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The Journal of Neuroscience, November 1, 2002, 22(21):9475-9489
Response of Neurons in the Lateral Intraparietal Area during a
Combined Visual Discrimination Reaction Time Task
Jamie D.
Roitman1 and
Michael N.
Shadlen2
1 Program in Neurobiology and Behavior, and
2 Howard Hughes Medical Institute, Department of Physiology
and Biophysics, and Regional Primate Research Center, University of
Washington, Seattle, Washington 98195-7290
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ABSTRACT |
Decisions about the visual world can take time to form, especially
when information is unreliable. We studied the neural correlate of
gradual decision formation by recording activity from the lateral intraparietal cortex (area LIP) of rhesus monkeys during a combined motion-discrimination reaction-time task. Monkeys reported the direction of random-dot motion by making an eye movement to one of two
peripheral choice targets, one of which was within the response field
of the neuron. We varied the difficulty of the task and measured both
the accuracy of direction discrimination and the time required to reach
a decision. Both the accuracy and speed of decisions increased as a
function of motion strength. During the period of decision formation,
the epoch between onset of visual motion and the initiation of the eye
movement response, LIP neurons underwent ramp-like changes in their
discharge rate that predicted the monkey's decision. A steeper
rise in spike rate was associated with stronger stimulus motion and
shorter reaction times. The observations suggest that neurons in LIP
integrate time-varying signals that originate in the extrastriate
visual cortex, accumulating evidence for or against a specific
behavioral response. A threshold level of LIP activity appears to mark
the completion of the decision process and to govern the tradeoff between accuracy and speed of perception.
Key words:
lateral intraparietal area (LIP); decision; reaction time; random-dot motion; electrophysiology; vision; psychophysics
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INTRODUCTION |
A hallmark of higher brain function
is the ability to form decisions from sensory data to guide appropriate
behavioral responses. Decisions tend to be more accurate if subjects
are given longer exposure to a stimulus (Green and Luce,
1973 ; Wickelgren, 1977; Gold and Shadlen,
2000; Mateeff et al., 2000), and given the
freedom to respond when ready, subjects will improve accuracy by taking more time (Luce, 1986). The relationship between
accuracy and processing time suggests that subjects accumulate
information to improve performance. A long-term objective of cognitive
neuroscience is to elucidate the neural mechanisms that underlie
decision formation. To this end, we have looked for a neural correlate
of decision making in a task that requires the accumulation of sensory
information as a function of time.
A simple form of decision making occurs when an observer discriminates
between two possible interpretations of a visual stimulus. Newsome and colleagues (1989) introduced a random-dot
motion discrimination task suitable for the simultaneous study of
threshold psychophysics and neurophysiology in monkey. A combination of
recording, lesion, and stimulation studies has established that neurons
in extrastriate cortex (areas MT and MST) carry critical sensory
information about motion direction (Newsome and Pare,
1988; Salzman et al., 1990; 1992; Britten et al.,
1992; Celebrini and Newsome, 1994;
1995). These signals
inform a binary decision about direction, which determines the
monkey's behavioral response, a saccadic eye movement.
Many neurons in the lateral intraparietal area (LIP) respond to visual
stimuli that are the target of a planned saccadic eye movement
(Gnadt and Andersen, 1988; Colby et al.,
1996; Platt and Glimcher, 1997). When the
direction of random-dot motion instructs the choice of a target for a
saccade, LIP activity modulates in a way that predicts the monkey's
eye movement response (Shadlen and Newsome, 1996,
2001). The gradual
evolution of activity during motion viewing and its dependence on the
difficulty of the discrimination suggests that neurons in LIP might
represent the accumulation of visual information about motion leading
to the formation of the monkey's decision.
To characterize neural activity as decision related, the period of
decision formation must be clearly defined. In previous experiments,
monkeys were given a fixed amount of time to view the motion stimulus
and decide its direction. The actual moment when the monkey committed
to a behavioral response was not known, so one could not distinguish
neural activity associated with decision formation from activity that
represented the planned eye movement. We therefore trained monkeys to
perform the same task, but to respond as soon as a decision was made.
The combination of threshold psychophysics with a reaction time
measurement allowed us to examine both the accuracy of the monkeys'
perception and the time required to reach a decision. Near
psychophysical threshold, we were able to record neural activity during
an extended epoch of decision formation, which was determined on each
experimental trial. We found that the activity of LIP neurons modulated
as the monkey viewed the motion stimulus, predicting both the
perceptual decision and the time required to reach it.
Parts of this work have been published previously in abstract form
(Roitman and Shadlen, 1998).
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MATERIALS AND METHODS |
We recorded from 54 neurons in the LIP of two rhesus monkeys
(both female, 4.5-5 kg) trained to perform a reaction-time
direction-discrimination task. Each monkey was implanted with a
head-holding device, a recording cylinder for transdural introduction
of electrodes (Crist Instruments, Damascus, MD), and a scleral eye coil
for monitoring eye position (Fuchs and Robinson, 1966;
Judge et al., 1980). For each recording session, a
plastic grid that held a sterile guide tube through which a tungsten
microelectrode was passed was secured in the recording chamber. Signals
from the electrode were amplified, filtered, and passed to a dual
voltage-time window discriminator (Bak Electronics, Germantown, MD) to
discriminate action potentials from a single neuron. A record of the
time of each action potential, marked to the nearest millisecond, as
well as events occurring in the trial were stored on a PC computer for
off-line analysis (Hays et al., 1982). Horizontal and
vertical eye positions were also measured (C-N-C Engineering) and
stored to disk for analysis (250 Hz). All surgical and animal care
methods conformed to 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.
Neurons were selected according to anatomical and physiological
criteria. Postoperative MRI was used to identify LIP and to direct the
placement of recording electrodes within the recording chamber. The
three coronal images in Figure 1 show the
recording locations from one of our monkeys. The arrowheads border the
sites where neurons were recorded. A minority of neurons (5-10%)
studied in this monkey may have been located within the histological
boundaries of the lateral occipitoparietal zone (in
"A"), whereas most of the neurons were located in LIPv
("B" and "C") (Lewis and Van Essen, 2000). Locations from the second monkey were nearly
identical to the images in Figure 1, B and
C.
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Figure 1.
Representative MRI of recording sites from one
monkey. The coronal images show the area of the intraparietal sulcus
(ips) studied in these experiments. The recording grid can
be seen above the ips. The arrowheads next to the ips
represent the boundaries of the locations from which neurons were
recorded. Images were obtained using STIR acquisition in 1.5T
using carotid RF coils adjacent to the head. Slices are centered 1.5 mm
apart (3 mm thickness). The diagram is a lateral view of the
brain showing the location of the corresponding coronal
images.
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We used a memory-guided saccade task to select LIP neurons that were
active during saccade planning (Hikosaka and Wurtz,
1983; Gnadt and Andersen, 1988). The monkey
fixated a central point while a target appeared briefly (200 msec) in
the periphery. After a delay of 1-2 sec, the fixation point was
extinguished, and the monkey made a saccade to the remembered location
of the target. By varying the location of the target from trial to
trial, we identified the location of the visual field that
caused a sustained response in the LIP neuron during the delay
period, termed the response field (RF). This location determined the
position of one of the choice targets in the direction discrimination
task (range, 4.5-15° eccentric).
Direction discrimination task. Monkeys performed a
single-interval, two-alternative forced-choice direction-discrimination task (Fig. 2). They were tested in two conditions: reaction time (RT)
and fixed duration (FD). The conditions shared the following features.
A trial began when the monkey fixated a central fixation point. Two
choice targets then appeared, with one located in the RF of the neuron
under study (T1) and the other located in the opposite hemifield (T2).
The random-dot motion stimulus appeared in a 5°-diameter aperture
centered at the fixation point. The stimulus was presented on a
computer monitor with a frame rate of 75 Hz. A set of dots was shown
for one video frame and then replotted three video frames later. When
replotted, a subset of dots was offset from their original location to
create apparent motion while the remaining dots were relocated
randomly. Therefore, the first pairing of coherent dots occurred after
three video frames (40 msec), although random motion was present by the
second video frame (13 msec). The net direction of motion was toward one or the other choice target. Both the direction and strength of
motion (the percentage of coherently moving dots) were chosen randomly
on each trial. The monkey's task was to decide the direction of motion
and to indicate its decision with a saccadic eye movement to the
appropriate choice target. The monkey received a liquid reward for
correct choices and was rewarded on half the trials that used 0%
coherent motion. Errors were followed by a short time out (extra 750 msec added to the intertrial interval).
In the RT task (see Fig. 2A), the choice targets were
displayed for a variable interval before the onset of the motion
stimulus. The duration of this prestimulus interval was randomly
selected from an exponential distribution (mean = 700 msec). This
randomization served to discourage anticipation of the onset of the
motion stimulus. We found that randomization of the prestimulus
interval in this fashion was essential for training on the RT task.
