J Neurophysiol 103: 2458 –2469, 2010.
First published February 17, 2010; doi:10.1152/jn.00215.2009.
Supplementary Eye Field Activity Reflects a Decision Rule Governing
Smooth Pursuit but not the Decision
Shun-nan Yang,1,2 Helen Hwang,2 Joel Ford,2 and Stephen Heinen2
1
Vision Performance Institute, College of Optometry, Pacific University, Forest Grove, Oregon; and 2Smith-Kettlewell Eye Research
Institute, San Francisco, California
Submitted 11 March 2009; accepted in final form 12 February 2010
INTRODUCTION
Primates rely on learned rules to guide their decisions. Previous
research has suggested that the prefrontal cortex (PFC) plays a
critical role in this process (Miller 1999; Passingham 1985, 1993;
Wallis et al. 2001; Wise et al. 1996). Neural activity in the PFC
reflects perceptual categories better than motor choices specified
by rules (Muhammad et al. 2006; Wallis and Miller 2003) and
differentially encodes spatial or nonspatial rule-related visual cues
(White and Wise 1999). The premotor cortex (PMC) is also
involved in processing rule-related information (Muhammad et al.
2006; Wallis and Miller 2003). Neural activity in different parts of
PMC reflects better perceptual features and motor decision, respectively (Hoshii and Tanji 2006; Wallis and Miller 2003). The
different characteristics of neural activity in these two structures,
and the fact that the PFC sends output to the PMC (Barbas and
Address for reprint requests and other correspondence: Shun-nan Yang,
Vision Performance Institute, College of Optometry, Pacific University, 2043
College Way, Forest Grove, OR 97116 (E-mail: [email protected]).
2458
Pandya 1987, 1989; Pandya and Yeterian 1990), suggest a transformation of rule encoding in this complex with the PFC encoding
perceptual categories and the PMC specifying the alternative
response choices, likely based on input from the PFC.
Currently it is not clear how the rule-encoding neural signal in
the PFC and PMC is further utilized downstream to enforce a
motor decision. The supplementary eye field (SEF) is a likely
region where this process is carried out for ocular decision. The
SEF receives inputs from the PFC and PMC (Carmichael and
Price 1995; Wang et al. 2005). It also receives information from
visual structures such as MT and MST where perceptual decisions
are encoded (Britten et al. 1996; Huerta and Kaas 1990; Newsome
et al. 1989). Furthermore, it projects to the FEF (Luppino et al.
2003) and SC (Fries 1985; Shook et al. 1990) where neural
activity reflects an ocular decision (FEF: Kim and Shadlen 1999;
SC: Horwitz and Newsome 2001). The unique anatomical position of the SEF suggests a role for this structure in enforcing
ocular decision. Our recent work suggests that the SEF is involved
in rule-based decision making in a go/nogo ocular task. Human
SEF is more active when making the decision than it is for either
the same visual stimulation or smooth pursuit behavior when no
decision is required (Heinen et al. 2006) and separate populations
of neurons in monkey SEF signal go or nogo, respectively (Kim
et al. 2005). However, a problem arises with interpretation of the
previous results because in those experiments, the behavior and
the stimulus were confounded, i.e., most responses that were
made were consistent with the rule. In other words, SEF activity
in these experiments may have merely signaled the rule and hence
was not necessarily related to the ocular decision that was made.
Therefore a claim that activity here was related to the ocular
decision is premature.
The present study investigated whether the SEF encodes the
rule or enforces the ocular decision. To achieve this, monkeys
performed a delayed go/nogo ocular decision task while singleunit activity was recorded from the SEF. If SEF neurons encode
the rule rather than enforce the ocular decision, one would expect
their activity to veridically predict the rule and continue to do so
even when a decision error is committed. Conversely, if SEF
neurons participate in enforcing the ocular decision, their activity
should signal the behavior regardless if the decision is correct or
not.
METHODS
Subjects and surgical procedures
Three juvenile male Macaque monkeys (VC, ED, and LE) participated in the experiments. Two of them (VC and ED) were also
involved in an earlier study (Yang et al. 2008). Surgeries were
0022-3077/10 Copyright © 2010 The American Physiological Society
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Yang S, Hwang H, Ford J, Heinen S. Supplementary eye field
activity reflects a decision rule governing smooth pursuit but not the
decision. J Neurophysiol 103: 2458 –2469, 2010. First published
February 17, 2010; doi:10.1152/jn.00215.2009. Animals depend on
learned rules to guide their actions. Prefrontal (PFC) and premotor (PMC)
cortex of primates have been reported to display rule-related neural
activity, but it is unclear how signals encoded here are utilized to enforce
the decision to act. The supplementary eye field (SEF) is a candidate for
enforcing rule-guided ocular decisions because the activity of neurons
here is correlated with the rule in an ocular decision-making task and
because this area is anatomically more proximal to movement structures
than PFC and PMC and receives inputs from them. However, in the
previous work, the rule encoding and ocular outcome were confounded,
leaving open the question of whether SEF activity is related to the rule or
the behavior. In the present study, we attempted to discriminate between
these alternatives by increasing task difficulty and forcing errors, thereby
putting the stimulus and the behavior at odds. Single SEF neurons were
recorded while monkeys performed the task in which the rule is to pursue
a moving target if it intersects a visible square and maintain fixation if it
does not. A delay period was imposed to monitor neural activity while the
target approached the square. Two complementary populations of go and
nogo neurons were found. When task difficultly was increased, the
monkeys made more errors, and the neurons took longer to encode the
rule. However, in error trials, most neurons continued to reflect the rule
rather the monkey’s ocular decision in both the delay period and after
square intersection (movement period). This was the case for both
directionally tuned and nondirectional SEF neurons. The results suggest
that SEF neurons encode the ocular decision rule but that the decision
itself likely occurs in a different structure that sums rule information from
the SEF with information from other areas.
