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The Journal of Neuroscience, January 15, 2003, 23(2):632-651
The Influence of Behavioral Context on the Representation of a
Perceptual Decision in Developing Oculomotor Commands
Joshua I.
Gold1 and
Michael N.
Shadlen2
1 Department of Neuroscience, University of
Pennsylvania, Philadelphia, Pennsylvania 19104-6074, and
2 Department of Physiology and Biophysics, National Primate
Research Center and Howard Hughes Medical Institute, University
of Washington, Seattle, Washington 98195-7290
 |
ABSTRACT |
To make decisions about sensory stimuli, the brain must weigh the
evidence that supports or opposes the alternative interpretations. In
the present study, we evaluated the hypothesis that a quantity reflecting the weight of sensory evidence is represented in brain circuits responsible for the behavioral response used to indicate the
decision. We trained monkeys to decide the direction of random-dot motion and to indicate their decision with an eye movement to one of
two choice targets. We interrupted decision formation with electrical
microstimulation of the frontal eye field, causing an evoked eye
movement that is influenced by ongoing oculomotor activity. For the
"pro-saccade" version of the task, in which the correct target was
at a known location in the direction of motion, the
microstimulus-evoked eye movement reflected both the impending
pro-saccadic response and the temporal accumulation of motion
information used to select that response. In contrast, for the
"colored-target" task, in which the correct target was of a
particular color but at an unpredictable location, little ongoing
oculomotor activity was evident. The results suggest that formation of
the decision and formation of the behavioral response share a common
level of neural organization, but only when the decision is associated
with a specific, predictable movement.
Key words:
electrical microstimulation; saccade; visual
motion; perceptual decision; sensory-motor association; cortex
 |
Introduction |
Higher brain function provides for
flexible associations between sensory information and behavior. Central
to this flexibility is the ability to make decisions about the presence
or identity of a stimulus to guide the appropriate action. Recent
studies have demonstrated neural correlates of perceptual decisions in association and motor cortices, which contain neurons that appear to
represent the transformation of sensory information into the preparation for action (Glimcher, 2001
; Gold and
Shadlen, 2001
; Romo and Salinas, 2001
;
Schall, 2001
). In the present study, we examined how the
representations of these transformations depend on the particular
sensory-motor association.
For many perceptual tasks, decisions about sensory stimuli instruct
particular courses of action, as when detection of a stimulus is
indicated with an eye or hand movement to the location of the stimulus.
For a variety of these sensory-motor associations, neurons that respond
selectively in anticipation of a particular movement also represent
sensory information that encodes the instruction (Taira et al.,
1990
; Boussaoud and Wise, 1993
; di
Pellegrino and Wise, 1993
; Chen and Wise, 1995a
,
1995b
; Kalaska and
Crammond, 1995
; Murata et al., 1996
,
1997
; Rizzolatti et al.,
1996
; Shen and Alexander, 1997
; Zhang et
al., 1997
; Fadiga et al., 2000
). For example,
neurons in various oculomotor areas, including the lateral
intraparietal area (LIP), the frontal eye field (FEF), and the superior
colliculus (SC), signal both the preparation of a particular eye
movement and the visual cue that instructs the movement (Gnadt
et al., 1991
; Glimcher and Sparks, 1992
;
Schall and Bichot, 1998
; Colby and Goldberg,
1999
). In monkeys trained to decide the direction of random-dot
motion and to indicate their decision with an eye movement in the
perceived direction (see Fig.
1A), these neurons
represent the transformation of motion information into a decision to
make the appropriate eye-movement response (Horwitz and Newsome,
1999
; Kim and Shadlen, 1999
; Shadlen and
Newsome, 2001
; Roitman and Shadlen, 2002
).

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Figure 1.
Temporal sequence of events for the
direction-discrimination tasks. A, Pro-saccade task. After
the monkey fixated, two red choice targets appeared 8°
from the fixation point and along the axis of motion. The motion
stimulus appeared after a 100-600 msec delay and remained on for
100-1500 msec, until fixation-point offset. The monkey then indicated
its direction decision with an eye movement to one of the two targets
and was rewarded for choosing the target in the direction of motion.
B, Colored-target task. After the monkey fixated, the motion
stimulus appeared after 100-600 msec and remained on for 100-1500
msec, until fixation-point offset. The two targets, one red
and one green, then appeared at random locations on an
imaginary circle with a radius of 8°, centered on the fixation point.
The monkey indicated its direction decision with an eye movement to one
of the two targets and was rewarded if motion was to the left and it
chose the green target or if motion was to the right and it
chose the red target. C, Anti-saccade task: like
the pro-saccade task, except that the monkey was rewarded for choosing
the target opposite the direction of motion.
|
|
We recently demonstrated the close link between formation of the
decision and formation of the behavioral response for the direction-discrimination task (Gold and Shadlen, 2000
).
We interrupted motion viewing with electrical microstimulation of the
FEF, resulting in evoked eye movements that deviated in the direction
of the subsequently selected target. The oculomotor signals responsible for these deviations reflected the accumulated motion information that
informed the monkey's direction decision.
In the present study, we used the same microstimulation technique to
assess how this decision-related oculomotor activity depends on the
association between the decision and the eye-movement response. We used
several tasks, each of which required the monkey to form a direction
decision by accumulating motion information over time but had different
associations between the decision and the response. We found that the
monkey's evolving direction decision was evident in oculomotor
commands only when the monkey could anticipate the particular eye
movement needed to indicate the decision. The results support the idea
that when a decision about a sensory stimulus calls for a specific
behavioral response, the decision is formed as a direct transformation
of sensory information into the commands that generate the response.
 |
Materials and Methods |
We used two adult male rhesus monkeys (Macaca
mulatta). All training, surgery, and experimental procedures were
in accordance with the National Institutes of Health Guide for
the Care and Use of Laboratory Animals and were approved by the
University of Washington Animal Care Committee.
