Center for Learning and Memory, RIKEN-MIT Neuroscience Research Center, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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ABSTRACT |
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Asaad, Wael F., Gregor Rainer, and Earl K. Miller. Task-Specific Neural Activity in the Primate Prefrontal Cortex. J. Neurophysiol. 84: 451-459, 2000. Real-world behavior is typically more complicated than a one-to-one mapping between a stimulus and response; the same stimulus can lead to different behaviors depending on the situation, or the same behavior may be cued by different stimuli. In such cases, knowledge of the formal demands of the task at hand is required. We found that in monkeys trained to alternate between three tasks, the activity of many neurons in the prefrontal cortex was task dependent. This included changes in overall firing rate, in firing-rate profiles (shape of responses over time), and in stimulus and response selectivity. These findings support the hypothesis that a major prefrontal function is the acquisition and implementation of task context and the "rules" used to guide behavior.
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INTRODUCTION |
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The flexibility of primate
behavior depends on the ability to choose actions appropriate not only
to the sensory information at hand but also according to the situation
in which it is encountered. The prefrontal (PF) cortex is a neocortical
region that has long been thought to be central to this ability. In
fact, recent studies have indicated that the specific sensory, motor,
and cognitive demands of the task the animal is performing (the
behavioral context) can be an important factor in determining PF neural
responses. For example, neural activity to an identical visual stimulus
can vary as a function of which portion of that stimulus must be
attended (Rainer et al. 1998b; Sakagami
and Niki 1994
) or with the particular motor response associated
with it (Asaad et al. 1998
). In fact, White and Wise
have shown that, on a more abstract level, the "rule" by which an
animal maps a given visual input to the correct motor output can have a
significant impact on the observed neural responses (White and
Wise 1999
).
Indeed, damage to the PF cortex of humans and monkeys tends to produce
impairments when available sensory information does not clearly dictate
what response is required. For example, PF lesions impair spatial
delayed response tasks in which a cue is briefly flashed at one of two
or more possible locations and the monkey must direct an eye movement
to its remembered location (Funahashi et al. 1993).
However, no impairment is observed if there is no delay and monkeys can
immediately orient to the cue. Thus the PF cortex seems critical when
the correct action must be selected using recent memory and knowledge
of task demands. Another example is the Wisconsin Card Sorting Task, a
test of the ability of human subjects to flexibly alter their responses to the same stimuli. The sorting rule varies surreptitiously every few
minutes and thus any given card can be associated with several possible
actions; the correct response is dictated by whichever rule is
currently in effect. Impairment on this task is a classic sign of PF
damage in humans (Milner 1963
), and monkeys with PF lesions are impaired on analogous tasks (Dias et al.
1997
). Knowledge of the formal requirements of the task is
critical in such cases. Indeed, several investigators have argued that
the representation of rules and other task information is a cardinal PF
function and that many of the deficits following PF damage are
explicable within this framework (Cohen and Servan-Schreiber
1992
; Grafman 1994
; Miller 1999
;
Passingham 1993
; Wise et al. 1996
).
To further explore this issue, we recorded neural activity from the
prefrontal cortices of two monkeys while they each alternated between
three tasks, an "object task," an "associative task," and a
"spatial task." The first two tasks shared common cue stimuli but
differed in how these cues were used to guide behavior, whereas the
latter two used different cues to instruct the same behavior (Fig.
1). All three required the same motor
responses. The associative task required the animals to associate a
foveally presented cue stimulus with a saccade either to the right or
left (Asaad et al. 1998). The cue-response pairings were
reversed within each session in order not to confound the influence of
cue stimulus and response direction on neural activity. The object task
used the same cue stimuli as the associative task; however, in this case, they needed only to remember the identity of the cue and then
saccade to the test object that matched it. Conversely, the spatial
task used small spots of light to explicitly cue a saccade to the right
or left and required the monkeys to remember simply the response
direction.