SEF ACTIVITY REFLECTS DECISION RULE
performed on the monkeys under aseptic conditions to implant a
recording chamber, head holder, and a search coil to measure eye
movements. With the monkey under isoflurane gas anesthesia, a 2 cm
craniotomy was trephined in the skull. The chambers implanted on the
monkeys were centered 24 mm anterior in Horsley-Clark stereotaxic
coordinates. A stainless steel recording chamber was positioned over
the craniotomy. The coil was constructed from Teflon-coated stainless-steel wire and was implanted under the conjunctiva of one eye
(Judge et al. 1980). The head holder was positioned on the midline.
After surgery, the monkey was returned to its cage and was allowed
to recover fully before experiments began. Antibiotics and analgesics
were administered under the direction of a veterinarian during the
postoperative period. All procedures were approved by the Institutional Animal Care and Use Committee and were in compliance with
the guidelines set forth in the United States Public Health Service
Guide for the Care and Use of Laboratory Animals.
A
delay period movement period
fixation point
& square
500ms
300ms
1200ms
target
target onset
square intersection
B
40°(easy nogo)
At the beginning of each recording session, the monkey was seated in
a primate chair with its head fixed by a post. The eyes were 50 cm from
the screen and the line of sight perpendicular to it. Eye position was
calibrated while the monkeys made saccades to visual targets that were
composed of a single dot with 0.5° angular extent and 10 vertical or
horizontal eccentricities. An 85–100 mm tungsten electrode that usually
had 1.0 –2.0 M⍀ impedance was lowered into the chamber via a stainless
steel tube to a predetermined site through a Crist grid (http://www.
cristinstrument.com/). The recording session typically lasted 2 h.
Monkeys were initially trained to successfully perform an easy version
of an ocular go/nogo task as described in Kim et al. (2005). For the
experiments described here, a modified version of the task was used
where task difficulty was manipulated. Figure 1A shows the temporal
schematic of the task. Each trial began with the appearance of a 0.5°
white spot at the center of the screen, surrounded by a visible 8 ⫻ 8°
square. To trigger the appearance of a moving target, also a 0.5° white
spot, the monkey had to maintain its gaze within a 4 ⫻ 4° invisible
electronic square window centered at the fixation point for 500 ms. At the
end of the fixation period, the target appeared 20° to the left or right and
moved toward the visible square at a constant velocity (30°/s) and angle
relative to horizontal (easy: 10 and 40°; difficult: 20 and 30°) for 1,200
ms. The fixation point and the square remained visible throughout the
trial. Figure 1B shows the possible trajectories in relation to the visible
square and fixation point and the defined point of square intersection.
Because of the difference in the trajectory angle, the timing of square
intersection varies as indicated in Fig. 1A. In some recording sessions
when time allowed, additional blocks of trials were conducted in which
the target trajectory angle was either 22° (extremely difficult go trial) or
25° (extremely difficult nogo trial). Again the angle and direction of
target motion was randomly determined and balanced within a block of
trials. Figure 1C shows the trajectories for extremely difficult trials with
higher constant velocity of 40°/s.
In the delay period, defined as the time between target onset and square
intersection, the monkey must maintain fixation regardless of whether the
target eventually intersects the square. Targets moving at 10, 20, and 22°
angles intersect the square (go trials), and those moving at 25, 30, and 40°
angles do not (nogo trials). In go trials, the monkey must initiate a pursuit
movement to acquire the target within 300 ms of square intersection and
maintain gaze within 3° of distance to the target until the end of the trial.
In nogo trials, the monkey must maintain fixation throughout the trial.
Because the angle and direction of target movement was randomized, the
monkey had to evaluate the target-square relationship on each trial to
determine whether it was a go or nogo condition in the delay period
(before square intersection) and enforce the ocular decision in the movement period (after intersection). Liquid reward was given at the end of a
trial only when the monkey successfully executed the task; the same
amount of liquid was given for trials of different difficulties. The intertrial
interval was set at 300 ms.
20°(difficult go)
10°(easy go)
square intersection threshold
C
25°(extreme
difficult nogo)
22°(extreme
difficult go)
square intersection threshold
FIG. 1. The delayed ocular go/nogo paradigm. A: temporal schematic. The
square and fixation point stayed on for the duration of the trial. The target
appeared 500 ms after fixation acquisition and moved at a constant angle and
velocity. The time period before square intersection is defined as the delay
period and the time period after that the movement period. B: spatial schematics of the target-square relationship. In each trial, a target appeared either
left or right 20° in the periphery and moved from the periphery with 1 of the
4 possible angles relative to horizontal meridian. The target moved in 40° (easy
nogo) and 30° (difficult nogo) and missed the square, and in others, it moved
in 10° (easy go) and 20° (difficult go) and intersected the square. C: spatial
schematic for extremely difficult trials. The target moved in higher radial
velocity (40°/s) and in 22° (extremely difficult go) and 25° angle (extremely
difficult nogo).
