf
Horwitz GD, Newsome WT (1999) Separate signals for target selection and
Horwitz GD, Newsome WT (2001) Target selection for saccadic eye move-
ments: prelude activity in the superior colliculus during a direction-
discrimination task. J Neurophysiol 86:2548–2558.
Judge SJ, Richmond BJ, Chu FC (1980) Implantation of magnetic search
coils for measurement of eye position: an improved method. Vision Res
KeppelG (1991) Designandanalysis:aresearcher’shandbook,Ed3.Upper
Saddle River, NJ: Prentice-Hall.
KimJN,ShadlenMN (1999) Neuralcorrelatesofadecisioninthedorsolat-
eral prefrontal cortex of the macaque. Nat Neurosci 2:176–185.
Krauzlis RJ, Dill N (2002) Neural correlates of target choice for pursuit and
saccades in the primate superior colliculus. Neuron 35:355–363.
Krug K, Cumming BG, Parker AJ (2004) Comparing perceptual signals of
single V5/MT neurons in two binocular depth tasks. J Neurophysiol
the superiorcolliculus. Science
3006 • J.Neurosci.,March19,2008 • 28(12):2991–3007KimandBasso•DecisionSignalsinSuperiorColliculus
Lee C, Rohrer WH, Sparks DL (1988) Population coding of saccadic eye Download full-text
movements by neurons in the superior colliculus. Nature 332:357–360.
Lee DK, Itti L, Koch C, Braun J (1999) Attention activates winner-take-all
competition among visual filters. Nat Neurosci 2:375–381.
Li X, Basso MA (2005) Competitive stimulus interactions within single re-
sponse fields of superior colliculus neurons. J Neurosci 25:11357–11373.
Li X, Kim B, Basso MA (2006) Transient pauses in delay-period activity of
superior colliculus neurons. J Neurophysiol 95:2252–2264.
Ma WJ, Beck JM, Latham PE, Pouget (2006) A Bayesian inference with
probabilistic population codes. Nat Neurosci 9:1432.
MazurekME,RoitmanJD,DitterichJ,ShadlenMN (2003) Aroleforneural
integrators in perceptual decision making. Cereb Cortex 13:1257.
McPeek RM, Keller EL (2002) Saccade target selection in the superior col-
liculus during a visual search task. J Neurophysiol 88:2019–2034.
McPeekRM,KellerEL (2004) Deficitsinsaccadetargetselectionafterinac-
tivation of superior colliculus. Nat Neurosci 7:757–763.
MeineckeC (1989) Retinaleccentricityandthedetectionoftargets.Psychol
Moschovakis AK, Karabelas AB, Highstein SM (1988a) Structure-function
cation of efferent neurons. J Neurophysiol 60:232–262.
Moschovakis AK, Karabelas AB, Highstein SM (1988b) Structure-function
relationships in the primate superior colliculus. II. Morphological iden-
tification of presaccadic neurons. J Neurophysiol 60:263–302.
Motter BC, Simoni DA (2007) The roles of cortical image separation and
size in active visual search performance. J Vision 7:1–15.
Munoz DP, Wurtz RH (1995) Saccade-related activity in monkey superior
colliculus. I. Characteristics of burst and buildup cells. J Neurophysiol
Newsome W, Britten K, Movshon A, Shadlen MN (1989a) Single neurons
and the perception of visual motion. Neural mechanisms of visual per-
ception. In: Proceedings of the Retina Research Foundation (Lam DK,
Gilbert C, eds), pp 171–198. The Woodlands, TX: Portfolio.
Newsome WT, Britten KH, Movshon JA (1989b) Neuronal correlates of a
perceptual decision. Nature 341:52–54.
OttesFP,VanGisbergenJAM,EggermontJJ (1987) Collicularinvolvement
in a saccadic colour discrimination task. Exp Brain Res 66:465–478.
Palmer J, Verghese P, Pavel M (2000) The psychophysics of visual search.
Vision Res 40:1227–1268.
Pare ´ M, Hanes DP (2003) Controlled movement processing: Superior col-
liculus activity associated with countermanded saccades. J Neurosci
Pare ´ M, Wurtz RH (2001) Progression in neuronal processing for saccadic
Parker A, Krug K, Cumming B (2002) Neuronal activity and its links with
Parker AJ, Newsome WT (1998) Sense and the single neuron: probing the
physiology of perception. Annu Rev Neurosci 21:227–277.