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METHODS |
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Subjects
The subjects were two rhesus monkeys, Macaca mulatta,
weighing 10 and 6 kg. Using previously described methods (Miller
et al. 1993), they were implanted with head bolts to immobilize
their heads and with recording chambers. One animal was implanted with an eye-coil to monitor eye movements (Robinson 1963
),
while an infrared monitoring system (ISCAN, Burlington, MA) was used
for the second animal. The infrared system was slightly less accurate than the eye-coil (the standard deviation for the noise of the eye-coil
was 0.06° while for the infrared system it was 0.12°). Except
during the inter-trial interval and until their saccade response at the
end of each trial, the monkeys were required to fixate within a small
box, 2.5° in width and height. However, monkeys were trained such
that their eye positions only very rarely varied more than 1° during
any given trial (less than 1% of trials). Breaking fixation
immediately aborted the trial without the delivery of juice.
Recording sites were localized using magnetic resonance imaging (MRI). The recording chambers were positioned stereotaxically over the left or right lateral prefrontal cortices of each animal. They were positioned such that the principal sulcus and surrounding cortex, especially the ventrolateral PF cortex, was readily accessible. All surgeries were performed under aseptic conditions while the animals were anesthetized with isoflurane. The animals received postoperative antibiotics and analgesics and were handled in accord with National Institutes of Health guidelines and the recommendations of the MIT Animal Care and Use Committee.
Behavioral tasks
Monkeys performed an object memory task (delayed
match-to-sample), a spatial memory task (spatial delayed response), and
an associative task (conditional visuomotor task, Fig.
1A) (Funahashi et al.
1989; Fuster 1973
; Glick et al.
1969
; Passingham 1975
; Petrides
1982
). In two of these tasks, the same stimuli were
used to cue different behaviors (object vs. associative tasks), while in another two the same behavioral responses were cued by different stimuli (spatial vs. associative tasks).
The tasks were administered and behavior monitored by a computer
running the "CORTEX" real-time control system
(http://cog.nimh. nih.gov/CORTEX/). The three tasks were interleaved
block-wise. Blocks lasted 100-200 trials depending on the animals'
level of performance. Each task was repeated at least twice during any single session (the time during which the same set of neurons were
isolated), and one recording session was run each day. No explicit cues
were used to signal to the animals which task they were performing.
Each animal performed the spatial and object tasks at a high level
(more than 90% correct for each, on average). Their performance on the
associative task was somewhat lower (77-90% correct overall) because
it required monkeys to learn new cue-response pairings each day and
reverse them twice during any single session. For all analyses,
however, only neural data from correct trials were used, and for the
associative task, only correct trials after the pairings were well
learned. These were selected by requiring the animals' performance to
be at least 80% correct across a moving window of ten trials. By
pairing each object with each saccade direction during the associative
task, we were able to disambiguate a neuron's response as cue-related
(object selective) or response-related (spatially selective). Neural
activity during learning in the associative task has been described
previously (Asaad et al. 1998).
For each recording session, two novel cue stimuli, never before seen by
the animal, were chosen at random. The same two stimuli and two
behavioral responses were used across the object and associative tasks.
The stimuli were small, complex objects about 2 × 2° in size.
The objects were presented on a computer screen positioned directly in
front of the animal. We made no attempt to determine which features of
particular objects were responsible for the cells' responses; for this
study, it was necessary only that different cue stimuli elicited
selective activity from a number of PF neurons. Complex objects were
used because they have been shown to elicit robust activity from
lateral prefrontal neurons (Miller et al. 1996).
Recording technique
Monkeys were seated in primate chairs within sound-attenuating enclosures (Crist Instruments, Damascus, MD). Their heads were restrained, and a juice spout was placed at their mouths for automated reward delivery. Recordings were made using arrays of eight dura-puncturing, tungsten microelectrodes (FHC Instruments, Bowdoin, ME) mounted on custom-made, independently adjustable miniature microdrives. These were introduced into the brain using a grid (Crist Instruments) with 1-mm spacing between adjacent locations. We did not prescreen neurons for task-related responses. Rather we advanced each electrode until the activity of one or more neurons was well isolated, and then data collection began. This procedure was used to ensure an unbiased estimate of prefrontal activity. In any given session, we were able to simultaneously record the activity of up to 18 individual neurons. Recording locations are pictured in Fig. 1B.