Data analysis
PURSUIT EYE MOVEMENTS. Vertical and horizontal eye position signals were digitized (1 kHz) and stored for off-line analysis. Eye velocity
was obtained by digital differentiation of eye position, and movement
initiation time was detected using two criteria modified from a previous
study (Badler and Heinen 2006): either the pursuit movement had a
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30°(difficult nogo)
Testing procedure and the ocular go/nogo task
J Neurophysiol • VOL
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S. YANG, H. HWANG, J. FORD, AND S. HEINEN
A
B
5°
5°
To characterize task-related activity as a
continuous function, the neural spike rate recorded in each trial was
convolved with 30 ms Gaussian. To determine if and when a neuron
signaled a rule state in the task, the difference in spike rate for
corresponding 20 ms time intervals in go and nogo trials for each
neuron was statistically tested (paired 2-tail t-test). A sliding window
moved at 5 ms steps, and the test was repeated for each step.
Separation time was defined as the beginning of the first step of the
first 20 consecutive steps in the delay period that were significantly
different, with a familywise ␣ ⱕ 0.05. When a neuron initially
displayed a higher spiking rate in go trials than in nogo trials, it was
classified as a go neuron; when activity was higher in nogo trials, it
was classified as a nogo neuron.
TASK-RELATED ACTIVITY.
square intersection
easy go
10°
correct
decision errors
10°/s
200 ms
difficult go
20°
extreme difficult
go
22°
A
D
movement initiation time
re: plate crossing (ms)
easy nogo
40°
difficult nogo
30°
350
300
250
200
150
100
50
0
B 100
percentage (%)
extreme difficult
nogo
25°
FIG. 2. Eye movements recorded from example blocks of ocular go/nogo
trials. Blue traces indicate success trials and red traces decision errors. A: eye
position traces in easy, difficult, and extremely difficult go trials. Catch-up
saccades were removed from the velocity traces. B: corresponding radial eye
velocity traces in easy, difficult, and extremely difficulty go trials shown
above. The small spikes shown before square intersection are microsaccades that did not bring the gaze outside of the 4 ⫻ 4° fixation window.
C: eye position traces for nogo trials. D: radial eye velocity traces in nogo
trials.
Four types of responses were defined in the
present study: go success, go error, nogo success, and nogo errors. Go
success trials had to satisfy two criteria: the animal had to maintain
fixation within a 4° fixation window during the delay period and the
target had to be acquired and continuously pursued within the 3°
window in the movement period. Nogo success trials were defined
as continuous fixation within the 4° fixation window for the
duration of the trial. Go errors were defined as maintaining fixation
throughout a go trial, whereas in nogo errors a pursuit movement
was initiated after square intersection.
BEHAVIORAL CHOICES.
J Neurophysiol • VOL
ext. diff
correct response
decision error
60
40
20
easy
difficult ext. diff
go trials
100
percentage (%)
minimum velocity of 10°/s for ⱖ250 ms or it surpassed the saccade
threshold of 40°/s for 80 ms and was followed by a pursuit
movement. Matlab and its Signal Processing and Statistical Toolboxes (The Mathworks 2006) were used to conduct signal processing and data analysis.
difficult
task difficulty
80
0
C
easy
80
60
40
20
0
easy
difficult ext. diff
nogo trials
FIG. 3. Behavioral outcomes in ocular no/nogo obtained from all 3 monkeys. A: movement initiation time relative to square intersection in all go
success trials. Error bars indicate SEs. B: percentages of successes and errors
in go and nogo trials and their SEs. Not shown here are the percentages of trials
where the monkey failed to acquire the fixation point or initiated the eye
movement prematurely.
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C
To determine whether SEF activity reflected the rule or was signaling the direction of target trajectory or
DIRECTIONALITY INDEX.
SEF ACTIVITY REFLECTS DECISION RULE
planned pursuit movement, a directionality index (DI) was calculated
for each neuron:
␮pref ⫺ ␮opp
DI⫽ ␮pref ⫹ ␮opp
冏
Ze⫽
冏
Histology
To determine whether the activity of a taskrelated neuron predicted the eventual ocular decision, we calculated
the choice probability (CP) of its activity at different time intervals in
the delay period. CP is adopted from the signal detection theory and
is used to specify the degree to which single neurons reflect the
animal’s decision (Britten et al. 1996). CP was derived by first
compiling a distribution of neuronal activity for each of the choices
that the animal made. A receiver operating characteristic (ROC) curve
was then computed from the two distributions, and the area under the
curve was integrated to yield the CP. CP is therefore an estimate of the
probability that an observer would correctly predict whether the
monkey pursued or fixated on a given trial from the neuron’s firing
rate. Note, however, that because different target trajectories were
used to specify go or nogo trials, activity of a go or nogo neuron could
also reflect the trajectory in addition to the rule. Although the neural
activity was sorted by the behavioral choice but not the trajectory, the
CP could still partially reflect target trajectory itself.