Port NL, Wurtz RH (2003) Sequential activity of simultaneously recorded
Pouget A, Dayan P, Zemel RS (2003) Inference and computation with pop-
ulation codes. Annu Rev Neurosci 26:381–410.
PurushothamanG,BradleyDC (2005) Neuralpopulationcodeforfineper-
ceptual decisions in area MT. Nat Neurosci 8:99–106.
Ratcliff R, Cherian A, Segraves M (2003) A comparison of macaque behav-
ior and superior colliculus neuronal activity to predictions from models
of two choice decisions. J Neurophysiol 90:1392–1407.
RatcliffR,HasegawaYT,HasegawaRP,SmithPL,SegravesMA (2007) Dual
in a brightness-discrimination task. J Neurophysiol 97:1756–1774.
Robinson DA (1972) Eye movements evoked by collicular stimulation in
the alert monkey. Vis Res 12:1795–1808.
Rodgers CK, Munoz DP, Scott SH, Pare ´ M (2006) Discharge properties of
monkey tectoreticular neurons. J Neurophysiol 95:3502–3511.
RoitmanJD,ShadlenMN (2002) Responseofneuronsinthelateralintrapa-
rietal area during a combined visual discrimination reaction time task.
J Neurosci 22:9475–9489.
Schall JD (1995) Neural basis of saccade target selection. Rev Neurosci
Schall JD, Hanes DP (1998) Neural mechanisms of selection and control of
visually guided eye movements. Neural Netw 11:1241–1251.
Schall JD, Thompson KG (1999) Neural selection and control of visually
guided eye movements. Annu Rev Neurosci 22:241–259.
Schall JD, Hanes DP, Thompson KG, King DJ (1995) Saccade target selec-
tion in frontal eye field of macaque. I. Visual and premovement activa-
tion. J Neurosci 15:6905–6918.
Shadlen MN, Newsome WT (2001) Neural basis of a perceptual decision in
the parietal cortex (area LIP) of the rhesus monkey. J Neurophysiol
Shadlen MN, Britten KH, Newsome WT, Movshon JA (1996) A computa-
tional analysis of the relationship between neuronal and behavioral re-
sponses to visual motion. J Neurosci 16:1486–1510.
Sommer MA, Wurtz RH (2000) Composition and topographic organiza-
tion of signals sent from the frontal eye field to the superior colliculus.
J Neurophysiol 83:1979–2001.
SparksDL (1975) Responsepropertiesofeyemovement-relatedneuronsin
the monkey superior colliculus. Brain Res 90:147–152.
SparksDL (1986) Translationofsensorysignalsintocommandsforcontrol
of saccadic eye movements: role of primate superior colliculus. Physiol
Thomas NWD, Pare ´ M (2007) Temporal processing of saccade targets in
parietal cortex area LIP during visual search. J Neurophysiol 97:942–947.
ThompsonKG,HanesDP,BichotNP,SchallJD (1996) Perceptualandmo-
neurons during visual search. J Neurophysiol 76:4040–4054.
Thompson KG, Bichot NP, Sato TR (2005) Frontal eye field activity before
visual search errors reveals the integration of bottom-up and top-down
salience. J Neurophysiol 93:337–351.
Treisman A, Gelade G (1980) A feature-integration theory of attention.
Cognit Psychol 12:97–136.
UkaT,DeAngelisGC (2004) ContributionofareaMTtostereoscopicdepth
perception: choice-related response modulations reflect task strategy.
Verghese P (2001) Visual search and attention: a signal detection theory
approach. Neuron 31:524–535.
WolfeJ,O’NeillP (1998) Whyarethereeccentricityeffectsinvisualsearch?
Visual and attentional hypotheses. Percept Psychophys 60:140–156.
Wolfe JM, Horowitz TS (2004) What attributes guide the deployment of
visual attention and how do they do it? Nat Rev Neurosci 5:495–501.
Wurtz RH, Goldberg ME (1972) Activity of superior colliculus in behaving
monkey: III. Cells discharging before eye movements. J Neurophysiol
KimandBasso•DecisionSignalsinSuperiorColliculusJ.Neurosci.,March19,2008 • 28(12):2991–3007 • 3007