Behavioral tasks. The monkeys were trained to discriminate
the direction of motion on a one-interval, two-alternative
forced-choice task. The display was generated in MATLAB on a Macintosh
computer, using the Psychophysics Toolbox extensions (Brainard,
1997
; Pelli, 1997
) and our own software to draw
the motion stimulus. The motion stimulus was a random-dot kinetogram
(Britten et al., 1992
) contained within a circular
aperture 5° in diameter and centered on the fixation point. The dots
were white squares, 0.069° per side (two pixels at a screen
resolution of 1024 × 768 on a 21 inch monitor positioned 60 cm
from the monkey's eyes) and 19.2 cd/m2 in
luminance, viewed on a black background. The dots were plotted in three
interleaved sets of equal size. Each set was plotted in one of three
successive video frames (at 75 Hz frame rate) and shown for just a
single frame. Three frames (40 msec) later, a fraction of the dots from
that set was plotted at a displacement of 0.2° to promote the
perception of motion (at 5° s
1); the remainder
of the dots were replotted at random locations. Together, the three
sets produced a dot density of 16.7 dots per degree squared per
second. The percentage of coherently moving dots (0, 3.2, 6.4, 12.8, 25.6, and 51.2%), viewing duration (100-1500 msec), and
direction (two alternatives separated by 180° for a given experiment)
were randomly interleaved from trial to trial. The distribution of
viewing durations was chosen to approximate a flat hazard function
(Luce, 1986
): 100 msec plus a random time chosen from an
exponential distribution with mean 300 msec. This strategy minimized
the monkey's ability to anticipate the end of the trial and thus
maximized our ability to "interrupt" motion viewing unexpectedly
either with motion offset alone or with motion offset plus electrical microstimulation.
We used three tasks. Each required the monkey to discriminate the
direction of random-dot motion and to indicate its direction decision
with an eye movement to one of two choice targets. For each task, the
monkey received a liquid reward for a correct response (an eye movement
to the correct target) and on half the 0% coherence trials. The tasks
differed in terms of the association between the direction decision and
the correct eye-movement response, as follows.
For the "pro-saccade" task (see Fig. 1A), the
monkey was rewarded for making an eye movement to a target at a known
location in the direction of motion. First, the monkey fixated a red
fixation point located at the center of the visual field. Next, two red choice targets appeared, located 8° from the fixation point and along
the axis of motion. After a random delay (100 msec plus a random time
chosen from an exponential distribution with mean 150 msec), the motion
stimulus appeared. The motion stimulus and the fixation point were
extinguished simultaneously, indicating to the monkey to make an eye
movement to one of the two targets. The correct target was located in
the direction of motion; thus, for rightward motion, the monkey was
rewarded for making an eye movement to the target to the right of the
motion stimulus. Data from this task have been presented previously
(Gold and Shadlen, 2000
).
For the "colored-target" task (see Fig. 1B), the
monkey was rewarded for making an eye movement to a target of the
appropriate color but at an unpredictable location. First, the monkey
fixated a blue fixation point located at the center of the visual
field. After a random delay, the motion stimulus appeared. Next, the motion stimulus and the fixation point were extinguished, and the two
choice targets (one red and one green, both with a luminance of 3.1 cd/m2) were shown simultaneously. The targets were
shown at random locations on an imaginary circle with a radius of 8°,
centered on the fixation point. The targets were separated by at least 3°. The green target was correct if motion had a leftward component, and the red target was correct if motion had a rightward component (we
never used purely vertical motion for this task).
For the "anti-saccade" task (see Fig. 1C), the monkey
was rewarded for making an eye movement to a target at a known location opposite to the direction of motion. First, the monkey fixated a
blue fixation point located at the center of the visual field. Next,
two red choice targets appeared, located 8° from the fixation point
and along the axis of motion. After a random delay, the motion stimulus
appeared. The motion stimulus and the fixation point were extinguished
simultaneously. The correct target was located opposite the direction
of motion; thus, for rightward motion, the monkey was rewarded for
making an eye movement to the target to the left of the motion
stimulus. In some experiments, the choice targets did not appear until
offset of the motion stimulus and fixation point.
The three tasks were not interleaved in a single experiment. Instead,
we first collected data using the pro-saccade task, which the monkeys
had several years of experience performing (Kim and Shadlen,
1999
; Gold and Shadlen, 2000
). We then trained
both monkeys and collected data using the colored-target task. Finally, we trained monkey I and collected data using the anti-saccade task.
Data acquisition. In preparation for electrophysiological
measurements, monkeys were implanted with a head-holding device, eye
coil, and recording cylinder (Crist Instrument Co., Damascus, MD)
suitable for magnetic resonance imaging (MRI). The recording cylinder
was positioned above the arcuate sulcus and the posterior third of the
principal sulcus (in the right hemisphere for monkey I and the left
hemisphere for monkey S). Tungsten microelectrodes (~1 M
impedance
at 1 kHz; Fred Haer and Co.) were advanced in sterile guide tubes that
extended through a plastic grid (Crist Instrument Co.) to the surface
of the dura mater. Electrode tracks were registered with the anatomy
using high-resolution MRI images obtained with fiducial markers in the
plastic grid (1.5 T scanner, short T1 inversion recovery, using custom
"carotid" radio-frequency coils). All tracks were in the portion of
the anterior bank of the arcuate sulcus corresponding to the FEF, which
was confirmed by physiological criteria (Bruce and Goldberg,
1985
).
During an experiment, eye position was monitored using the scleral
search-coil technique, sampled at 500 Hz (Robinson,
1963
; Judge et al., 1980
). The FEF was targeted
with a single tungsten microelectrode using stereotaxic and MRI
information. FEF sites were selected on the basis of the ability to
evoke saccadic eye movements using electrical microstimulation.
Microstimulation consisted of a train of 0.25-msec-long, biphasic
pulses applied at a rate of 350-450 Hz for 60 msec. A site was
selected if saccades with consistent trajectories were evoked using
<50 µA of current applied in darkness.
Once a site was selected, the axis of motion discrimination was rotated
to be roughly perpendicular to the trajectory of saccades evoked with
fixation only. The microstimulation current was then adjusted to evoke
saccades reliably during the motion-discrimination task (25-110 µA).
In each experiment, a fixed percentage of trials were accompanied by
microstimulation (75-80% of trials for experiments using the
pro-saccade task; 70-100% of trials for experiments using the
anti-saccade task; 50-100% of trials for experiments using the
colored-target task); these trials were chosen at random and
interleaved with trials without microstimulation. The microstimulation pulses began simultaneously with offset of the fixation point and the
motion stimulus. On these trials, the evoked saccade was followed by a
second, voluntary saccade to one of the choice targets. Reward was
given on the basis of this second, voluntary saccade.