RESULTS
Behavioral choice
Four types of responses were defined according to the
go/nogo rule: go success, go error, nogo success, and nogo
error (see METHODS). Response errors due to premature eye
movements or failure to achieve initial fixation were excluded
from the analysis (2.8% of trials). Figure 2 shows typical eye
traces from a single block of trials in which easy and difficult
trials were randomly interleaved, and another block of extremely difficult trials. Shown in Fig. 2A are Cartesian eye
position traces for easy (top), difficult (middle), and extremely
NORMALIZED MEAN SPIKE RATE. To compare neural activity in
trials with different behavioral choices (go success, nogo success, and
go error), the mean spike rate for each neuron in go error trials in the
delay period was first computed separately and then normalized
against that for success go and nogo trials respectively using the
following equation
Monkey VC
anterior
Anterior
6
4
2
left
SEF
Ar
c
right
Posterior
0
-4
-6
B
1 cm
-2
go neuron
nogo neuron
-6
-4
Left
-2
0
2
4
6
Evoked saccade site
Right
0
-1
FIG. 4. Locations and depths of rule-related neurons and
evoked saccade sites. The symbols indicate sites from which go
(F) and nogo (E) neurons were recorded. }, evoked saccade
sites were located with 50 –75 ␮A current. A: topographic map
of recorded sites from monkey VC. The center of the map
corresponds to the chamber center as indicated in the inset in
the upright corner, which was centered 24 mm anterior and on
the midline. Symbols are offset slightly if they were recorded
from the same electrode track. Positive x and y axis values
indicate anterior and right locations, and negative values indicate posterior and left. B: horizontal positions and measured
depths of recording sites obtained from monkey VC. The coronal schematic of probed sites is based on a reference histological slice close to the center of the supplementary eye field
(SEF) chamber.
-2
-3
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After completing the experiments, the monkeys were perfused, and
the brains were fixed with 10% formalin. A block of cortical tissue
encompassing the probed area was taken from monkey VC, and 5 ␮m
thick coronal slices were obtained from the SEF tissue block and
stained (Hematoxylin-and-eosin, or HandE, and Perl) to highlight the
electrode tracks. In addition, before perfusing monkey VC, two electrical lesions were made with DC current located 2 mm laterally from
the center of the chamber and 4.5 mm in depth. After perfusion, the
reference lesions were revealed using cresyl violet staining. The
recorded site depth and locations in experimental sessions were then
aligned to these reference tracks.
CHOICE PROBABILITY.
Depth (mm)
2(␮e⫺␮b)
⫺1
(␮s⫺␮b)
Here Ze is the normalized mean spike rate for decision error trials.
The mean spike rate for go success, nogo success, and go error trials
are ␮s, ␮b, and ␮e, respectively. The normalized mean spike rate is ⫺1
when the mean spike rate in go error trials is the same as that in nogo
success trials and 1 when it is the same as that in go success trials. A
value ⬎1 indicates the firing rate in go error trials was higher than that
in success go trials. This was not done for nogo error trials because
they were infrequent.
Here DI is for an individual neuron. The mean spike rate for the
preferred direction, or ␮pref, was derived from the pair of trajectories
that specify the same rule and have the highest pooled mean spike rate
compared with other eight pairs of trajectories; the mean spike rate for
the two opposite trajectories was noted as ␮opp. It also removes any
rule-related influence on the spike rate, as the opposite pairs always
have the same rule state. The resultant DI value is between 0 and 1.
The activity of a neuron is spatially tuned if the DI value is significantly different from zero. DI values were calculated for the delay and
movement periods respectively for each neuron.
A
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S. YANG, H. HWANG, J. FORD, AND S. HEINEN
A
B
go neuron
longer in difficult go trials than in easy trials but was not longer
in extremely difficult go trials than in difficult trials. Here only
successful go trials were included. Figure 3B shows the percentages of decision success and error trials and the corresponding SEs. Not shown here are the percentages of other
errors unrelated to decision making, such as failure to acquire the
fixation points or premature eye movements, that can be inferred
based on the percentages shown here. It can be seen that the
frequency of go errors increased with task difficulty level (for all
3 difficulty levels: ␹2 ⫽ 3452, P ⬍ 0.001), and there were also
more nogo errors for more difficult trials (␹2 ⫽ 312, P ⬍ 0.001).
In addition, there were more errors in difficult go than in difficult
nogo trials (␹2 ⫽ 623, P ⬍ 0.0001), and more errors in extremely
difficult go than extremely difficult nogo (␹2 ⫽ 1067, P ⬍
0.0001). Therefore our manipulation successfully heightened task
difficulty and resulted in more decision errors.
Effects of task difficulty on SEF activity
Of 149 neurons recorded from the SEF of the three monkeys, 44 were classified as rule-related because they displayed
differential activity between easy go and nogo trials in the
no go neuron
spike rate (sp/s)
easy trials
square intersection
time
30
30
20
20
10
10
0
0
400
800
1200
190
C
0
0
D
400
800
1200
175
spike rate (sp/s)
difficult trials
30
30
20
20
10
10
0
0
400
800
1200
300
E
0
0
400
800
1200
800
1200
FIG. 5. Neural activity recorded from example go and nogo
neurons in easy, difficult, and extremely difficult trials. Each curve
represents the mean spike rate for targets of a specific motion
trajectory (green: go; red: nogo). Same-color dashed lines indicate
corresponding square intersection times. The arrows below the
panels indicate the separation time for all go and nogo trials with
the mean spike rate of all trajectories pooled. A: mean spike rates
of a typical go neuron in easy go and nogo trials. B: mean spike
rates of a typical nogo neuron in easy trials. C: mean spike rates of
the same go neuron in difficult trials. D: mean spike rates of the
same nogo neuron in difficult trials. E: mean spike rates of a go
neuron in extremely difficult trials. F: mean spike rates of a nogo
neuron in extremely difficult trials.