When the onset of the stimulus was predictable, RT was faster than in
these experiments and varied less across the range of coherences [see
also Green et al. (1983)]. Once the motion stimulus began, the monkey
was free to indicate its choice at any time. When the computer detected a break in fixation, the random dots were extinguished. If the monkey
made a saccade to either choice target, the trial was scored as correct
or incorrect. Breaks in fixation that were not associated with an
immediate saccade to a choice target were rare and are not included in
our data set.
In the FD task (Fig. 2B), the choice targets were
displayed for 700 msec followed by the appearance of the random-dot
motion stimulus. The monkey then viewed the motion stimulus for 1 sec. This was followed by a random delay of 500-1500 msec, followed by the
disappearance of the fixation point, which cued the monkey to report
its choice of direction. Until then, the monkey was required to
maintain fixation within a window of ±0.5° (monkey N) or ±1.5°
(monkey B).
The RT and FD tasks were conducted in alternating blocks consisting of
10-40 trials at each of the six levels of motion strength. The monkeys
were informed of the task condition by the color of the fixation point,
which was red for FD and blue for RT. Note that in the RT trials, the
monkey could take as little or as much time as it needed to make its
decision. However, the reward was withheld for a minimum of 800 msec
(monkey B) or 1200 msec (monkey N) after onset of random-dot motion, no
matter how quickly the monkey indicated its choice. This strategy
created an incentive to respond within ~1 sec of motion onset, but no
incentive to go any faster. On each trial we obtained a measurement of
the monkey's direction judgment and, on the RT version of the task, the amount of time taken to achieve it. We refer to the period from
onset of random-dot motion to saccade initiation as the reaction time.
Analysis of behavioral data. For each experiment, the
monkey's sensitivity to motion was estimated by plotting the
probability (p) of a correct choice as a function of
motion coherence (C). The accuracy data were fit by a
cumulative Weibull function (Quick, 1974):
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(1)
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using a maximum likelihood fitting procedure. The discrimination
threshold,  is the coherence level at which the monkey would make
82% correct choices. The second parameter,  , describes the slope of
the psychometric function.
Analysis of neural data. All physiological data reported in
this paper were acquired from trials in which the monkeys completed the
direction-discrimination task by choosing one of the two choice targets. Spike times were recorded to 1 msec precision and aligned to
events in the trial. In the RT task, there are two relevant time
scales: one that relates the time of spikes to the onset of random-dot
motion, the other to initiation of the saccadic eye movement response.
Unless indicated otherwise, when reporting data aligned to the onset of
motion, we exclude all spikes that occurred from 100 msec before the
saccadic eye movement. Therefore, the averages shown on the left
portion of Figures 7A, 11, and 12 exclude any perisaccadic enhancement
that is commonly observed in LIP neurons (Barash et al.,
1991a). Similarly, when analyzing data with respect to saccade
initiation, we exclude all spikes occurring within 200 msec after onset
of random-dot motion. This precludes any activity associated with
stimulus onset from the averages. These manipulations are important
when analyzing the RT data because events with fixed latency to
stimulus onset occur with variable time with respect to the saccade,
and vice versa.
We use various regression tests to evaluate the role of motion strength
and other factors on the neural response. In general, these regression
models provide a convenient test of the null hypothesis that the factor
in question does not affect the measured response. They also offer a
sensible way to combine data from several neurons: unless indicated
otherwise, the regression models make use of an identifier variable,
Iu (also known as a dummy variable), to indicate
neuron identity. This maneuver adjusts for differences in overall
response level between neurons and allows the measurement from each
neuron to affect the regression fit with leverage commensurate to its
reliability. Unless indicated otherwise, all fits were obtained using
weighted least squares (Draper and Smith, 1966), which
furnishes maximum likelihood estimates of the fitted coefficients and
their confidence intervals under the assumption that variability ( )
is distributed as multivariate Normal. Here we list the specific
regression models with minimal explanation so as to complement the
development in Results.
Effect of motion strength, RT group, and task during specific
epochs. To determine whether the neural response was affected by
the strength of motion at a particular time (see Figs. 6, 7, and 9), we
fit:
|
(2A)
|
where Yt is the spike rate in the epoch
(as specified in the text), C is the coherence level from
that trial (0-0.512), Iu is the dummy variable
that identifies the neuron (Iu = 1 when u = n and 0 otherwise), and is random error (see
above). i represents the fitted coefficients
(1,u is a vector of fitted constants, one per
neuron), estimated using weighted least squares. Responses for trials
in which the monkeys selected T1 or T2 correctly were analyzed
separately. In Equation 2A, the fitted value for 2 and
its confidence interval (CI) furnishes an estimate of the change in
response per 100% coherence. The null hypothesis is that motion
strength does not affect the level of LIP activity (H0: 2 = 0), which we
evaluate using an F test for nested models (Draper
and Smith, 1966).
We used the same strategy to determine whether the response was
affected by RT during an epoch before saccade initiation (see Fig. 8).
Instead of motion strength, we tested the effect of RT on spike rate,
measured at designated times:
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(2B)
|
where Tgroup is the median RT for the
group of trials used to generate the response functions in Figure
8A.
We obtained data from both the FD and RT versions of the task in a
subset of neurons (see Fig. 10). For these neurons we were interested
in whether the responses differed according to the behavioral paradigm.
For specific epochs during the trials, we expanded Equation 2A to
incorporate the effect of task type:
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(2C)
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using the same conventions as above.
Itask equals 1 or 0 for RT and FD trials,
respectively. Any difference in activity between the two paradigms is
estimated by 3 and its confidence interval. The null
hypothesis that the choice of paradigm does not influence the level of
the neural response is tested by setting 3 = 0.
To examine whether the buildup and decline of activity during motion
viewing differs during correct and error trials (see Fig. 11), we
modified Equation 2C to compare correct and error trials:
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(2D)
|
where Icorrect equals 1 or 0 for correct
and error trials, respectively. The analysis excludes all 0% coherent
motion trials and is performed separately for T1 and T2 choices. The
null hypothesis (H0: 3 = 0)
is that the response is not affected by whether the target was chosen
correctly (or equivalently, that the response is not affected by the
direction of motion).
We were also interested in whether the response recorded in particular
epochs was related to the amount of time it took the monkey to make its
choice (see Figs. 12, 13). For each neuron, all correct trials for each
coherence level in which the monkeys had chosen T1 were sorted into a
"short" or "long" group on the basis of the RT on that trial.
To test the effect of RT group, Equation 2A was expanded to:
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(2E)
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using the same conventions as above. In this equation,
IRT equals 1 for trials in the short RT group
and 0 otherwise. 3 estimates the difference in spike
rate that can be attributed to RT group. The null hypothesis is that RT
group does not affect the response (H0:
3 = 0). This analysis was also performed on data from experiments in which identical random-dot patterns were presented on half of the trials. Equation 2E was expanded to:
|
(2F)
|
where Ip identifies each unique pattern
of random dots and 0,p is a list of constants
associated with each pattern. The same null hypothesis
(H0: 3 = 0) allows us to
examine the possibility that any difference in response associated with
short versus long RT is explained by motion strength and differences in
the particular sequence of random dots.
Time course of response. To examine the effect of motion
strength on the time course of the neural response, we modeled spike rate as a linear function of time and examined the effect of motion strength on the slope of these lines. For these analyses, spike rate
(Y) was measured in 20 msec bins, and time (T)
denotes the center of the bin. The effect of random-dot coherence
(C) on time course of the response can be estimated by
fitting:
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(3A)
|
where T × C is the term describing the
interaction between time and motion strength. Responses for trials in
which the monkeys selected T1 or T2 correctly were analyzed
separately. The interaction term instantiates the possibility that the
time-dependent change in spike rate is affected by motion strength.
Therefore, the null hypothesis is that 4 = 0.
We also were interested in whether the neural response changed as a
function of time early in the trials even when RT was long. Trials were
selected according to choice (T1 or T2 correct) and RT (for example,
700-799 msec; see Fig. 8), permitting a reduction of Equation 3A
to:
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(3B)
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2 is the average slope of the response during
this interval. The null hypothesis is that there is no time-dependent
change in response (H0: 2 = 0).
For correct T1 choices, we tested whether the time course of the neural
response was related to the amount of time it takes the monkey to make
its choice (see Figs. 12 and 13 and Eq. 2E). For each coherence level,
the response was estimated for each RT group with a modification of
Equation 3A:
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(3C)
|
using the same conventions as above. The null hypothesis is that
RT group does not affect the slope of the response
(H0: 4 = 0). The slope of
each response function was estimated separately for short- and long-RT
groups (using Eq. 3B).