Data analysis. We included for analysis only trials in which
the monkey maintained fixation to within a 1.2 × 1.2° square aperture while the fixation point was illuminated. Control trials without microstimulation were included only if the monkey made a
single, voluntary eye movement to within 3.5° of one of the two
choice targets 40-500 msec after fixation-point offset. Trials with
microstimulation were included only if there were two saccades: first
an evoked saccade that began 15-90 msec after fixation-point offset
(i.e., microstimulation onset) and then a voluntary saccade to one of
the two choice targets that began <500 msec after fixation-point offset. For the pro-saccade task, 96.2% of all control trials and
73.0% of all microstimulation trials tested in monkey I and 97.4% of
all control trials and 85.0% of all microstimulation trials tested in
monkey S met these criteria. For the colored-target task, 99.4% of all
control trials and 90.2% of all microstimulation trials tested in
monkey I and 99.6% of all control trials and 88.7% of all
microstimulation trials tested in monkey S met these criteria. For the
anti-saccade task, 96.7% of all control trials and 83.6% of all
microstimulation trials tested in monkey I met these criteria. Most
(66.0%) of the dropped microstimulation trials were attributable to no
evoked saccade.
Eye-position data were aligned such that the eye position at the time
of fixation-point offset (the "initial eye position" for
measurements of saccade trajectory) was at the origin. Saccade parameters were measured using eye position data that were smoothed with a Gaussian function (5 msec width at half-height). Saccades were
defined as eye movements that reached a peak velocity of at least
70°s
1 (this value was chosen to include the
shortest microstimulus-evoked saccades, which were ~1.5° in
amplitude; the peak velocity of most saccades was substantially higher:
mean ± SD = 327 ± 150°s
1 for
all microstimulus-evoked saccades and 451 ± 99°s
1 for all voluntary saccades). The end of a
saccade was defined as the eye position at the time that eye velocity
returned to nearly zero (< 10°s
1 for at least
10 msec).
The trajectories of electrically evoked saccades were quantified as the
vector from the initial eye position to the position measured at the
end of the evoked saccade (the "endpoint vector"). The validity of
this measurement was assessed in two ways. First, the trajectory
defined by the endpoint vector was compared with the trajectory
measured early in the saccade (defined as the vector from the initial
eye position to the eye position measured 16 msec after saccade onset).
Second, the length of the endpoint vector was compared with the path
length of the actual saccade. We included for analysis only straight
saccades, which were defined as those with a difference between early
and late trajectories of
20° and a ratio of path length to endpoint
vector length of
1.25 (91.4% of all evoked saccades met these criteria).
Endpoints of electrically evoked saccades typically drifted over the
course of an experiment (often lasting >3 hr) by 2.4 ± 1.8°
(mean ± SD; n = 60). To control for this drift,
we calculated a 150 point, chronological running mean of the endpoints
(separately for x and y coordinates) from all
trials corresponding to each of the two direction decisions in a given
experiment. For each endpoint, we then subtracted the average of the
two running means (corresponding to the two direction decisions)
centered on that point. This procedure caused the distributions
of the x and y positions of all endpoints from a
given experiment to be centered at nearly zero.
The deviation of a saccade in the direction of the monkey's decision
was quantified as the dot product between the endpoint vector
from running-mean-subtracted data and a unit vector along the axis of
motion. For the pro- and anti-saccade tasks, the unit vector was in the
direction of the target that the monkey ultimately chose. Thus, for the
pro-saccade task, a positive deviation measured the magnitude of the
component of the endpoint vector that was toward the direction of
motion on correct trials and opposite the direction of motion on error
trials. For the anti-saccade task, a positive deviation measured the
magnitude of the component of the endpoint vector that was opposite the
direction of motion on correct trials and toward the direction of
motion on error trials. For the colored-target task, the location of
the choice targets was not specified before the stimulated eye
movement. We therefore adopted the same convention as in the
pro-saccade task: the unit vector was in the direction of motion.
We used two strategies to combine data from multiple stimulation sites.
First, we simply combined data from all sites without standardization.
Thus, each stimulation trial contributed a value to the population
average that reflected its absolute magnitude of deviation. Second, we
performed a standardization of the deviation vectors before combining
data. For each site and for each direction, the average deviation was
subtracted, and the residual was expressed in units of SD, using all
trials in that experiment. This method discarded information about
overall magnitude of deviation but maintained information about the
relative effects of motion strength and viewing duration within each
session. We used the latter method to control for the possibility that
experimental sessions with different average magnitudes of deviation
could exert unfair leverage on the population analyses.
To quantify how the magnitude of deviation depended on motion coherence
(C) and viewing time (T), we used the following
regression equations:
|
(1)
|
|
(2)
|
where the ki are coefficients that were
fit by weighted least squares, and
is a random quantity that is
assumed to be distributed as Gaussian with variance estimated from the
data. To test the significance of the dependence on C (Eq. 1), we used an F test (Draper and Smith,
1998
) to evaluate the null hypothesis, H0: k1 = 0. To test the significance of the
dependence on T (Eq. 2), we evaluated H0:
k1 = k2 = 0. Icorrect is 1 or 0 to indicate correct or error
trials, respectively, and was used to test the nested hypothesis that
the magnitude of the deviation was different for correct versus error
trials; we evaluated H0: kc = 0.
Assessment of performance and the underlying decision
variable. We measured probability correct (P) as a
function of motion strength and viewing duration. We quantified this
dependence using several models. For each model, psychometric functions
were derived by maximizing the (binomial) likelihood of observing the
monkey's choices. We report the maximum likelihood estimate of each
parameter along with its uncertainty using standard methods
(Meeker and Escobar, 1995
).
For each task, we binned the performance data by viewing duration and
fit each bin to a coherence-dependent cumulative Weibull distribution
function (Quick, 1974
):
|
(3)
|
where
,
, and
are the fit parameters. The value 0.5 represents chance performance.
represents the lapse rate, which is
the probability of making an incorrect response even for easily discriminable stimuli. Because
determines asymptotic performance at
high coherences and long viewing durations, its value was determined by
fitting Equation 3 to performance data from trials with long viewing
durations (> 500 msec), and this value was then inserted in the
equation to find best fits of
and
to the data binned by viewing
duration.
is the threshold, which governs the scaling of stimulus
strength (C) on performance and corresponds to the value of
C that elicits 82% correct responses when asymptotic performance is perfect (i.e.,
= 0).
governs the shape of
the function, affecting mainly its steepness.