475
F
spike rate (sp/s)
extreme difficult trials
30
30
20
20
10
10
0
0
400
800
1200
0
0
400
time re: to target onset (ms)
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difficult go trials. In easy trials, the monkey usually acquired
the target close to where it intersected the square, and go errors
were rare. In difficult trials, the monkey at times acquired the
target further away from the intersection point, and go errors
were more frequent. In extremely difficult go trials, the target
was acquired consistently further away from the intersection
point, and more errors occurred. Figure 2B shows radial velocity traces for the same trials, aligned to the time of square
intersection. Here it can be seen that movement initiation time was
closer to that of square intersection in easy trials but was later in
difficult and extremely difficult trials. Figure 2C shows eye
position traces from easy, difficult, and extremely difficult nogo
trials. The monkey successfully maintained fixation in all
easy trials and made few errors in difficult trials. More
errors were made in extremely difficult trials. Figure 2D
shows radial eye velocity for the nogo trials. Eye movements in nogo error trials appear similar to those in successful difficult and extremely difficult go trials in their
timing and initial intersection location.
Figure 3 summarizes behavioral results from all recording
sessions for the three monkeys. Figure 3A shows that overall
the mean pursuit latency relative to square intersection was
SEF ACTIVITY REFLECTS DECISION RULE
J Neurophysiol • VOL
cumulative percentile (%)
square intersection time
100
80
60
40
go easy
nogo easy
go difficult
nogo difficult
20
0
0
200
400
600
800
1000
separation time re: to target onset (ms)
FIG. 6. Cumulative curves of separation times for all neurons in easy and
difficult trials (thick solid: go neurons in easy trial; thick dashed: nogo neurons
in easy trials; thin solid: go neurons in difficult trials; thin dashed: nogo
neurons in difficult trials). The corresponding vertical lines indicate the timing
of square intersection. Arrows at bottom indicate the median separation times
for corresponding conditions. Note that most neurons encoded go/nogo activity
before square intersection even in difficult trials. Results from extremely
difficult trials are not shown here because all but 1 neuron had a separation
before square intersection.
selectivity in the movement period than in delay period. To
achieve this, the mean spike rate for each trajectory in these two
periods was computed for success trials. Figure 7A, left, shows
results from an example go neuron for which all go trajectories
had a similarly higher spike rate than nogo trajectories in the delay
period. The pair of trajectories having the highest spike rate was
marked as the “pref” trajectory, and the opposite ones the “opp”
trajectory; the mean spike rate for all trajectories was normalized
against the trajectory with the highest mean spike rate (green: go
trials; red: nogo trials). The arrows indicate the motion direction.
Figure 7A, right, shows the mean spike rate in the movement
period for the same neuron. There was consistently higher activity
for some go trajectories compared with all nogo trajectories.
Figure 7B, left, shows the mean spike rates in the delay period for
another example, a nogo neuron, for which there was significantly
higher activity for leftward and downward nogo trajectories than
other nogo trajectories in the delay period. However, activity for
most nogo trajectories was still higher than that for adjacent go
trajectories. In the movement period, as shown in Fig. 7B, right,
a similar directional selectivity and rule-related difference is
shown, although one of the opp trajectories now had a higher
mean spike rate.
To assess the spatial selectivity of SEF neurons as a
whole in the delay and movement period, in Fig. 7, C and D,
we plot histograms of the DI for individual neurons (see
METHODS). Figure 7C summarizes the DI value in standard
go/nogo trials (easy and difficult) for all 44 task-related
neurons. Neurons having a DI value significantly ⬎0.0 as
determined by the 95% confidence interval of the DI value,
are considered directionally selective and marked by lighter
symbols. During the delay period, as shown in the left panel,
a good number of task-related SEF neurons (14 of 34 go
neurons; 2 of 10 nogo neurons) are not directionally tuned in
the delay period. For those that were, 48% of them displayed
higher contralateral spike rate (12 of 20 for go neurons, 1 of
8 for nogo neurons); four neurons showed a higher ipsilat-
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delay period (see METHODS). Of these, 34 displayed higher
activity in go trials (go neurons), whereas 10 did so in nogo
trials (nogo neurons). Figure 4 summarizes the location of
task-related neurons and evoked saccade sites obtained from
monkey VC from which a large portion of neurons were recorded. Figure 4A shows the topographic location of neurons and
sites where eye movements were evoked at low currents (50 –75
␮A) plotted relative to the center of the recording chamber (see
histology analysis in METHODS). The inset shows the location of the
recording chamber. Most task-related neurons were located 2–5
mm from the midline and 21–28 mm AP in Horsley-Clarke
stereotaxic coordinates. Figure 4B shows the depth of the same
sites. Most sites were within 2.5 mm of the cortical surface.