Saccade metrics. We were interested in whether the
variability in the neural response during motion viewing could be
accounted for by variability in the eye movements themselves. To test
this, we first determined whether saccade metrics were affected by the strength of the motion stimulus. For each saccade, we measured its
maximum velocity (Vmax), average
velocity ( ), duration
(Tdur), amplitude (A),
reaction time (RT), and accuracy (ACC, 1/distance of saccade endpoint from target, divided by target
eccentricity). We tested each parameter to determine whether it varied
with coherence for each direction of motion, using the model:
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(4A)
|
where S is the value for
Vmax, ,
Tdur, A, RT, or ACC. The null
hypothesis is that motion strength does not affect the saccade parameter (H0: 2 = 0).
We then tested whether these saccade parameters could explain the
apparent relationship between spike rate and motion stimulus strength.
Equation 2A was expanded to incorporate the potential confounding
variables:
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(4B)
|
where C is motion strength and
Yt is the response on each trial during the 40 msec epoch ending at the median RT for 51.2% coherence trials. Only
saccade metrics that varied as a function of coherence level are
included. The null hypothesis is that motion strength does not affect
the level of LIP activity once saccade metrics are known
(H0: 6 = 0).
Response on single trials. We used a maximum likelihood
procedure to estimate the rate of increase in the spike rate from single trials, using spikes from 200 msec after dots onset until 100 msec before saccade initiation. Only correct T1 choices were included
in this analysis. The spike rate,  (t), was approximated as a line:
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(5)
|
We solved for values of 0 and k that
maximize the likelihood of obtaining the observed spike train assuming
that spikes are emitted in accordance with a nonstationary Poisson
point process parameterized by (t). The procedure
furnishes for each trial an estimate of the change in spike rate per
unit time (k, which we term ramp-slope) along with its
standard error. The latter is obtained by inverting the 2 × 2 Hessian matrix of second partial derivatives of the error function
(minus log likelihood) with respect to 0 and
k.
To evaluate the relationship between the ramp-slope and RT, we used
weighted multiple regression to factor out the influence of cell
identity and coherence on ramp-slope:
|
(6)
|
where k is the ramp-slope for each trial (from Eq. 5), C is motion coherence, and RT is the reaction
time. As above, 1,u represents a list of
constants, one per neuron. When analyzing data from just one neuron,
this term is replaced by a single constant that represents the average
ramp-slope independent of motion strength and RT. The change
in ramp-slope that is related to RT is estimated by
3. The units are spikes per second squared per second
(i.e., spikes per second cubed). The null hypothesis, that there is no relationship between the neural activity and RT, is tested
by setting 3 = 0.
Because behavioral and physiological results were similar in the two
monkeys (see Fig. 6, Table 1), all population analyses were performed
on combined data from both monkeys.
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RESULTS |
We recorded from 54 LIP neurons in two rhesus monkeys while they
performed a direction discrimination task (Fig.
2). We will first show how the speed and
accuracy of the monkeys' direction decisions depended on motion
strength. Then we will examine the activity of LIP neurons during the
period of decision formation. Finally, we will expose a weak
relationship between RT and the activity of LIP neurons on single
trials.
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Figure 2.
Direction discrimination tasks. Monkeys
discriminated the direction of motion in a dynamic random-dot display.
The color of the fixation point signified whether the
experiment follows a reaction time or fixed duration protocol, which
were conducted in separate blocks. A, Reaction time version.
After fixation, two choice targets appeared in the periphery. One of
the targets was within the response field (RF) of the
neuron, indicated by the gray shading. After a variable
delay period, dynamic random dots appeared in a 5° diameter aperture.
The fraction of coherently moving dots and the direction of motion,
toward one of the choice targets, were selected at random from a
predetermined list of values. The monkey was allowed to make a saccadic
eye movement to a choice target at any time after onset of random-dot
motion to indicate the direction of perceived motion. A liquid reward
was administered for choosing the correct target in the direction of
motion and on half the trials in which there was no coherent motion.
See Materials and Methods for additional details. Reaction time
(RT) is defined as the interval from motion onset to
saccade initiation. B, Fixed duration version. After the
monkey fixated the central point, two choice targets appeared for 700 msec. The monkey maintained fixation through a 1 sec motion-viewing
period followed by a variable duration memory delay. When the fixation
point was extinguished, the monkey reported its judgment by making an
eye movement to a choice target.
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Speed and accuracy of direction judgments
Performance accuracy on the RT and FD tasks depended on the motion
strength of the stimulus. Accuracy data from a single recording session
are shown in Figure 3A. The
monkey's performance varied from chance (50% correct, data not
shown) to perfect discrimination as the motion strength increased from
0 to 51.2% coherence. The fitted psychometric function revealed a
threshold (RT) of 6.3% coherence
motion and a slope (RT) of 1.7 (see
Materials and Methods, Eq. 1). This level of performance on the RT task
was comparable with the performance on alternating blocks of trials in
which the random dots were viewed for a full second (Fig.
3A, dashed curve) (FD = 7.5% coherence; FD = 1.5). The
psychometric functions obtained from FD and RT trials in this
experiment were not statistically different (p = 0.49; likelihood ratio test).
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Figure 3.
Behavioral data from one experiment. A,
Psychometric functions from RT and FD versions of the direction
discrimination task. RT and FD tasks were performed in alternating
blocks of ~60 trials. The probability of a correct direction judgment
is plotted as a function of motion strength and fit by sigmoid
functions (see Materials and Methods, Eq. 1). Vertical lines
indicate psychophysical thresholds ( in Eq. 1): the motion strength
that would support 82% correct choices (horizontal dashed
line). B, Effect of motion strength on reaction time.
Mean RT (± SEM) was obtained from correct trials in the experiment in
A (RT block). The line is a least squares
regression of RT versus log motion coherence (p < 0.001).
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Overall, the accuracy of the direction discrimination was not
compromised when tested in the RT condition (Table
1). For 38 experiments in which we
obtained data on both the FD and RT tasks, the ratio of thresholds
(RT/FD) was <1
(geometric mean = 0.74, 95% CI: 0.63-0.87; p = 0.002, paired t test after log transform). Thus when given
the freedom to respond when ready, the monkeys took the time needed to
perform the task slightly better than the level that they achieved with
a full second of stimulus viewing.
The amount of viewing time required to achieve such performance varied
inversely as a function of motion strength. Figure 3B shows
the mean RT for correct choices in the same experiment depicted in
Figure 3A. RT varied from 350 ± 9 msec (mean ± SEM) for the strongest motion (51.2% coherence) to 876 ± 35 msec
for the weakest motion strength. Summary statistics for all 54 experiments are provided in Table 2. RT
tended to be slightly faster for T1 choices than T2 choices, presumably
because of the extensive practice given the monkey during RF mapping.
The distributions of RT associated with any one condition tend to
exhibit positive skew. Various statistics have been advanced to
describe the moments and shape of the RT distribution (Luce,
1986; Carpenter and Williams, 1995;
Ratcliff and Rouder, 1998). For the present purposes, we ask the reader to consider the values in Table 2 as indicators of
central tendencies. A full account of the distribution of RT will be
reported separately (Ditterich et al, 2001).
The rapid reaction times accompanying strong motion indicate that the
monkeys did not procrastinate in reporting their decisions once
attained. This is remarkable considering that there was no incentive to
respond any faster than 800 or 1200 msec (depending on monkey). In
contrast, on the weaker motion strengths, the monkeys often took more
than 1 sec to reach a decision (e.g., in 20% of the trials at 3.2%
coherence) (Table 2). Presumably, the ability to view a difficult
stimulus for a longer time accounts for the small difference in
performance on the RT and FD tasks. Overall, the pattern of results
indicates that the monkeys took about as long as required to achieve
the level of performance to which they had grown accustomed on the 1 sec FD task. Although we did not attempt to manipulate the tradeoff
between speed and accuracy in these experiments, one of the monkeys
tended to respond with short latencies when first exposed to the RT
task. We observed that this monkey's performance improved over weeks
as it took more time to respond to weaker stimuli. This observation
suggests that the additional viewing time on difficult trials was
devoted to solving the task, consistent with previous studies
(Gold and Shadlen, 2000). The important point is that
the RT task allows us to deduce the window of time corresponding to
decision formation on each trial, before the monkey is committed to a
particular behavioral response.
Neural response during direction discrimination task
The direction of motion and the location of targets in the
discrimination task were arranged so that the activity of the neuron under study would indicate the monkeys' decisions. One of the choice targets (T1) was in the response field of the neuron; the other
choice target (T2) was placed in the opposite hemifield. The random-dot
motion was in a 5° aperture centered at the fovea and was directed
toward either T1 or T2. The monkey was trained to interpret such motion
as an instruction to make an eye movement to the corresponding target.
On the basis of our selection criterion (Materials and Methods), we
expected neurons to respond more when the monkey planned an eye
movement to the target in its RF (Shadlen and Newsome,
1996, 2001). The
RT version of the task allowed us to examine the neural activity in the
time frame of the monkey's decision formation.
Neural activity increased during the period of decision formation when
the monkey's judgment resulted in an eye movement to the choice target
in the RF (T1) but not before an eye movement to the other target (T2).