We used the best fits of
and
to the time-binned data to analyze
how performance depended on viewing time and whether this time
dependence was affected by FEF microstimulation. We used a power-law
expression to characterize the monotonic decline in threshold as a
function of viewing time:
(T) = k0Tk1, where
k0 and k1 are fitted
parameters. To test whether the dependence on time was significant, we
evaluated H0: k1 = 0.
Logarithmic transformation of this power-law expression yields a linear
function, which we used to test the nested hypothesis that
microstimulation affects the dependence on time:
|
(4)
|
where the ki are fitted parameters,
Istim is 1 or 0 to indicate microstimulation or
no microstimulation, respectively, and
is as in Equations 1 and 2.
We evaluated H0: k2 = k3 = 0.
The shape parameter,
, did not exhibit a monotonic relationship with
time. We therefore used simple polynomial regression to characterize
its time dependence and to evaluate the effect of microstimulation:
|
(5)
|
where the symbols are as in Equation 4. To test the dependence
on time, we evaluated H0: k1 = k2 = 0. To test whether microstimulation affects
the relationship between
and T, we evaluated
H0: k3 = k4 = k5 = 0.
To analyze the relationship between performance and the motion- and
time-dependent saccade deviation data (see Model of a decision variable
in Results), we did not bin the data by viewing time but rather
expressed P as a continuous function of both motion strength
and viewing time. We used two models of performance. The first model
assumed that the brain accumulates motion information over time and
that the monkey forms its direction decision on the basis of the value
of this accumulated quantity. By treating the representation of motion
information as a random variable, this model is a form of random walk,
which is central to many theories of perception (Luce,
1986
; Link, 1992
; Ratcliff and Rouder, 1998
; Gold and Shadlen, 2002
). Note that many
such models also attempt to explain response times (i.e., the subject
determines the viewing duration needed to reach a decision) and thus
assume that the decision is reached when the accumulated
quantity
called a decision variable
reaches a particular value (or
barrier). In contrast, in our experiments we controlled the viewing
time, so our model assumed that the decision was made on the basis of
whatever value the decision variable had reached at the end of that time.
For this model, the decision variable was computed as the difference
between two variable quantities, S1 and
S2, which can be thought of as the
accumulated, coherence-dependent responses of motion sensors that
encode the correct and incorrect directions of motion,
respectively:
|
(6a)
|
|
(6b)
|
where C is coherence (the fraction of coherently
moving dots, 0 ... 1), T is viewing time (in seconds),
and R0 is the response (in spikes per second) of
either sensor to a 0% coherence motion stimulus. The remaining
terms
a, m, and n
are fitted, positive-valued parameters that describe how coherence and time affect
S1 and S2 and are
described in detail in Results.
···
denotes expectations of S1 and S2,
which are treated as normally distributed random variables with
variances that scale by a factor
with their mean values:
This variability accounts for errors, which result when
S2 > S1. A correct response
results when S1 > S2. If
S1 and S2 are independent and normally distributed, then the probability, P, of a
correct response is:
|
(7)
|
where µ is the expected value of the accumulated difference,
S1
S2
=
S1
S2
, and
2
is the variance, computed as the sum of the individual variances associated with S1 and
S2. Note that µ and
2 depend on
both C and T. P was adjusted to take into account
the lapse rate,
, computed using Equation 3.
Our primary rationale for using this accumulation model is that when
fit to the behavioral data, it enabled us to derive a decision
variable, D, that we could compare to the evoked-saccade data. The value of D depended only on the difference between
the opposing pools of sensors described by Equation 6 and was computed for a given motion strength and viewing duration by using the values of
the parameters a, m, and n fit to the behavioral
data. As with the deviation data, we calculated and plotted
D for correct and incorrect choices:
|
(8)
|
where
x|y
indicates the expected value of
x given that y is true. D is therefore
the expected difference favoring the decision that the monkey actually
makes and is always positive for all motion strengths and viewing
durations, including the case of 0% coherent motion.
To transform D into units related to the probability of
making a particular eye-movement response, we first assumed that
D was proportional to the logarithm of the likelihood ratio
favoring the choice that the monkey made (Gold and Shadlen,
2001
):
|
(9)
|
Thus, D can be thought of as the amount of evidence
supporting the choice that the monkey made. For a two-alternative
forced-choice task, we can transform this decision variable into the
probability of making that choice (in this case, the correct choice)
given the value of D. Using Bayes' rule and
assuming equal previous probabilities of making each of the two
choices:
|
(10)
|
which rearranges to:
|
(11)
|
where
is a constant.
Equation 11 describes the probability of making a correct choice given
a particular value of the decision variable. We fit a time-shifted and
scaled version of this quantity to the magnitude of evoked-saccade
deviations. That is, we fit the deviation data to a quantity related to
the probability of making a particular, correct choice, given the value
of D:
|
(12)
|
where
and
are the fit parameters and
is the time
shift.
scales the magnitude, which is offset to take into account the fact that P(choice|D) varies from 0.5 to
1.0.
is defined in Equation 11. The time shift
accounts for a
short delay after the onset of the motion stimulus, before the growing
decision variable affected the developing oculomotor commands; it was
computed as the first time at which deviation magnitude depended
significantly on motion strength (Eq. 1; p < 0.05).
Recall that D is a function of motion coherence and viewing time.
The second time-dependent model of performance assumed that the
probability of making a correct response improves as a function of time
because there are more opportunities to make the appropriate discrimination. This process, which is termed probability summation over time (Watson, 1979
), was modeled using a
psychometric function that was also based on the Weibull
distribution:
|
(13)
|
where
and
are the same as in Equation 3, and
is a
fit parameter that determines the time-dependent threshold,
This model
predicts that the underlying neural signals do not reflect an
accumulation of motion over time but instead reflect either an
undecided or a committed state in favor of one of the alternatives. To
test this prediction, we analyzed whether the deviation data comprised
a mixture of values that correspond to the undecided and fully
committed states (see Fig. 15).
Finally, we fit the deviation data with an alternative model that did
not depend on the underlying decision process. For this model, we
assumed that the saccade deviations arise from stereotyped dynamics of
oculomotor commands that are simply delayed as a function of motion
strength:
|
(14)
|
where k,
, and n are fit parameters
that describe the temporal dynamics of the saturating exponential
function and are independent of motion strength. Only the parameter
coh, which describes the delay, depended on
motion strength. Equations 12 and 14 were both fit to the monotonically
increasing part of the deviation data (<350 msec viewing time).
 |
Results |
Our central hypothesis was that when the monkey forms a decision
about the direction of random-dot motion and indicates its decision
with a specific, predictable eye movement, the underlying decision
process is reflected in signals associated with the oculomotor response. To test this idea, we evoked eye movements with electrical microstimulation of the FEF while the monkey was forming its decision. We analyzed the trajectories of these evoked eye movements for evidence
of ongoing oculomotor activity associated with the generation of the
eye-movement response (Gold and Shadlen, 2000
).