Therefore the recorded neurons were mostly located in the SEF.
Figure 5 shows the activity of typical go and nogo neurons
in easy, difficult, and extremely difficult trials with correct
behavioral responses. The mean spike rate of the cells for each
target trajectory in the delay and movement periods is displayed separately. Figure 5A shows activity from a go neuron
in easy trials. Shortly after target onset, go trial activity rose
above nogo trial activity, peaking just before square intersection. Note that the traces segregate to reflect whether the trial
type is go or nogo rather than for a set of trajectories that have
the same general motion directions. In difficult go trials (Fig.
5C), neural activity appears to rise more slowly and the
difference between it and activity in nogo trials appears less
than that observed in easy trials. Furthermore, the separation
time, the time that the difference in activity level between go
and nogo trials first reached significance (see METHODS), was
later for difficult than for easy trials (easy, 190 ms; difficult,
300 ms). Figure 5, B and D, shows a typical nogo neuron
exhibiting the necessary criterion of greater activity in nogo trials
than in go trials. For this cell, the separation time was also later in
difficult trials than in easy ones (easy, 175 ms; difficult, 470 ms).
Figure 5, E and F, shows results from a block of extremely
difficult trials from other go and nogo neurons. It can be seen that
there is little separation between go and nogo trials, and no
preintersection separation in mean spike rate was observed.
In Fig. 6 we summarize the separation times for all rule-related
neurons by plotting their cumulative percentile functions for easy
go (thick solid), easy nogo (dashed), difficult go (thin solid), and
difficult nogo trials (thin dashed), respectively. Results from
extremely difficult trials were not plotted here because of a lack of
separation in the delay period for all but one neuron. Median
separation times are indicated with arrows. The figure shows
separation times in difficult trials were later than those in easy
ones as shown by the results of Mann-Whitney test (go neurons:
W ⫽ 612, P ⬍ 0.0001; nogo neurons: W ⫽ 52, P ⫽ 0.0058). Note
that several neurons, which had a separation in mean spike rate in
go and nogo trials, did not have significantly different activity at
any time in the delay period in difficult go and nogo trials (go
neuron separations: easy ⫽ 39, difficult ⫽ 27; nogo neuron
separations: easy ⫽ 14, difficult ⫽ 11). The later separation times
observed in difficult trials are consistent with the later pursuit
onsets and more decision errors observed in the behavior in these
trials (see Figs. 2 and 3).
To examine whether these go/nogo-related neurons are movement and fixation neurons and have differential involvement in
the delay and movement periods, the directionality of their activity
in these two periods was analyzed. A neuron is more likely to be
involved in enforcing the ocular decision if it had greater spatial
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S. YANG, H. HWANG, J. FORD, AND S. HEINEN
standard go/nogo
A
delay period
90
1
120
movement period
60
30
pref
op
0
180
ref
opp
210
330
non-directional
0.5
150
300
240
270
B
pref
opp
pref
standard go/nogo
C
10
14
go/nondir
nogo/nondir 12
go/dir
nogo/dir
10
8
8
6
6
4
4
14
# of neurons
12
2
2
0
0
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
0.4
0.6
0.8
1
extreme difficult
D
# of neurons
movement period
delay period
FIG. 7. Directionality of SEF activity. A: mean spike rates
for all target trajectories in the delay period (left) and in the
movement period (right) for an example go neurons that were
not directionally tuned. B: mean spike rates for all trajectories
for a directionally tuned nogo neuron. C: directionality index
(DI) values for all neurons in standard go/nogo trials the delay
(left) and movement periods (right). D: DI values for all
neurons in the extremely difficult go/nogo trials.
6
6
4
4
2
2
0
0
0
0.2
0.4
0.6
0.8
1
0
0.2
directionality index
eral spike rate in the delay period (3 go neurons and 1 no go
neuron). During the movement period, as shown in the right
panel, a greater portion of go (18/34) and nogo (8/10)
neurons were not directionally tuned. Figure 7D shows
results for extremely difficult go/nogo trials. The portion of
directionally tuned neurons was ⬍50% (6/15) in the delay
period (left), and only increased slightly (7/15) in the movement period (right).
To assess whether the differential activity for rule-related
neurons in go and nogo trials predicted the actual behavioral
choices, we computed the CP for individual neurons as has
been done previously with neurons implicated in perceptual
decision making (Purushothaman and Bradley 2005). If a
neuron has a CP of 1.0, it perfectly signals the animal’s
behavioral decision. Note that although the CP value mainly
reflects the ocular decision, it is also likely affected by other
J Neurophysiol • VOL
factors such as target trajectory (see METHODS). Figure 8 shows
the quantitative derivation of CP for an example go neuron,
and the population results of CP over the delay period. In Fig.