Figure 4 illustrates the responses
obtained in the RT condition of the same experiment as Figure 3. When
motion was strong, the monkey made its decision rapidly, and the
response modulation was apparent for only ~150 msec before saccade
initiation (Fig. 4, top, 51.2% coherence).
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Figure 4.
Response of an LIP neuron during the
RT-direction-discrimination task. Data were obtained from the block of
RT trials depicted in Figure 3. Only correct choices at two motion
strengths are shown. The diagram at the top indicates
whether the monkey's behavioral response was an eye movement into or
out of the response field (gray shading). Spike
rasters and response histograms are aligned to the beginning of the
monkey's eye movement response (sac). Carets
denote the onset of random-dot motion. Trial rasters are sorted by RT.
The monkey took longer to decide the direction of the weaker (6.4%
coherent) motion. Notice the buildup and attenuation of activity that
occurred during motion viewing (spike histogram binwidth = 20 msec). Spikes/s, Spikes per second.
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When the motion was weaker, the time required to reach a decision was
prolonged. As shown in Figure 4 (bottom, 6.4%
coherence), the response of the neuron began to increase several
hundred milliseconds before the execution of the saccade to the target
in the RF. The example exposes a dividend of the combined RT
threshold-discrimination task. When the task is easy, it is difficult
to interpret the neural data because the sequence of events from
stimulus onset to saccade execution occurs within a short time frame.
However, when the task is more difficult, the decision is formed over a prolonged period, which can be differentiated from the immediate preparation of an eye movement.
A similar pattern of activation was evident during blocks of trials in
which the monkey viewed motion for a fixed duration. Figure
5 illustrates the responses obtained in
the FD condition of the same experiment as Figure 3. The activity
increased during the period of motion viewing on trials in which the
monkey judged the direction as toward the RF, and this change in
activity persisted through the delay period until the go signal. When
the monkey judged the direction of motion as away from the RF, the
activity decreased and remained suppressed through the delay period.
This pattern of persistent activity during the delay period helps to distinguish LIP neurons from visual sensory neurons like those in area
MT (Seidemann et al., 1998). Our hypothesis is that the buildup and attenuation of activity during motion viewing
represents formation of the monkey's decision about direction
(Shadlen and Newsome, 2001). To test this, it is
necessary to discern whether the activity modulates in the time period
that the monkey uses to reach a decision. This is not possible using
the FD version of the task because there is no way to tell when the
monkey has reached its decision.
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Figure 5.
Response of an LIP neuron during the
FD-direction-discrimination task. Data were obtained from the block of
FD trials depicted in Figure 3 (same neuron as Fig. 4). The diagram at
the top indicates whether the monkey's behavioral response
was an eye movement into or out of the response field
(gray shading). Spike rasters and histograms are
aligned to two events in each trial. In the left portion of
each axis, the responses are aligned to the onset of motion,
which is then followed by a 1 sec motion-viewing period. In the
right portion of the axes, the delay period
(del) response is shown aligned to saccade initiation
(sac). The break in the panel is
attributable to the variable length of the delay period. The elevated
spike rate accompanying T1 choices was more pronounced for the easier
(51.2% coherent) motion (spike histogram binwidth = 20 msec).
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The chief advantage of the RT task is that we can examine the neural
activity during the period of decision formation, before the monkey is
committed to an eye movement response. In particular, we may ask
whether the stimulus has an effect on the neural activity that cannot
be accounted for by the initiation of the saccade. We examined the
spike rate as a function of motion strength during a short epoch after
the onset of random-dot motion (Fig. 6).
We chose for this analysis a 100 msec epoch ending at the median RT for
the strongest motion strength, thus permitting us to estimate spike
rate from at least half of the trials at every coherence level. Figure
6A shows the spike rates associated with T1 and T2
choices for the neuron depicted in Figure 4, plotted as a function of
motion strength and fit by a line. For this neuron, the average response associated with T1 choices increased by 42.2 spikes per second per 100% coherence (CI: 14.6-69.9; p < 0.001) (Eq. 2A, H0: 2 = 0),
indicating a profound effect of stimulus strength on the buildup of
activity during motion viewing. When the monkey chose T2, there was a
decrease of  16.5 spikes per second per 100% coherence (CI: 29.0 to
4.0; p < 0.001) (Eq. 2A, H0:
2 = 0).
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Figure 6.
Effect of motion strength on LIP response during
decision formation. Graphs depict the effect of stimulus strength on
single neuron activity for trials ending in the same eye movement. All
data are from correct choices in the RT version of the discrimination
task. A, Effect of motion strength on firing rate for the
neuron shown in Figures 3 and 4. Spike rate (mean ± SEM) was
measured in the same 100 msec epoch for each motion strength. The epoch
was chosen to end at the median RT for trials using the strongest
(easiest) motion strength. Lines are weighted least squares
fits (Eq. 2A) performed separately for T1 and T2 choices (T1,
filled symbols, solid line; T2, open
symbols, dashed line). The activity of this neuron
increased 42.2 spikes per second per 100% coherence for T1 choices
(CI: 14.6-69.9) and decreased 16.5 spikes per second per 100%
coherence for T2 choices (CI: 29.0 to 4.0). B, Effect of
motion strength on firing rate for each of the neurons in our data set.
For each neuron, the change in firing rate per 100% coherence was
estimated by the slope of the best fitting line as in A. Results are shown separately for T1 and T2 choices and for each monkey.
Shading indicates p < 0.05 (Eq. 2A,
H0: 2 = 0). Means are shown
by arrows (from top to bottom,
30.9 ± 8.6, 22.2 ± 7.3, 12.5 ± 4.6, 22.9 ± 6.2; differences between monkeys were not significant;
p = 0.46 and 0.17 for T1 and T2 comparisons,
respectively; t test). sp/s, Spikes per second;
coh, coherence.
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We performed this analysis on each of the neurons in our sample. For
each neuron, we used this regression procedure to estimate the change
in spike rate per 100% coherence on trials that would ultimately
culminate in T1 or T2 choices. These values are displayed for each
monkey in the histograms in Figure 6B. For the
population of neurons studied, the modulation of the response during
motion viewing was related to motion strength. Arrows
indicate the average change in firing rate for each monkey. The
dependence of the response on coherence during motion viewing was found
across the population of neurons studied and was similar in the two monkeys.
The evolution of activity accompanying motion viewing furnishes insight
into the neural basis of decision formation. Figure 7A shows the averaged
responses of 54 neurons recorded while monkeys performed the RT task.
On the left half of the graph, the responses are aligned to
the onset of random-dot motion. They show activity accompanying motion
viewing and exclude any perisaccadic response. On the right
half of the graph, the responses are aligned to the onset of the
eye movement response. They show activity leading to the
monkeys' behavioral response and exclude any response
modulation that accompanies onset of random-dot motion. The next three
paragraphs focus primarily on the left portion of the graph,
during the period in which the monkey is viewing random-dot motion but
has not committed to a choice.
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Figure 7.
Time course of LIP activity in the
RT-direction-discrimination task. A, Average response from
54 LIP neurons. Responses are grouped by motion strength and choice as
indicated by color and line type. The responses
are aligned to two events in the trial. On the left,
responses are aligned to the onset of stimulus motion. Response
averages in this portion of the graph are drawn to the median RT for
each motion strength and exclude any activity within 100 msec of eye
movement initiation. On the right, responses are aligned to
initiation of the eye movement response. Response averages in this
portion of the graph show the buildup and decline in activity at the
end of the decision process. They exclude any activity within 200 msec
of motion onset. The average firing rate was smoothed using a 60 msec
running mean. Arrows indicate the epochs used to compare
spike rate as a function of motion strength in the next panels.
Arrows a and b mark the 40 msec epoch ending at
the median RT for 51.2% motion trials (370-410 msec after stimulus
onset); arrows c and d mark the 40 msec epoch
ending 30 msec before saccade initiation. Only correct choices are
included in these graphs for motion coherences >0%. B,
Effect of motion strength on firing rate during decision formation.
Response averages were obtained from 54 neurons in the 40 msec epochs
corresponding to arrows a and b above. When
motion was toward the RF (solid line; epoch a),
the spike rate increased linearly as a function of motion strength.
When motion was away from the RF (dashed line; epoch
b), the spike rate decreased as a function of motion
strength. Note that, for the 0% coherence stimulus, there was no net
direction of motion, but the activity was greater when the monkey chose
the T1 direction. Symbols represent weighted means ± SEM. Lines are weighted least squares fits to Equation 2A
(*p < 0.05; H0:
2 = 0). C, Effect of motion
strength on firing rate at the end of the decision process. Response
averages were obtained from 54 neurons in the 40 msec epochs
corresponding to arrows c and d. The large
response preceding eye movements to the RF (solid line,
filled circles; arrow c) did not depend on the
strength of motion. Responses preceding eye movements away from the RF
were more attenuated with stronger motion stimuli (dashed
line; arrow d). Use of weighted means in B
and C introduces small discrepancies from averages indicated
by arrows in A.