In the first section, below, we present data from the pro-saccade task,
which had a predictable association between the decision and the
eye-movement response. We show that performance improved steadily with
increasing motion strength and viewing time and that a similar buildup
of motion information was evident in neural signals associated with the
generation of the eye-movement response. In the second section, we
present data from the colored-target task, which did not have a
predictable association between the decision and the eye-movement
response. We show that performance also improved steadily with
increasing motion strength and viewing time, but that no commensurate
developing oculomotor commands were evident. We also present data from
the anti-saccade task, which confirm our interpretation of the results
from the other two tasks: when the decision is associated with a
specific eye-movement response, decision formation is evident in the
signals that generate the response. In the third section, we develop a
computational model to assess more quantitatively the relationship
between the motion- and time-dependent oculomotor signals evident in
the pro-saccade task and the neural computations responsible for
forming the monkey's direction decision.
Pro-saccade task
For the pro-saccade task (Fig. 1A), the monkey
indicated its direction decision with an eye movement of a prespecified
trajectory in the direction of motion. The two choice targets, which
were visible from before the motion stimulus appeared until after the monkey indicated its decision, were located along the axis of motion.
The correct target was located in the direction of motion. Thus, for
example, a rightward decision was always indicated with a rightward eye
movement. This task has been used extensively in other studies
(Britten et al., 1992
).
Performance on this task depended on both the strength and duration of
the motion stimulus (Fig. 2). At low
motion strengths and short viewing durations, performance was near
chance. As motion strength and viewing duration increased, the
percentage of correct responses improved steadily, to ~100% accuracy
at 51.2% motion coherence and viewing durations of more than ~250
msec (Fig. 2A). We quantified this dependence by
fitting behavioral data for different viewing durations to best-fitting
cumulative Weibull distribution functions (Fig. 2B)
(Quick, 1974
). The fits demonstrated a systematic, inverse relationship between discrimination threshold (
in Eq. 3)
and viewing duration (Fig. 2C) (Eq. 4; p < 0.01). The parameter
in Equation 3, which describes the shape
of the psychometric function, had best-fit values of between 0.9 and
1.4 and depended significantly on viewing duration in monkey S (Fig.
2D) (Eq. 5; p < 0.01) but not in
monkey I (p = 0.18). The lapse rate (
in Eq. 3),
determined from long-duration trials, was zero for both monkeys
[likelihood ratio (LR) test, H0:
= 0; p = 1].

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Figure 2.
Performance on the pro-saccade task. A,
Percentage correct plotted as a function of viewing time. Points are
shown at the center of time bins that had the width adjusted to include
approximately equal numbers of trials (n 300 trials
per point). Colors represent different motion strengths, as
indicated. Data are from 45,511 trials, with and without FEF
microstimulation, from both monkeys. B, Percentage correct
plotted as a function of motion strength. Points represent a subset of
the data presented in A, binned into four groups of viewing
duration, as indicated. The solid curves are maximum
likelihood fits of the cumulative Weibull function (Eq. 3), computed
separately for each curve. The dashed line shows the
asymptotic performance (determined from in Eq. 3), estimated from
long duration trials only. C, Discrimination threshold as a
function of viewing time. Symbols and error bars represent the best fit
and SEM of the parameter in Equation 3 to performance data in
80-msec-wide bins of viewing time. To emphasize the inverse
relationship between threshold and time, the best fits of Equation 4
are shown (lines) for each data set. Circles
correspond to monkey I; triangles correspond to monkey S. Closed symbols and solid lines correspond to
trials without FEF microstimulation; open symbols and
dashed lines correspond to trials with FEF microstimulation.
D, Slope of the psychometric function (the parameter in
Eq. 3) as a function of viewing time. Symbols and error bars are
plotted as in C. Note the logarithmic time scales in
C and D. coh,
Coherence.
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To assess developing oculomotor activity during formation of this
coherence- and time-dependent decision, we analyzed the trajectories of
eye movements evoked with electrical microstimulation of the FEF. It is
important to note that this microstimulation technique allowed us to
study the mechanisms responsible for forming the direction decision
without substantially disrupting performance. As shown in Figure
2C, discrimination thresholds measured on trials with
microstimulation were slightly higher than those measured on control
trials, but by only 1.6 ± 0.3% coherence in monkey I and
1.4 ± 0.6% coherence in monkey S (Eq. 4; p < 0.01 for each monkey). There was no obvious explanation for this
slight decrease in performance (e.g., it was unrelated to the
percentage of trials in a given experiment in which microstimulation
occurred; weighted least-squares regression; p > 0.05
for each monkey). Moreover, microstimulation affected the slope of the
psychometric function in monkey S (Fig. 2D) (Eq. 5;
p < 0.01) but not in monkey I (p = 0.77), and it did not affect the lapse rate (zero for both monkeys on trials with microstimulation). Together, these observations indicate
that FEF microstimulation interfered only slightly with the
monkeys' ability to perform the discrimination task.
Figure 3 illustrates the effects of FEF
microstimulation on the eye movements in a single experiment
(eye-movement data from all experiments are summarized in Table
1). Figure 3A-C shows the
monkey's eye positions from individual control trials. The eyes
remained stationary during motion viewing. After a random viewing
duration, the motion stimulus and the fixation point were extinguished
simultaneously. The monkey then made a voluntary, saccadic eye movement
that brought its gaze to one of the two choice targets, thereby
indicating its direction decision.

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Figure 3.
Examples of eye movement traces from correct
trials in a single pro-saccade experiment, using various motion
strengths and viewing durations. Eye position was sampled in 2 msec
intervals. A, Ten trials without microstimulation. The
two large circles represent the two targets. The fixation
point is at the origin. The dashed line indicates the axis
of motion. B, C, Time course of the eye movements
corresponding to the trials in A. Data are aligned to
fixation-point offset (time = 0). D-F, Ten trials with
microstimulation, plotted as in A-C. In A-F,
rightward decisions (i.e., rightward eye movements) are indicated with
thicker lines than leftward decisions (i.e., leftward eye
movements). pos, Position; deg,
degree.