Electrical events crossing a chosen threshold were digitized and stored (DataWave, Longmont, CO). Off-line, we sorted these events into single-neuron records using parameters derived from individual components of these collected waveforms (such as peak-height, peak time, valley depths and times, etc.). The data were discarded if these parameters were unstable across the recording session, or if we were unable to cleanly separate neural waveforms from noise or multiple neural waveforms from each other.
Analysis of neural data
Data were analyzed using custom-written routines in MATLAB (Mathworks, Natick, MA) and SPSS (Chicago, IL). Trials were divided into four epochs for the analysis of neural activity. The "fixation" period consisted of the 500 ms immediately preceding stimulus onset. The "cue" period started 100 ms after stimulus onset and had a duration of 600 ms. The first 100 ms were excluded to compensate for the minimum latency of visual responses in PF cortex, and the length of this time window was selected to include any activity related to the offset of the stimulus. The "delay" epoch consisted of the subsequent 800 ms. The "presaccadic" epoch was the 250 ms immediately preceding the animals' responses (which usually occurred 150-300 ms after the end of the delay and choice onset, depending on the task and the animal). These epochs were chosen for simplicity. The results reported here were insensitive to the exact time windows used.
To assess the effects of the cues, saccade directions, and tasks on neural activity, a set of two-way ANOVAs was performed for each cell and on activity from each epoch. To compare cue-related object-selective activity across behaviors, we used two-way ANOVAs with cue stimulus (either "A" or "B") and task (either object or associative) as factors. To compare activity related to the animals' behavioral responses across tasks, we used two-way ANOVAs with direction (right or left) and task (spatial or associative) as factors. A significant effect of stimulus, direction, or task means that activity varied significantly with the cue stimuli, saccade direction, or task being performed. If stimulus or direction had different effects on neural activity depending on the task, this would produce a significant interaction between this factor and the task factor. All ANOVAs were evaluated at P < 0.01. This alpha level was adjusted for multiple comparisons where appropriate. All neural activity histograms were calculated with a resolution of 1 ms, then smoothed with a rounded-shoulder boxcar (50-ms boxcar convoluted with a 5-ms Gaussian).
Neurophysiological experiments that compare activity across different blocks of trials must make efforts to be confident that any neural effects are not the result of artifacts of that design, such as slow-wave changes in neural activity over time. We made certain such artifacts did not influence our data in several ways. First, we required animals to perform at least two repetitions of each task within any single session, and any cells showing gross instability across the recording session were never included in our studied population. In addition, to be certain that nonspecific changes in neuronal activity over time were not mistaken for true task-specific effects, analyses were repeated using only the subpopulation of neurons which showed no difference in activity across task repetitions (1-way ANOVA using block as a factor, P > 0.1). All results reported here have been replicated in this manner. Furthermore we confirmed the task effects by controlling for possible drift in two ways: we subtracted the average baseline activity on each block from the within-trial activity to express neural responses as a difference from baseline, and we divided within-trial activity by baseline activity so that within-trial activity would be expressed as a proportional change over baseline. Across the population, both techniques yielded identical results to the analyses based on raw data reported here.
Analysis of eye movements
Eye position was monitored at 100 Hz in both animals. In seven of nine sessions in the second animal, these data were stored for off-line analysis. Microsaccades and saccades were detected using a simple velocity threshold set at four times the standard deviation of the signal derived from the fixation period. The start and end of each saccadic movement was determined by finding, respectively, the last point before and first point after this threshold crossing in which eye-velocity fell below 1 SD. This method reliably selected the smallest "microsaccadic" movements that could be confidently confirmed by eye given the signal-to-noise of the infrared monitoring system used in this animal.
Reaction times were available for all sessions, even when continuous eye position was not, as they were determined by simply time-stamping the moment when eye position first left the fixation box.
Where a particular neural or behavioral effect was significant in both animals, the data across them were pooled for simplicity of presentation. All major effects were observed in both animals. The few results that differed between the monkeys are so noted in RESULTS.
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RESULTS |
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We recorded 210 neurons from the left lateral PF cortex of one monkey and 95 neurons from the right lateral PF cortex of the other. Across all tasks, nearly all cells were responsive (within-trial activity differed significantly from inter-trial activity) in at least one epoch (294/305 or 96.4%, t-test, P < 0.01). Similarly in any single epoch, most cells were responsive (243/305 or 79.7% during the cue period; 233/305 or 76.4% during the delay; 255/305 or 83.6% in the presaccadic period).