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Before the onset of motion, there was a modest level of activity
attributable to the presence of one of the choice targets in the RF of
the neuron. Approximately 90 msec after the onset of random-dot motion,
there was a transient dip and recovery in the activity lasting ~100
msec that did not depend on the strength of motion (indicated by
color) or the monkey's choice (solid or dashed lines). The activity then increased or decreased in a
manner that reflected the strength of motion and the monkey's ultimate choice. On trials that culminated in T1 choices (direction judged as
toward the RF), the activity increased in a ramp-like fashion. The rate
of growth in the response was largest for the strongest motion and
smaller as the coherence decreased. The slopes of the functions ranged
from 21.5 spikes per second squared to 88.8 spikes per second
squared as motion strength was varied from 0 to 51.2% coherence (CI:
16.5-26.6 and 49.2-128.3, respectively; p < 0.0001) (Eq. 3A, H0: 4 = 0). The
effect of motion strength on the rate of change was statistically
significant for both monkeys (increase of 59.3 and 40.7 spikes per
second squared from 0 to 51.2% coherence for monkeys N and B; CI:
51.3-67.3 and 31.5-50.0, both p < 0.0001) (Eq. 3A,
H0: 4 = 0). A similar
pattern was apparent in the declining responses that accompanied T2
choices. For T2 choices the responses were less ramp-like but tended to
drift toward lower rates in a manner that also depended on the strength
of motion. This inverse relationship between the rate of change in
neural response and motion strength seen in Figure 7A was
also reliable (p < 0.0001) (Eq. 3A,
H0: 4 = 0).
Importantly, the ramp-like modulation in discharge accompanied motion
viewing and was not an immediate antecedent to the saccadic eye
movement. The response averages shown in the left half of Figure 7A are drawn to the median reaction time and do not
include any activity in the 100 msec preceding saccade initiation.
These curves therefore exclude any enhancement (or attenuation) that occurred just before the eye movement. They reveal clear differences in
the neural processing that accompanied the formation of difficult versus easy decisions. It will prove useful for comparison to tabulate
the mean response in the 40 msec epoch denoted by arrows a
and b in Figure 7A. The mean responses (±SEM)
for each coherence level for both directions are shown in Figure
7B. These means, drawn from at least half of the trials at
all motion strengths, demonstrate a systematic dependency on motion
strength: an increase in activity of 38.4 spikes per second per 100%
coherence when motion was toward T1 (a, CI: 23.9-52.8 spikes per
second; p < 0.001) (Eq. 2A, H0:
2 = 0) and a decrease of 29.9 spikes per second per 100% coherence when motion was toward T2 (b, CI: 50.2 to
9.6 spikes per second; p < 0.01) (Eq. 2A,
H0: 2 = 0).
Because many LIP neurons modulate their activity in relation to eye
movements (Barash et al., 1991a; Colby and
Goldberg, 1999), it is natural to ask whether the effect of
motion strength on neural activity could be explained by differences in
the monkeys' eye movement responses. Except for RT, however, the
saccade parameters that we measured did not vary substantially as a
function of motion strength. Saccadic duration and accuracy did not
show any dependency on motion strength (p > 0.05) (Eq. 4A, H0: 2 = 0).
The higher coherence stimuli were associated with saccades that were
slightly slower and shorter, but these effects were very small
(~3%); they did not constitute violations of the main sequence
(Fuchs, 1967). Of course, RT was 53.4% faster across
the range of motion strengths (p < 0.0001) (Eq. 4A, H0: 2 = 0). To test
whether such variation could explain the effect of motion strength on
LIP activity, we incorporated the saccade parameters into the
regression analysis used to model the responses at
arrows a and b in Figure 7 as a function of
motion strength. We found that even when the saccade parameters were
incorporated, the fit was improved significantly by including the
coherence level of the trial (p < 0.001) (Eq. 4B, H0: 6 = 0). We are
therefore confident that LIP neurons reflect the strength and direction
of the motion in a way that is not explained by differences in saccade metrics.
In fact, by the time the monkey was committed to a particular eye
movement response, differences in neural activity attributable to
motion strength were substantially attenuated or absent. This is shown
by the solid curves on the right half of Figure
7A, in which the activity was aligned to the monkeys' eye
movement responses. On the trials in which the monkeys chose the T1
target (in the RF), the response achieved a common value of 68.6 ± 0.9 spikes per second in the epoch 30 to 70 msec before the
saccades, indicated by arrow c. The mean responses for each
coherence level during this epoch are shown in Figure 7C. In
this interval (c), the neural response was not affected by
motion strength (1.1 spikes per second per 100% coherence; CI: 3.2
to 5.4; p = 0.46) (Eq. 2A, H0:
2 = 0).
The responses preceding T2 choices (outside the RF) also exhibited a
precipitous change before saccade initiation, but unlike the T1
choices, the responses remained associated with motion strength. For
example, in the 40 msec epoch indicated by d in Figure 7,
A and C, there was a decrease of 17.5 spikes
per second per 100% coherence (CI: 36.7 to 1.6; p < 0.05) (Eq. 2A, H0: 2 = 0). The inverse relationship between spike rate and motion
strength was evident throughout the time course. The decline in spike
rate is consistent with mounting evidence against a T1 choice, but in
contrast to the conditions favoring a T1 choice, there is no common
spike rate value that precedes the onset of the saccade.
The gradual change in LIP activity leading up to the monkey's choice
is better appreciated by examining trials sharing the same duration. In
Figure 8A, we have
grouped the trials by the monkey's RT, using T1 choices from all
motion strengths. Each curve shows the average spike rate from sets of
trials that end within 25 msec of each other, for example 400-425
msec, plotted as function of time from the saccade. Unlike Figure
7A, the averages shown do not exclude any spikes between
stimulus onset and saccade. Trials ending in short RT follow a steeper
trajectory than those ending in long RT. For example, in the epoch from
200 msec after motion onset to 100 msec before saccade, the firing rate
increased 64.1 spikes per second squared for the 500 msec RT group and
25.7 spikes per second squared for the 900 msec RT group (CI:
34.4-93.9 and 14.1-37.2; both p < 0.01) (Eq. 3A,
H0: 4 = 0). At the time just
before the saccade (same as c in Figure 7), there is no
dependence of the response on RT group (change of 5.5 spikes per second
across RT groups; CI: 26.7 to 37.8; p = 0.62) (Eq. 2B, H0: 2 = 0). Just 40 msec
earlier, the same epoch revealed a dependency on reaction time. As
shown in Figure 8A, shorter RT was associated with lower spike rates, consistent with the steeper rise of response (38.8 spikes
per second per second RT; CI: 2.1 to 75.0; p < 0.05). The stereotyped activity in the last 50 msec of the trial could mark a
completion of decision formation and a commitment to an eye movement.
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Figure 8.
Time course of activity on trials with similar
reaction time. A, Population average responses for T1-choice
trials. The responses are aligned to saccade initiation.
Color designates the RT of the trials included in the
average, which fall within 25 msec of the time indicated (e.g.,
400-425 msec). All spikes are included in these averages
(n = 54 neurons). Average firing rate was smoothed
using a 60 msec running mean. B, The gradual change in spike
rate is evident hundreds of milliseconds before the monkey
discriminates motion. Trials with long RT were selected for a closer
examination of an early portion of motion viewing period, from 200 to
500 msec after motion onset. This epoch corresponds to the beginning of
the coherence-dependent response after the stereotyped dip after motion
onset and ends at least 200 msec before the saccadic response. Trials
are grouped by RT spanning a 100 msec range: 700-799, 800-899, and
900-999 msec, indicated by red, green, and
blue, respectively. Points show the average
firing rate, calculated in non-overlapping 40 msec bins. A
representative error bar (± 1 SEM) is shown for one data
point. Lines are weighted least squares fits (Eq. 3B)
performed separately for each RT group. Solid lines and
filled symbols correspond to T1 choices; dashed
lines and open symbols correspond to T2 choices. The
slope (2) estimates the change in firing rate as
a function of time. C, Response change as a function of time
during early motion viewing. Bars represent the slope from
the fits in B (error bars represent 95% confidence
intervals). The ability to detect linear trends in this epoch implies
that the ramp-like responses in Figure 7 do not arise as a consequence
of averaging responses that step from an intermediate level of firing
to a high or low rate once the decision is formed.