8A the frequency distributions of mean spike rate in a 25 ms
interval for individual trials in a single block of trials are
displayed, sorted by the subsequent behavioral choice. The pursuit
and fixation distributions are analogous to “signal⫹noise” and
“noise” distributions in the signal detection theory from which
this analysis is derived (Green and Swets 1966). When a
decision criterion (vertical line) is set at a given spike rate, the
proportion of pursuits to the right of the criterion spike rate is
correctly predicted by the spike rate. Shifting the criterion
further left results in an increasing number of fixations classified incorrectly as pursuits. The ROC functions (Fig. 8B) are
derived by plotting the proportion of correct pursuits against
the proportion of incorrect fixations predicted by different
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directional
opp
SEF ACTIVITY REFLECTS DECISION RULE
A
2465
B
15
10
5
0
15
10
5
0
C
neuron count
neuron count
neuron count
14
12
10
8
6
4
2
0
0
8
6
4
2
0
0
1
difficult
classified
pursuit
extreme difficult
.8
0.93 (easy)
0.67(difficult)
.6
0.54 (ext. difficult)
.4
.2
0
0
easy
10
8
6
4
2
0
1
0
difficult
14
12
10
8
6
4
2
0
1
0
1
.8
movement period
go - nondir
nogo - nondir
go - dir
nogo -dir
.6
.8
.2 .4
.6
.8
.6
extreme difficult
8
6
4
2
0
0 .2
.8 1
.4
.6
false alarm rate
(fixations)
.2 .4
.2
.4
.2
10 20 30 40
spike rate (sp/s)
delay period
10
8
6
4
2
0
0
choice probability
pursuit
fixation
.2 .4
.6
.8
1
.2 .4
.6
.8
1
.6
.8
1
.4
FIG. 8. Choice probability for SEF neurons. A: the frequency distributions of mean spike rate in a 25 ms interval for
individual trials obtained from a go neuron, sorted by the
subsequent behavioral choice. Results from easy, difficulty and
extremely difficult trials were plotted separately. B: the receiver
operating characteristics (ROC) functions derived by plotting
the proportion of correct pursuits against the proportion of
incorrect pursuits predicted by different criterion spike rates,
and the area under each curve yields the choice probability
(CP). C: CP in easy, difficult, and extremely difficult trials in
the delay period. D: CP results in the movement period.
choice probablity
criterion spike rates, and the area under each curve yields the
CP. For this example neuron, CP had a relatively high value of
0.93 for easy trials. For difficult trials, however, its CP decreased (W ⫽ 721, n ⫽ 29, P ⬍ 0.001).
Figure 8C, left, are the CP values in the delay period for all
neurons in easy, difficult and extremely difficult trials. Directional neurons were marked with light-colored symbols. Here it
can be seen that the CP values in the delay period decreased
when task difficulty was increased (easy: 0.70; difficult: 0.59,
P ⬍ 0.05). Directionally tuned (0.67) and nondirectional neurons (0.75). In the right panel are the CP values for the
movement period. The change in CP value in relation to task
difficulty was not apparent (easy ⫽ 0.65; difficult ⫽ 0.64), and
there is even higher CP values for directional neurons (0.67)
J Neurophysiol • VOL
than for nondirectional ones (0.58, P ⬍ 0.01). In addition, for
easy trials, the CP value was higher in the delay period (0.68)
than in the movement period (0.65, P ⬍ 0.05). That CP, an
indicator of whether a neuron agrees with the animal’s ocular
decision, was higher in the delay period and decreased in the
difficult trials, suggests that these cells are not directly involved in enforcing the ocular decision. The lack of a relationship between directionality and CP value further refute a direct
role of these SEF neurons in specifying the ocular response.
SEF activity and decision making
To directly test whether these SEF neurons were veridically
encoding the rule state or enforcing the decision, we compared
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15
10
5
0
0
easy
hit rate (pursuits)
trial count
by behavior
2466
S. YANG, H. HWANG, J. FORD, AND S. HEINEN
DISCUSSION
The present study investigated whether the SEF is involved
in encoding the rule of an ocular go/nogo task or in enforcing
the ocular decision. We found that the activity of individual
SEF neurons reflected pursuit latency and the difficulty of the
ocular decision task. Furthermore, in both the delay and movement periods, the activity reflected whether a given trajectory
A
12
delay period
mean spike rate (sp/s)
16
mean spike rate (sp/s)
B
go neuron
go success
go error
nogo success
8
4
0
0
200
400
600
800
nogo neuron
square intersection time
16
go success
go error
nogo success
12
8
4
0
0
200
time re: target onset (ms)
25
go - nondir
nogo - nondir
go - dir
nogo -dir
5
0
-4
-2
0
2
4
normalized spike rate
600
800
6
movement period
25
20
15
10
5
0
-10
consistent
with rule
10
ranked cell number
15
consistent with
decision
20
consistent
with rule
ranked cell number
D
delay period
go
nogo
success success
400
time re: target onset (ms)
consistent with
decision
C
specified go or nogo but not the decision of the animal, i.e., to
fixate or pursue. Many of these go and nogo neurons lacked
spatial selectivity in both the delay and movement periods. The
spatially selective neurons were no more predictive of the ocular
decision than were the nonselective ones. Therefore the SEF
appears to play a similar role to the PMC in signaling a ruleconforming response and does not enforce the ocular decision
itself.