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Figure 3D-F shows eye-position data from the same
experiment but from trials with microstimulation. The monkey's eyes
again remained stationary during motion viewing. After a random viewing duration, the motion stimulus and the fixation point were extinguished, and electrical microstimulation was begun simultaneously. The monkey
then made two distinct saccadic eye movements. The first, electrically
evoked saccade had a trajectory roughly perpendicular to the axis of
motion. The second, voluntary saccade traveled from the endpoint of the
evoked saccade to one of the two choice targets, thereby indicating the
monkey's direction decision.
By interrupting motion viewing, FEF microstimulation interrupted the
period during which the monkey gathered motion information toward a
direction decision. We tested whether this evolving decision was
reflected in developing oculomotor commands by analyzing the trajectories of the eye movements evoked by the FEF microstimulation. We quantified this trajectory as the vector defined by the change in
eye position from the onset of electrical microstimulation to the pause
immediately after the first (evoked) saccade (the "endpoint vector"
or simply "endpoint"). Subsequent analyses include only straight
saccades, for which the endpoint vector was a reliable estimate of
saccade trajectory (see Materials and Methods).
The endpoints of eye movements evoked by electrical microstimulation of
the FEF during the discrimination task were similar to those evoked
with fixation only but tended to deviate in the direction of the
subsequently selected target. Figure
4A shows the endpoints
of the electrically evoked saccades from correct discrimination trials
in a single experiment, sorted by the monkey's choice. In nearly all
cases, the endpoints were located above the fixation point, similar to
those evoked with fixation only (data not shown). However, on trials in
which the monkey subsequently chose the left target, the endpoints of
the evoked saccades tended to deviate leftward. Conversely, on trials
in which the monkey subsequently chose the right target, the endpoints
tended to deviate rightward.

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Figure 4.
Effect of target choice on electrically evoked eye
movements from the pro-saccade task. A, Endpoints of evoked
saccades for all correct trials from the experiment depicted in Figure
3. Open and closed symbols represent leftward and
rightward decisions, respectively. The magnitude of separation was
defined as the distance between the means of the two distributions of
endpoints (filled circles, designated with an
open and a closed symbol; r = 4.4°). The dashed line connecting the mean endpoints
defines an axis of separation that is nearly parallel to the axis of
random-dot motion, indicated by the line between the choice targets
(large filled circles). The angle between these two lines
gives the direction of separation ( = 0.28 radians).
B, Mean endpoint positions of evoked saccades for all
correct discrimination trials from each site tested. Line
segments radiating from the origin represent the mean evoked
saccade. Circles (monkey I) and triangles (monkey
S) show the mean evoked saccades sorted by the direction of the ensuing
target choice. Closed symbols indicate the more rightward
choice. For each experiment, the dependence of endpoint position on
target choice was significant (two-dimensional Kolmogorov-Smirnov
test; p < 0.01). C, Polar plot of the
magnitude (r) and direction ( ) of saccade endpoint
separation, as described in A, for all sites tested in
monkey I (circles) and monkey S (triangles).
Angles near zero indicate that the endpoints tended to
deviate along the axis of motion and in the direction of the
subsequently selected target. Values for r and were
computed from run-mean-subtracted data, as described in Materials and
Methods but not shown in A. deg, Degree; rad,
radian.
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Figure 4, B and C, summarizes the effect of
the monkey's direction decision on the endpoints of evoked saccades
for all 32 sites tested. Figure 4B shows the average
endpoint vectors of saccades evoked on all correct discrimination
trials (lines radiating from the center). These average trajectories,
which ranged in amplitude from 1.5 to 12.5°, were predicted by the
saccades evoked during fixation, when the monkey did not perform the
discrimination task. However, averaging the trajectories across all
trials belies a systematic deviation as a function of the monkey's
direction decision. The pairs of symbols at the end of each radiating
line show the mean saccade endpoints sorted by the monkey's decision (the dependence of endpoint position on target choice was significant for each experiment: two-dimensional Kolmogorov-Smirnov test; p < 0.01). These endpoints tended to be separated by
1.6 ± 0.2° (mean ± SEM; radii in the polar plot in Fig.
4C), along the axis formed by the two targets (angles in the
polar plot in Fig. 4C). These results indicate that the
trajectory of an electrically evoked saccade had a component determined
by the site of electrical microstimulation and a component in the
direction of the subsequently selected choice target.
We quantified the magnitude of the component of deviation in the
direction of the subsequently selected choice target for both correct
and error trials (see Materials and Methods for details). For correct
trials, this quantity was positive, on average, indicating a deviation
in both the direction of motion and the direction of the monkey's
subsequent voluntary saccade (monkey I: mean ± SEM = 0.82 ± 0.01°, n = 12,480 correct trials; monkey
S: 0.74 ±0.01°, n = 11,226). Although the value of
this quantity varied across stimulation sites (Kruskal-Wallis test;
p < 0.01 for both monkeys together or each
individually), ranging from 0.39 ± 0.06 to 2.80 ± 0.20°
in monkey I and 0.23 ± 0.02 to 1.67 ± 0.03° in monkey S,
it indicated a consistent direction of deviation for all 32 sites. For
error trials, this quantity was also positive, on average, indicating a
deviation opposite the direction of motion but in the direction of the
monkey's subsequent voluntary saccade (monkey I: 0.53 ± 0.02°,
n = 2,577 error trials; monkey S: 0.62 ± 0.02°,
n = 2,556; these values were significantly smaller than those measured on correct trials; t tests; p < 0.01). Like the data from correct trials, the value of this
quantity varied across stimulation sites (p < 0.01 for
both monkeys together or each individually), ranging from 0.31 ± 0.03 to 1.78 ± 0.10° in monkey I and 0.17 ± 0.05 to
1.32 ± 0.08° in monkey S, but indicated a consistent direction
of deviation for all sites. These results indicate that the deviation
reflected the impending motor command (which itself reflected the
monkey's direction decision) and not simply the direction of the
motion stimulus.
This component of the evoked saccade in the direction of the
subsequently selected target was not simply a function of the intended eye movement; it also depended on the strength and viewing duration of the motion stimulus. Figure
5A shows the endpoints of
evoked saccades on trials from a single experiment in which the monkey
viewed weak motion for a short time, conditions that make it difficult
to reach an accurate direction decision. These evoked saccades deviated
only slightly in the direction of the subsequently selected target.