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Importantly, grouping the data by RT allows us to appreciate a gradual
change in spike rate for hundreds of milliseconds before the monkeys
initiated their eye movement responses. Figure 8B shows the responses in the epoch from 200 to 500 msec after motion onset (that is, subsequent to the transient dip) at the very beginning of the coherence-dependent portion of the response. The responses are
shown for the three longest RT groups (700-799, 800-899, 900-999 msec). Points represent the mean spike rate from 40 msec epochs and
were fit by lines using weighted regression. The slopes of these
regressions and their confidence intervals are shown in Figure
8C (p < 0.05 for all six fits) (Eq. 3B, H0: 2 = 0). The analysis
documents a time-dependent change in the spike rate accompanying motion
viewing during an epoch that ends 200-500 msec before initiation of
the eye movement response. This observation helps to exclude the
possibility that the gradual change in activity seen in previous figures could have resulted from averaging across trials containing more abrupt changes in activity as the monkey chose the T1 or T2 choice
target. Instead, we can be confident that the spike rate changes in a
gradual fashion many hundreds of milliseconds before the monkey is
committed to its choice.
The buildup and decline in spike rate also occurred in the FD version
of the task (Fig. 9A), where a
decision does not immediately lead to an eye movement. Early in the
motion-viewing period (left panel), the response modulated
in a ramp-like fashion. The mean responses at the time point marked by
the arrow a (corresponding to arrow a in Fig.
7A) are shown in Figure 9B (a). The
response associated with T1 choices increased 25.8 spikes per second
per 100% coherence (CI: 20.1 to 31.6 spikes per second;
p < 0.0001) (Eq. 2A, H0:
2 = 0). Likewise, in the same period, the
response level associated with T2 choices decreased 10.2 spikes per
second per 100% coherence (b in Fig. 9A,
B) (CI: 19.8 to 0.6; p < 0.05) (Eq. 2A,
H0: 2 = 0). The level of
response attained during the motion-viewing period reflected the
direction and strength of the motion stimulus, consistent with previous
reports (Shadlen and Newsome, 1996,
2001; Horwitz and
Newsome, 1999, 2001; Kim and Shadlen, 1999).
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Figure 9.
Time course of LIP activity in the
FD-direction-discrimination task. A, Average response from
38 LIP neurons. Responses are grouped by motion strength and choice as
indicated by color and line type. On the
left, responses are aligned to the onset of stimulus motion.
On the right, responses are aligned to initiation of the
saccadic response. Response averages in this portion of the graph show
activity during the delay period. Otherwise, we use the same labeling
conventions as in Figure 7A. Arrows indicate the
epochs used to compare spike rate as a function of motion strength in
the next panels. Arrows a and b mark the 40 msec
epoch from 370 to 410 msec after stimulus onset, corresponding to time
points from Figure 7; arrows c and d mark the
epoch ending 30 msec before saccade initiation. B, Effect of
motion strength on firing rate during motion viewing. Same conventions
as in Figure 7B. C, Effect of motion strength on
firing rate at the end of the delay period and shortly before the eye
movement response. Same conventions as Figure 7C. Motion
strength did not affect the level of spike discharge late in the delay
period.
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The effect of motion strength is apparent early in the delay period,
but by the end of the delay, the activity reflected only the monkey's
choice of saccade target. Responses preceding saccades to T1 bear
striking similarity to the enhancement seen in the RT task. Like the RT
data, there was a stereotyped increase in activity that did not vary
systematically with motion strength in the epoch 30-70 msec before the
saccade (c in Fig. 9A, C) (change of
1.9 spikes per second per 100% coherence; CI: 4.1 to 8.0; p = 0.37) (Eq. 2A, H0:
2 = 0). Unlike the RT data, neural activity was not affected by motion strength during the epoch 30-70 msec preceding the monkey's correct saccades to T2 (d in Fig.
9A, C) (9.4 spikes per second per 100%
coherence; CI: 28.1 to 9.3; p = 0.18) (Eq. 2A,
H0: 2 = 0). For both FD
and RT tasks, the activity depends on motion strength while the monkey
gathers evidence, and the activity becomes stereotyped once the monkey
is committed to a particular eye movement response. In the RT task,
this occurs ~50 msec before saccade initiation. In the FD task, this
occurs earlier and is presumably variable.
These qualitative similarities belie important quantitative differences
in the responses on the FD and RT tasks. Figure
10 compares the activity from 38 neurons from which we were able to obtain data using both FD and RT
tasks. In the epoch before onset of random dot motion (Fig.
10A), activity was on average 4.4 spikes per second
higher in the RT task (CI: 3.2 to 5.5; p < 0.0001)
(Eq. 2C, H0: 3 = 0). In
Figure 10B, the average responses during motion
viewing for T1 and T2 are shown for one coherence level (12.8%).
Across all motion strengths, the responses were 13.8 spikes per second
higher in the RT task for T1 choices (CI: 11.0 to 16.6;
p < 0.0001) (Eq. 2C, H0:
3 = 0). For T2 choices, the activity was 6.9 spikes per second higher in the RT task (CI: 4.2 to 9.6;
p < 0.0001), which is not substantially larger than the difference before fixation. In the epoch 30-70 msec preceding saccade initiation, responses were 9.6 spikes per second higher in the
RT task when the monkey chose T1 (Fig. 10C) (CI: 6.8 to 12.4; p < 0.00001) (Eq. 2C, H0:
3 = 0). In contrast, for saccades to T2, the
responses were not significantly different in the RT task, despite the
higher firing rate during fixation (CI: 8.1 to 0.6; p = 0.12) (Eq. 2C, H0: 3 = 0).
These observations suggest that both the offset and gain of the LIP
response is greater in the RT version of the task.
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Figure 10.
Comparison of neural activity on RT and
FD tasks. Scatter plots depict average spike rates from three
comparable epochs for 38 neurons studied in both tasks. A,
Response before onset of motion. Response averages are from the 40 msec
epoch before onset of random-dot motion, when choice targets and
fixation point are visible. All correct choices are included in the
response averages. B, Response during motion viewing.
Response averages are from the 100 msec epoch ending at the median RT
for the easiest motion strength (310-410 msec). Averages are
computed separately for T1 and T2 choices, as indicated. Only correct
choices accompanying 12.8% coherent motion are shown. C,
Response preceding eye movements. Response averages are from the period
30-70 msec before the eye movement response. Averages are computed
separately for T1 and T2 choices, as indicated. All correct choices are
included.
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The observations in Figures 7-9 are consistent with the idea that LIP
reflects the mounting evidence for or against a T1 choice. The evidence
is represented in the ramp-like activity during the period of
decision-making before the monkey is committed to a choice. This
period ends ~50 msec before the saccade in the RT task and some time
during motion viewing (or just after) in the FD task. The pattern of
activity in the RT task in particular suggests that the neurons are
accumulating some quantitydetermined by the strength and direction of
motiontoward a threshold, at which point the monkey is committed to
one action or another. Two additional observations support this
hypothesis. The first derives from an analysis of the trials in which
the monkeys chose the wrong direction of motion. The second involves a
closer look at the relationship between the neural response and the
monkeys' reaction times.
Errors
Near psychophysical threshold, monkeys often judged the direction
of motion incorrectly. Figure 11
compares the averaged responses on correct and error trials at two of
the weaker motion strengths. Regardless of the direction of random-dot
motion, LIP activity increased when the monkeys chose the target in the
RF and decreased when the target outside of the RF was selected.
However, the activity followed different trajectories on error trials.
When monkeys selected T1, the activity that occurred during motion
viewing increased less when the direction of motion was away from the RF (dashed gray curves). The response was 5.0 spikes per
second higher on correct T1 choices than on errors during stimulus
viewing (CI: 1.9 to 8.1; p < 0.05) (Eq. 2D,
H0: 3 = 0; response compared in 40 msec epoch ending at median RT for 51.2% coherence trials). When
monkeys chose T2, the spike rate was 5.1 spikes per second lower on
correct choices than on errors (CI: 6.1 to 4.1; p < 0.01) (Eq. 2D, H0: 3 = 0).
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Figure 11.
Comparison of errors with correct discriminations
on the RT task. The average responses from 54 LIP neurons are shown for
two weak motion strengths. Axes use the same conventions as
in Figure 7A. The diagrams shown to the right
indicate the direction of motion and the monkey's choice. The
colored curves show correct trials; the direction of motion
is toward the chosen target. These curves are replicas of
those shown in Figure 7A. Gray curves represent
error trials; the direction of motion is away from the chosen
target.
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The more gradual evolution of the response on error trials was
accompanied by prolonged reaction times. Table 2 presents mean RT for
each motion strength on error and correct choices. The best evidence
for longer RT comes from the weaker motion strengths where errors are
more common. This can also be appreciated from the length of the curves
in Figure 11, which are drawn to the median RT for each condition. The
association of longer RT with the muted ramps of activity seen on error
trials lends additional support to the idea that the buildup of
activity represents an accumulation of evidence toward a threshold. We
will explore this point further in Discussion.