Given that PMC and SEF activity both suggest the ruleconforming choice, what different function could the SEF
perform relative to the PMC? Because the SEF receives inputs
from the PMC, the rule-encoding signal from the PMC might
be further transformed in the SEF to suggest a response more
specific to the oculomotor system. Previous research has
shown that microstimulation of the PMC evokes forelimb
movements as well eye movements (Fujii et al. 1998, 2002),
whereas SEF stimulation only affects saccades and pursuit
(Park et al. 2006; Tehovnik 1995; Tian and Lynch 1995). Other
studies have demonstrated that the PMC is involved in planning movement sequences (Nakayama et al. 2008), whereas the
SEF is more active in specifying a single movement when a
movement sequence is performed (Isoda and Tanji 2002,
2003). Therefore the PMC could help specify a complex
multimodal movement and/or sequential responses, whereas
the signal in the SEF might be specific to a single, pending
oculomotor response.
If the SEF does not make the ocular decision, where is the
decision made? The FEF is certainly a candidate structure. This
region is involved in initiating voluntary saccadic and pursuit
eye movements (Gottlieb et al. 1993; Schall 1991a), and
lesions here impair saccade and pursuit initiation (Guitton et al.
0
10
20
normalized spike rate
J Neurophysiol • VOL
103 • MAY 2010 •
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30
FIG. 9. Neural activity in success and decision error trials. A: spike rate in go error
(dashed green), go success (green), and nogo
success (red) trials recorded from a go neuron in difficult trials. The vertical line indicates the corresponding square intersection
times for go and nogo trials, and the black
arrow indicates the separation time for go
decision error and nogo success trials. The
shaded area indicates the delay period with
which the mean spike rate of these types of
trials is compared in Fig. 8C. B: spike rate in
success and go errors trials recorded from a
nogo neuron in difficult trials. C: the mean
spike rate of go and nogo neurons in error
trials in the delay period that was normalized
against that in go-success and nogo-success
trials in the delay period (see METHODS). Bars
pointing to the right indicate that the spike
rate is more similar to that in go success
trials, thus consistent with the rule; leftpointing bars indicate that the activity is
consistent with the behavioral choice. The
dashed vertical lines indicate the normalized
go rate for success go (green) and nogo (red)
trials. D: the normalized mean spike rate of
go and nogo neurons in error trials in the
movement period.
Downloaded from http://jn.physiology.org/ by 10.220.33.1 on July 28, 2017
their activity in go error trials (failure to pursue in go trials) to
that in go success trials. Because there were very few go errors
in easy trials and only few neurons with extremely difficult
results, only results from difficult trials were analyzed. Nogo
errors were not analyzed also because of their low frequency.
Figure 9A shows the mean activity of a typical go neuron in go
success, nogo success, and go decision error trials recorded in
a block of difficult trials. Here the activity in go error trials had
a similar profile to that observed in go success trials. Figure 9B
shows the result obtained from a nogo neuron. The activity in
go error trials was similar to that in go success trials as well.
Figure 9C shows the spike rate of individual go and nogo neurons in
go error trials normalized relative to go and nogo successful trials
during the delay period in difficult trials (see METHODS). The direction
of the bars indicates whether the activity in error trials is better
predicted by the rule or the behavioral choice. A value of ⫺1
or 1 indicates the mean spike rate is the same as that of success
nogo and go trials, respectively. Only neurons with a minimum
of 10 error trials in difficult trials are shown. Here it can be
seen that the activity of most neurons reflect the rule better than
the behavior. Figure 9D shows the results in the movement
period, revealing a similar tendency of veridically signaling the
rule rather than the ocular decision.
SEF ACTIVITY REFLECTS DECISION RULE
J Neurophysiol • VOL
even when the cue was displayed at a location inconsistent
with the corresponding target location for saccade. Also at
odds with our results, Tremblay et al. (2002) found that SEF
activity did not reflect the rule; rather the activity was tuned
to the position of the selected target relative to that of the
other target, but not its position in the visual field. These
results suggest that SEF neurons can specify the target
location of the intended saccade relative to competing distractors rather than simply indicating the location of a visual
cue. Unlike in these previous studies, the current results and
that of an earlier study (Kim et al. 2005) demonstrated that
the spatial selectivity of SEF neurons did not predict the
specifics of motor responses in a task where the rule does
not specify a relationship between perceptual categories
(intersect or miss the square) and a specific motor response
(pursue a leftward or rightward moving target).
We do not believe that the contrasting observations of SEF
spatial selectivity during rule-based ocular decisions suggest a
fundamental difference in SEF function. Instead they may
reflect that different types of SEF neurons were recorded from,
and/or the different tasks were performed in these studies. In
the earlier work, each potential target had a specific location
relative to other competing targets, which might have recruited
only SEF neurons capable of spatial tuning. In contrast, in our
task a go response is not specified by a target’s location in
space, rather it is guided by the abstract rule that the target
must intersect a square for the response to be required. Therefore populations of spatially selective and nonselective SEF
neurons may be both recruited to signal go and nogo ruleconforming responses.
To conclude, in the cascading process of oculomotor decision
making, the SEF appears to be a stage where rule-conforming
go-nogo ocular responses are specified; however, it is unlikely
directly involved in making the ocular decision. Other downstream areas, that likely include the FEF, might act on the SEF
signal and decide on the ocular response instead. Different areas
displaying rule-encoding activity therefore might serve as functionally distinctive stages to transform abstract rules into executable motor commands. The current results highlight a way forward in understanding these processes.
GRANTS
The studies described here are supported by National Eye Institute Grant
EY-117720 to S. Heinen and by a R. C. Atkinson Fellowship at SmithKettlewell Eye Research Institute.
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