Figure 5B shows the endpoints of evoked saccades from the
same experiment, but on trials in which the monkey viewed strong motion
for a longer time, conditions that make it easy to make an accurate
direction decision. These evoked saccades deviated more strongly in the
direction of the subsequently selected target.

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Figure 5.
Effect of motion strength and viewing duration on
electrically evoked eye movements from the pro-saccade task. A,
B, Endpoints of evoked saccades from all correct trials in the
experiment depicted in Figure 4A. Open and closed
symbols indicate trials in which the monkey subsequently indicated
a leftward or rightward decision, respectively. The average endpoint
was estimated as a running mean and subtracted from each data point,
thereby centering the distribution of points at the origin. The
magnitude of deviation in the direction of the subsequently selected
target was quantified as the dot product between the endpoint vector
(thin arrows) and the unit vector in the direction of the
target (thick arrows). Examples are illustrated in
red. Zero magnitude is represented by the roughly
vertical lines that pass through the origin. Positive values
indicate deviations in the direction of the subsequently selected
target. Deviation magnitude was smaller on trials with weak motion and
short viewing durations (colored symbols in A;
mean ± SEM = 0.5 ± 0.3°) than on trials with strong
motion and longer viewing durations (colored symbols in
B; 3.2 ± 0.1°). C, Summary of the effect
of motion strength on deviation magnitude. Data are sorted by viewing
duration, as labeled. Symbols and error bars represent mean and SEM.
The effect of motion strength was significant for all but the earliest
epoch (Eq. 1; p < 0.01). Data are from correct trials.
D, Summary of the effect of viewing duration on deviation
magnitude. Data are sorted by motion strength, as labeled. Symbols and
error bars represent mean and SEM in 40-msec-wide bins of viewing
duration. The effect of viewing duration was significant at each
coherence (Eq. 2; p < 0.01). Solid lines
represent data from correct trials; dashed lines represent
data from error trials. E, Data as in D, but
standardized with respect to the mean for each experiment (see
Materials and Methods for details). For this data set, the effect of
motion strength was significant for all but the earliest epoch (Eq. 1;
p < 0.01), and the effect of viewing duration was
significant at each coherence (Eq. 2; p < 0.01).
coh, Coherence; deg, degree.
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Figure 5C-E summarizes the effects of motion strength and
viewing duration on evoked-saccade trajectory from the population of
data from all 32 experiments. For correct trials, the average magnitude
of deviation increased steadily as a function of both motion strength
(Fig. 5C) and viewing duration (which tended to cause a
steady increase in deviation magnitude to a plateau or slight decrease
at ~350-500 msec) (Fig. 5D, E). For error trials, the
average magnitude had a similar dependence on viewing duration but was
smaller and tended to decrease with increasing coherence (Fig.
5D, E). These trends were evident in data combined directly (Fig. 5C, D) and data standardized to account for
differences in deviation magnitude measured in different experimental
sessions (Fig. 5E). As illustrated by the similarity of
these two data sets, the strength and duration of the motion stimulus
were the strongest and most consistent factors of the experimental
protocol that influenced the deviation data.
Several other factors had slight effects on the deviation data. An
increased percentage of microstimulation trials in a given experiment
corresponded to larger deviations in both monkeys (weighted linear
regression; p < 0.01). The amount of overall drift in
endpoint position was also positively correlated with the magnitude of deviation in monkey I (p < 0.01) but not monkey S
(p = 0.15). The average magnitude of the endpoint
vector was positively correlated with deviation magnitude in monkey I
(p < 0.01) but negatively correlated in monkey S
(p < 0.01). These effects are removed in the
standardized data set used to construct Figure 5E.
The results suggest that when a decision about the direction of motion
is indicated with an eye movement in the same direction, neural signals
associated with the generation of the eye-movement response also
reflect a quantity that is a graded function of motion strength and
viewing duration. Thus, for this task, there appears to be a close link
between formation of the decision and formation of the behavioral
response. In the next section, we corroborate this interpretation by
measuring evoked-saccade deviations on tasks with different
decision-response associations.
Colored-target task
A key point in interpreting the pro-saccade data is that the task
explicitly linked the monkey's direction decision with a specific
eye-movement response. Our results appear to be a direct consequence of
this link: formation of the decision was evident in signals that
reflect the development of the eye-movement response (the
evoked-saccade deviations). A test of this interpretation would be to
unlink the decision and the eye-movement response, which in principle
should eliminate the decision-related oculomotor activity. We performed
this test by requiring the monkeys to make the same decision about the
direction of random-dot motion as the pro-saccade task but to indicate
their decision with an eye movement to a target of a certain color:
green for leftward motion, red for rightward motion (Fig.
1B). Importantly, the targets were not visible until
after the motion stimulus was extinguished, and they appeared at random
locations. Thus, during motion viewing and decision formation, the
monkey could not prepare an eye-movement response to a particular location.
Performance on the colored-target task improved as a function of motion
strength and viewing duration (Fig. 6).
At low motion strengths and short viewing durations, performance was
near chance. As motion strength and viewing duration increased,
performance improved steadily, to ~90% accuracy at 51.2% motion
coherence and viewing durations of more than ~400 msec. This
dependence on motion strength and viewing duration was similar to that
found on the pro-saccade task. Psychometric threshold was inversely related to viewing time (Fig. 6C) (Eq. 4; p < 0.01) and was significantly affected by FEF microstimulation for
monkey I (p < 0.01) but not monkey S (p = 0.80). Likewise, the slope of the psychometric function varied
significantly (but not monotonically) with viewing time (Fig.
6D) (Eq. 5; p < 0.01) and was
significantly affected by FEF microstimulation in monkey I
(p < 0.01) but not monkey S (p = 0.37). Peak performance was lower (i.e., the lapse rate was higher: 9.1 ± 1.0% coherence for monkey I; 7.6 ± 1.0% for
monkey S) on the colored-target task, particularly on microstimulation trials (16.2 ± 1.4% coherence for monkey I; 11.5 ± 2.1%
for monkey S).

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Figure 6.
Performance on the colored-target task.
A, Percentage correct plotted as a function of viewing time.