Relationship between neural response and RT
If LIP represents the accumulation of evidence favoring one or the
other alternative in the discrimination task, then the amount of time
required to reach a decision should be related to the rate of growth
and decline in LIP activity. This interpretation would explain the
tendency for stronger motion to produce faster RT. Alternatively, both
the timing of the saccade and the rate at which the LIP response builds
up could be related to motion strength but not to each other, raising
the possibility that the relationship between RT and LIP activity is
merely coincidental. For example, separate processes, related to
coherence level, could operate to determine when to initiate the
saccade and where to direct it. If LIP activity represents where, but
not when, to move the eyes, then there should not be a relationship
between LIP response and RT for trials of the same motion strength. We therefore performed two analyses to determine whether the rate of rise
in neural response preceding correct T1 choices was related to the
monkey's RT.
First, at each coherence level, we sorted trials from each neuron into
short or long RT groups based on the median [as in Hanes and
Schall (1996)]. Examples from the 6.4% coherent motion strength are shown in Figure 12. These
response functions represent activity during motion viewing up to the
median RT for the group, again excluding any spike activity within 100 msec of saccade initiation. To estimate the rate of change in response
as a function of time, we fit lines to the average responses for the
two RT groups (Eq. 3C). For the responses at 6.4% coherent motion, the neural activity grew at a rate of 53.4 spikes per second squared for
trials in the short RT group and 33.9 spikes per second squared for
trials in the long RT group (CI: 47.5-59.2 and 30.1-37.6, respectively). The rate of rise of the response for the two groups at
each coherence level is shown in Figure
13. The steeper rise in spike rate
associated with shorter RT was statistically significant for all motion
strengths except 25.6% (p < 0.05) (Eq. 3C,
H0: 4 = 0). A comparison of
the neural response in the same epoch indicated by arrow a
in Figure 7 shows that, across motion strengths, the firing rate in LIP
was 4.3 spikes per second higher for trials in the short group (CI: 3.1 to 5.5; p < 0.01) (Eq. 2E, H0:
3 = 0). In contrast, just before saccade
initiation (same epoch as c in Fig. 7), there was no
difference in firing rate between the two groups (difference = 0.5 spikes per second; CI: 1.0 to 2.0; p = 0.71)
(Eq. 2E, H0: 3 = 0).
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Figure 12.
Relationship between LIP response and reaction
time at a fixed motion strength. Average spike rate from 54 experiments
is plotted as a function of time from onset of motion using all correct
T1 choices at a near-threshold motion strength (6.4% coherence toward
the RF). Trials from each experiment were divided into short and long
RT with respect to the median. The inset shows the
distribution of reaction times contributing to each group. There is
considerable overlap because the median RT varied across experiments.
Average spike rate functions were computed in 20 msec time bins aligned
to motion onset (t = 0) excluding activity within 100 msec of saccade initiation. Lines are weighted least squares
fits to Equation 3C in the epoch from t > 200 msec.
The slopes (and confidence intervals) of these fits are plotted in
Figure 13.
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Figure 13.
Relationship between LIP response and reaction
time at each of the six motion strengths. Histograms show the rate of
increase in spike rate during motion viewing (T1 choices only). For
each coherence level, trials were sorted into a short or long RT group
as in Figure 12. Spike rate versus time functions for each group were
fit with Equation 3C in epochs beginning 200 msec after motion onset
and ending at the median RT. The analysis was performed separately for
each motion strength (percentage of coherence). Only correct choices
were analyzed for nonzero coherence motion. Error bars represent 95%
confidence intervals. Inset, The median reaction time for
correct choices at each of the motion coherence values
(solid curve and filled circles,
short RT; dashed curve and open circles, long
RT).
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One possible concern with this analysis stems from the fact that
random-dot stimuli of nominally identical motion strength are in fact
different from trial to trial because the position and timing of random
dots are determined by different sequences of random numbers. We
therefore performed further experiments using identical patterns of
random dots on half of the trials. For three neurons studied in this
fashion, we observed a 10.6 spikes per second difference in spike rate
favoring short over long RT trials (epoch a in Fig. 7; CI:
4.7-16.4 spikes per second; p < 0.001) (Eq. 2F,
H0: 3 = 0). Removing the
trial-to-trial variation in motion strength caused a small reduction of
the trial-to-trial variability in RT (7% change in coefficient of
variation,  /µ), but the remaining variability in RT retained its
correlation with the activity in LIP.
Up to now, we have considered only averages of responses and averages
of RT. Yet, it is clear from Figures 4 and 12 (inset) that
RT can be quite variable from trial to trial, even when the same
stimulus is shown to the monkey. We wondered whether this variability
might be reflected in the neural activity, which is also highly
variable from trial to trial (Softky and Koch, 1993; Shadlen and Newsome, 1994, 1998). We therefore devised a method to estimate the
rate of change in the spike rate on individual trials. The irregularity
of spike trains in cortex generally precludes an estimate of
instantaneous spike rate from single trials (spike rate is usually
inferred from averages of many trials), but this impediment can be
overcome with previous knowledge of the shape of the spike rate function.
On the basis of the preceding analyses (Fig. 8), we assumed that for T1
choices, the spike rate, (t), approximates a linear ramp
during the interval from 200 msec after motion onset to 100 msec before
saccade initiation. We used a maximum likelihood procedure to estimate
the slopes of the ramps and associated standard error (see Materials
and Methods, Eq. 5). An example of such a fit is shown in Figure
14A for a single
trial. The quality of the fit should not be viewed as evidence that the
spike rate changes linearly as a function of time. In principle,
various functions could provide a better depiction of the spike rate on
any individual trial. However, the slope of the fitted ramp does
estimate the overall magnitude of change in spike rate per unit of
time.
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Figure 14.
Trial-by-trial correlation between LIP response
and reaction time. A, Example of a trial spike train and
estimated rate function from one trial. The raster shows the times of
action potentials with respect to the time of motion onset
(t = 0). The dashed line indicates average
firing rate calculated in 100 msec bins. For this analysis, spike rate
is assumed to follow linear function of time. The fit maximizes the
likelihood of observing the sequence of spikes in the epoch from 200 msec after motion onset to 100 msec before saccade initiation (Eq. 5,
see Materials and Methods). It provides an estimate of the slope of the
"firing-rate-versus-time" function and its standard error.
B, Relationship between reaction time and slope of the
firing-rate-versus-time function for one neuron. Each point
represents the estimated slope from a single trial. Error bars are ± 1 SE. Only data from correct T1 choices at 6.4% coherent motion are
shown. The slope of this line is 48.3 spikes per second cubed (i.e.,
change in slope of the firing-rate-versus-time line per 1 sec change in
RT). The weighted average slope across all motion strengths was
14.8 ± 10.5 spikes per second cubed for this neuron (Eq. 6).
C, Summary of the trial-by-trial relationship between spike
rate and RT across the population of 54 neurons. For each neuron, the
relationship between RT and slope of the firing-rate-versus-time
function was estimated using the strategy developed in A and
B. The histogram shows the change in slope of the
firing-rate-versus-time function per second of RT for each unit.
Shaded bars represent neurons with a significant
trial-by-trial relationship between slope and RT
(p < 0.05) (Eq. 6, H0:
3 = 0).
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These estimates of ramp-slope allowed us to detect a weak relationship
between RT and LIP response on single trials. For each trial, we
obtained a set of paired observations: RT and ramp- slope
(k ± SE). An example of the trials from one neuron at
one coherence level (6.4%) are shown in Figure 14B.
Each point represents the estimated ramp-slope (± SE) from a trial,
plotted as a function of RT. For this example, there was a change in
the slope of firing rate of 48.3 spikes per second squared per second
of RT (CI: 75.3 to 21.3; p < 0.001) (Eq. 6,
setting 2 = 0, H0:
3 = 0). For each neuron, we obtained a single
estimate of the relationship between slope change and RT using all
motion strengths by incorporating motion coherence in Equation 6
(2  0). In effect, this is the average change in
ramp-slope per second change in RT with respect to the mean RT for each
coherence level. The values for each neuron are shown in Figure
14C. Across trials from all neurons, the change of ramp
slope per second of RT was 58.5 spikes per second cubed (CI:
79.9 to 37.1; p < 0.0001) (Eq. 6,
H0: 3 = 0).
The inverse relationship between change in spike rate and RT supports
the idea that LIP neurons accumulate signals from visual cortex to a
threshold value, which marks completion of the decision process.
Variability in the direction signals (Britten et al., 1992, 1993,
1996; Shadlen and
Newsome, 1998) and possibly in the accumulation process itself
(Carpenter and Williams, 1995; Ratcliff and
Rouder, 1998; Ditterich et al., 2001) leads to
variability in the amount of time it takes to reach a decision.
| |
DISCUSSION |
Neurons in area LIP are known to respond to salient visual
stimuli, especially when they are the target of a planned eye movement (Gnadt and Andersen, 1988; Colby et al.,
1996). We exploited this property of LIP to study the neural
basis of a perceptual decision. We targeted neurons in area LIP that
discharge during a delay period after an instruction to make an eye
movement into a particular region of the visual field (Barash et
al., 1991a, 1991b;
Mazzoni et al., 1996). Shadlen and Newsome
(1996, 2001)
showed that when the instruction for the eye movement is random-dot
motion, the activity of these neurons predicts the monkey's decision
about direction.
When the monkey is given a fixed duration to view the random-dot
motion, activity in LIP modulates during motion viewing and persists
through a memory period (Fig. 9) (Shadlen and Newsome, 1996, 2001).
Because the time course and level of activity depend on the strength of
random-dot motion, it is likely that LIP neurons not only represent the
planned eye movement response but also the visual information on which
the developing decision is basedin other words, a decision variable.
This interpretation rests on an assumption that the modulation occurs
during the epoch of decision formation, before the monkey is committed
to a choice.
The incorporation of a reaction time measurement in the discrimination
task further clarifies two aspects of the LIP response. First, it
allows the monkey to demarcate the period of decision formation on each
trial. We can now be certain that the modulation of LIP activity that
accompanies motion viewing is not a consequence of the decision,
fait accompli. The gradual increase or decrease in spike
rate occurs before the monkey is committed to a particular action,
while the monkey is evaluating the evidence but has not yet decided the
direction of motion. Second, the completion of the decision process
seems to be marked by a stereotyped level of excitation among neurons
that signal the plan to make an eye movement response to a choice
target in their RF. This provides novel insight into a step between
decision formation and action that we term commitment.
Certain features of the activity in LIP differentiate it from sensory
signals that represent visual motion and motor signals that command eye
movements. The persistence of activity through the delay period of the
FD version of the discrimination task occurs in the absence of visual
stimulation and, in both RT and FD versions of the task, the response
is ultimately dominated by the monkey's choice rather than the
direction of random-dot motion [e.g., 0% coherent motion and error
trials; see also Shadlen and Newsome (1996,
2001)]. This is in stark
contrast with direction-selective neurons in area MT, which do not
respond during a delay period on this task (Seidemann et al.,
1998) and the responses of which are determined largely by the
direction and strength of motion (Britten et al., 1993,
1996). The gradual buildup of
activity in the RT paradigm allows us to differentiate the response
from motor planning. As seen in Figure 7, the discharge is affected by
the strength and direction of motion beginning ~200 msec after onset
of random-dot motion, and this influence persists until ~50 msec
before initiation of the eye movement response.
The pattern of activity observed in the RT experiments lends insight
into the computations that underlie the formation of a perceptual
decision. After onset of random-dot motion, there is a stereotyped
change in activity that appears as a dip (and recovery), which is
followed by a stimulus-dependent rise or fall in spike rate resembling
a linear drift or ramp, on average. The dip seems to mark the beginning
of this accumulation process. When the activity reaches a threshold
value, the decision process is complete and the monkey initiates a
saccade ~50 msec later. The process suggests an accumulation of
information toward a threshold, consistent with models of reaction time
proposed by other investigators (Luce, 1986;
Carpenter and Williams, 1995; Hanes and Schall,
1996; Ratcliff and Rouder, 1998; Reddi
and Carpenter, 2000).
This idea lends itself to a physiological interpretation. Neurons in
extrastriate visual cortex (areas MT and MST) furnish the
"information" about the direction of motion that is critical to
performance on this task (Salzman et al., 1990;
Britten et al., 1992, 1996; Celebrini and Newsome, 1995).
During motion viewing, they emit a constant level of spike discharge
(after an initial transient) at a rate that depends on the strength and
direction of motion (Britten et al., 1993). The
ramp-like change in spike rate in LIP could reflect the integral of a
difference in direction signals representing motion toward T1 and T2
(Mazurek et al., 2000). From moment to moment, this
difference in activity between neurons selective for the two directions
of motion provides evidence for (or against) a T1 choice.
Gold and Shadlen (2001) showed that the difference in
signals from opposing motion sensors is proportional to the log of the likelihood ratio favoring one direction over the alternative. Therefore, a useful decision variable is the accumulated difference between opposing motion sensors. On average, the difference will favor
the true direction of motion (for nonzero motion coherence), but on any
one trial, variability in the neural responses can lead to an
accumulation of evidence favoring the wrong direction. For example,
error trials resulting in a rightward choice would be based on a net
accrual of motion information favoring rightward, despite the fact that
motion was actually leftward. The evidence leading to these errors
appears to be weaker, on average, than the evidence for a rightward
choice based on actual rightward motion (Kim and Shadlen,
1999, their Fig. 8). In short, the discharge on any one trial
resembles a random walk to a threshold representing the level of
evidence required to reach a decision, at which point the decision
process terminates (Luce, 1986; Ratcliff and
Rouder, 1998). The stereotyped activity that occurs 50 msec
before saccade initiation (Fig. 7) could represent such a threshold
level: the amount of evidence required for commitment to one of the alternatives.
The relationship between RT and the rate of accumulation to threshold
suggests that LIP is read out downstream to determine the moment of
commitment to a particular eye-movement response. We found a weak
association between RT and single-unit activity on a trial-by-trial
basis, suggesting that the signal that is compared to the threshold is
likely to be represented by an ensemble of neurons in LIP. The
relationship between single neuron activity in LIP and RT might
therefore be likened to a similar weak relationship between the
activity of single neurons in MT and the monkey's choice, so-called
choice probability (Celebrini and Newsome, 1994; Britten et al., 1996; Shadlen et al.,
1996; Thiele and Hoffmann, 1996; Croner
and Albright, 1999).
On average, the level of activity preceding eye movements to the RF of
the neuron (T1 choices) ceases to depend on motion strength ~50 msec
before initiation of the saccade, suggesting that a threshold for
commitment was set at an ensemble average of ~68 spikes per second.
The variability inherent in this estimate is consistent with the
observation that neurons in other brain structures exhibit a more
consistent relationship with saccade initiation (Hanes and
Schall, 1996). In light of decision-related activity in these
and other brain structures (Salinas and Romo, 1998;
Horwitz and Newsome, 1999; Kim and Shadlen,
1999), it is likely that the transition from accumulation of
evidence to commitment involves interactions between several brain
areas. We are unable to discern whether the apparent threshold is
computed de novo in LIP or is imposed from other structures.
Nonetheless, it is rewarding that such a cognitive step should possess
a neural correlate. It has been suggested that when shorter RT is
promoted by changing the urgency of a behavioral response, this
threshold value is lowered (Reddi and Carpenter, 2000).
This hypothesis can now be tested at the physiological level.
The main alternative to the accumulation-to-threshold model is that a
coherence-dependent change in activity in LIP during decision formation
is only coincidentally related to RT because the monkey tends to
respond earlier when motion is stronger. According to this idea, the
stereotyped activity preceding the eye movement would be explained by a
burst preceding a saccade, regardless of antecedent. This alternative
explanation would predict no relationship between RT and the ramp of
activity before this burst, but we find precisely this relationship
between ramp slope and RT even excluding all spike discharge in the
last 100 msec before saccade initiation (Fig. 8). Thus the rate of rise
in the evidence favoring a T1 choice appears to predict whether the
monkey will complete the decision process sooner or later.
The inference of an accumulation of sensory "evidence" toward a
threshold would suggest that LIP neurons compute time integrals on
appropriate sensory inputs. This is an appealing idea because it could
explain many features of the LIP response. For example, it would
provide a qualitative explanation for the persistence of activity in
the delay period seen in the fixed duration version of the task
(Shadlen and Newsome, 2001). In general, it would cast
the "memory" response in simpler memory-guided eye movements (Funahashi et al., 1991; Bracewell et al.,
1996) as the integral of a discrete impulse (e.g., a transient
representation of a suprathreshold target). According to this idea, the
early dip and recovery in activity after onset of motion could
represent a reset of a neural integrator (Seung et al.,
2000). Although such interpretation is only speculative, our
results indicate that temporal integration of sensory evidence
underlies both the speed and accuracy of a perceptual decision and that
this integral is represented by the spike discharge of neurons in LIP.
It is likely that similar "accumulations" will be evident in other
brain structures that participate in eye movement planning, allocation
of attention, and working memory (Kim and Shadlen, 1999;
Horwitz and Newsome, 2001). It remains to be determined
how this computation is achieved and whether it applies more generally
to other tasks and to other types of decisions.
| |
FOOTNOTES |
Received March 18, 2002; revised July 18, 2002; accepted July 24, 2002.
This research was supported by Grants EY07031, EY11378, and RR00166 and
the McKnight Foundation. M.N.S. is an Investigator of the Howard Hughes
Medical Institute. We thank Melissa Mihali for expert technical
assistance. We are also grateful to John Palmer, Josh Gold, Jochen
Ditterich, and Mark Mazurek for helpful suggestions on this manuscript.
Correspondence should be addressed to Dr. Michael N. Shadlen,
Department of Physiology, University of Washington Medical School, Box
357290, Seattle, WA 98195-7290. E-mail:
shadlen{at}u.washington.edu.
| |
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TOP
ABSTRACT
INTRODUCTION