Points are shown at the center of time bins that had the width adjusted
to include approximately equal numbers of trials (n 350 trials per point). Colors represent different motion
strengths, as indicated. Data are from 61,057 trials, with and without
FEF microstimulation, from both monkeys. B, Percentage
correct plotted as a function of motion strength. Points
represent a subset of the data presented in A, binned into
four groups of viewing duration, as indicated. The solid
curves are maximum likelihood fits of the cumulative Weibull
function (Eq. 3), computed separately for each curve. The dashed
line shows the asymptotic performance (determined from in Eq. 3), estimated from long duration trials only. C,
Discrimination threshold as a function of viewing time. Symbols and
error bars represent the best fit and SEM, respectively, of the
parameter in Eq. 3 to performance data in 80-msec-wide bins of
viewing time. To emphasize the inverse relationship between threshold
and time, best fits of Equation 4 are shown (lines) for each
data set. Circles correspond to monkey I;
triangles correspond to monkey S. Closed symbols
and solid lines correspond to trials without FEF
microstimulation; open symbols and dashed lines
correspond to trials with FEF microstimulation. D, Slope of
the psychometric function (the parameter in Eq. 3) as a function of
viewing time. Symbols and error bars are plotted as in C. Note the logarithmic time scales in C and D. coh, Coherence.
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Figure 7 illustrates eye-movement data
from a single experiment using the colored-target task (eye-movement
data from all experiments are summarized in Table 1). The monkey's
eyes remained stationary during motion viewing. On trials without
microstimulation, the fixation point and the motion stimulus were
extinguished, and the two choice targets were shown simultaneously,
after which the monkey made a single, saccadic eye movement directly to
one of the targets. This eye movement was initiated with a longer latency relative to fixation-point offset than for the pro-saccade task
(Table 1), reflecting the additional time needed to process the
location of the targets. On trials with FEF microstimulation, the
fixation point and the motion stimulus were extinguished, the two
choice targets were shown, and electrical microstimulation was begun
simultaneously. The monkey then made two distinct eye movements. The
first, electrically evoked saccade had a trajectory roughly
perpendicular to the axis of motion. The second, voluntary saccade
traveled from the endpoint of the evoked saccade to one of the two
choice targets, the color of which indicated the monkey's direction
decision.

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Figure 7.
Examples of eye movement traces from correct
trials in a single colored-target experiment, using various motion
strengths and viewing durations. Eye position was sampled in 2 msec
intervals. A, Ten trials without microstimulation. The
targets are not shown because they appeared at different locations for
each trial. The fixation point is at the origin. The dashed
line indicates the axis of motion. B, C, Time courses
of the eye movements corresponding to the trials in A. Data
are aligned to fixation-point offset (time = 0). D-F,
Ten trials with microstimulation, plotted as in A-C. In
A-F, rightward decisions (i.e., eye movements to the red
target) are indicated with thicker lines than leftward
decisions (i.e., eye movements to the green target). pos,
Position; deg, degree.
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For this task, the monkey's direction decision had little effect on
the endpoints of saccades evoked with FEF microstimulation. Figure
8A shows the
evoked-saccade endpoints from all straight saccades and correct trials
in a single experiment, sorted by the monkey's choice. The endpoints
of these saccades were located above the fixation point, similar to
those evoked with fixation only (data not shown). Importantly, the
endpoints were similarly distributed regardless of the monkey's
direction decision.

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Figure 8.
Effect of target choice on electrically evoked eye
movements from the colored-target task. A, Endpoints of
evoked saccades for all correct trials from the experiment depicted in
Figure 7. Open and closed symbols represent
leftward and rightward direction decisions, respectively
(n = 905; many points are obscured by other symbols).
The magnitude of separation was defined as the distance between the
means of the two distributions of endpoints (filled
circles, which are superimposed, designated with an
open and a closed symbol; r = 0.1°). The dashed line connecting the mean endpoints
defines an axis of separation that crosses the axis of random-dot
motion, indicated by the line through the fixation point at
the origin. The angle between these two lines gives the
direction of separation ( = 0.73 radians). B, Mean
endpoint positions of evoked saccades for all correct discrimination
trials from each site tested. Line segments radiating from
the origin represent the mean evoked saccade. Circles
(monkey I) and triangles (monkey S) show the mean evoked
saccades sorted by the monkey's direction decision. Closed
symbols indicate rightward decisions. Large symbols
indicate experiments in which the dependence of endpoint position on
target choice was significant (two-dimensional Kolmogorov-Smirnov
test; p < 0.01). C, Polar plot of the
magnitude (r) and direction ( ) of saccade endpoint
separation, as described in A, for all sites tested in
monkey I (circles) and monkey S (triangles).
Radii near zero imply minimal separation of endpoints. Values for
r and were computed from run-mean-subtracted data, as
described in Materials and Methods but not shown in A. deg, Degree; rad, radian.
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Figure 8, B and C, summarizes the effects of the
monkey's direction decision on the endpoints of saccades evoked on
discrimination trials from each of the 18 colored-target experiments.
There was a slight but significant difference between endpoints
associated with the two direction decisions in 15 of these experiments
(Fig. 8B) (two-dimensional Kolmogorov-Smirnov test;
p < 0.01). However, this difference reflected a
separation of mean endpoints of <0.6° for all sites tested (radii in
the polar plot in Fig. 8B). Moreover, these
separations were in no systematic direction relative to the axis of
random-dot motion (angles in the polar plot in Fig. 8C).
Accordingly, the magnitude of deviation along the axis of motion was
significantly smaller than that found in the pro-saccade data
(t tests comparing correct or error data across sites for each monkey individually; p < 0.01). For correct
trials from monkey I, the mean ± SEM deviation was 0.11 ± 0.01° (n = 7062 trials), ranging from
0.02 ± 0.02 to 0.13 ± 0.01° for individual experiments. For error
trials from monkey I, the magnitude of deviation was 0.05 ± 0.01° (n = 2610), ranging from
0.09 ± 0.01 to
0.08 ± 0.11°. For correct trials from Monkey S, the magnitude
of deviation was
0.07 ± 0.01° (n = 6512),
ranging from
0.18 ± 0.03 to 0.03 ± 0.01°. For error
trials from Monkey S, the magnitude of deviation was 0.00 ± 0.01° (n = 2513), ranging from
0.07 ± 0.02 to
0.07 ± 0.03°.
The near absence of deviations along the axis of motion appeared to
reflect the fact that the monkey could not predict the direction of the
appropriate eye-movement response while viewing the motion stimulus.
The lack of visual targets