Task-selective baseline activity
Most of the 305 neurons displayed a task-dependent change in overall activity, particularly in the fixation interval preceding cue presentation. Because the task remained constant for a block of 100-200 trials, the monkeys could usually predict which task they would perform on an upcoming trial. Indeed their behavior suggests that they did: when they switched to a new task, reaction times initially increased (P < 0.01 by t-test comparing 20 trials before a task-switch to the 20 trials just after) then decreased again over the course of a few trials (P = 0.03 t-test comparing 20 trials just after a task-switch to the next 20 trials).
During the precue fixation interval, about half of the cells showed small but significant differences in activity depending on which task was being performed (114/210 cells, or 54.3%, in one animal, and 53/95 cells, or 55.8%, in the other, by ANOVA, P < 0.01). Figure 2 shows two such cells. Here, activity during the inter-trial interval was similar for all tasks. However, shortly after the monkey began the trial by directing gaze to the fixation point, each showed an increase in activity whose level was significantly dependent on the current task. During this fixation interval, sensory stimulation was identical across all three tasks; all that differed was which task the monkey was about to perform. Note that although the absolute difference in spike rate is low (on the order of just a few spikes per second), this might nevertheless comprise a meaningful proportion of a neuron's activity during the fixation period, when activity is generally low (less than 10 spikes/s on average, across our population). In fact, the percent change in fixation activity between the best and worst tasks for the cells showing a significant difference was 32.0% (best minus worst divided by best; SD: 18.9%).
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While in one animal there was a slight tendency for cells to prefer the
associative over the object and spatial tasks during the fixation
period (26, 11, and 16 cells, respectively: 2,
P = 0.037), no significant tendency was observed in the
other monkey (44, 30, and 40 cells preferring each task, respectively:
2, P = 0.254). That a sizeable
proportion of neurons was found to prefer each of the three tasks
suggests that different neurons are selective for different tasks much
as they are selective for different objects or saccade directions.
Effects of task on neural activity related to cues and saccades
Across the object and associative tasks, the cue stimuli seen at the start of the trial were identical. The tasks differed in what needed to be done with the cues, either find its match (object task) or perform the saccade currently associated with it (associative task). Many neurons (163/305 or 53.4% see Fig. 3A and Table 1) showed sensory-related activity; they reflected the identity of the objects irrespective of the task (stimulus P < 0.01, stimulus × task interaction P > 0.01 in at least 1 epoch). However, over a quarter of the neurons showed stimulus selectivity that was modulated by the task (84/305 or 27.5%), i.e., selectivity that was significantly stronger in one of the tasks (Fig. 3, B and C, stimulus × task interaction P < 0.01). Indeed, for some cells the task influence could be so powerful as to make a neuron unresponsive in one task while clearly responsive to the same stimulus in the context of the other task (Fig. 4).
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A similar pattern of results was found when we compared activity between the spatial and associative tasks (Table 2). They required the same saccadic responses, but differed in whether they were explicitly cued (spatial task) or inferred from an association with a cue stimulus (associative task). Many neurons (125/305 or 41.0%, Table 2) showed saccade-direction selectivity whose magnitude was about equal across both tasks (Fig. 5A; direction, P < 0.01, no interaction with task, P > 0.01, in at least 1 epoch). This activity presumably reflects a mechanism common to both tasks, perhaps a "premotor" signal and/or a shift in attention preceding the saccade. Other neurons (139/305 or 45.6%) showed direction selective activity whose magnitude depended on which task the animal was performing (direction × task, P < 0.01, in at least 1 trial epoch). For some neurons, direction selectivity was stronger in the spatial than the associative task (103/305 or 34%, Fig. 5B). This could reflect a neuron's response to the peripheral cues used in the former but not the latter. There is no such simple explanation for the neurons that showed stronger direction selectivity in the associative than the spatial task (49/305, or 16%). For example, the neuron depicted in Fig. 5C showed delay activity during the associative task that reflected the forthcoming saccade and not the cue stimuli (effect of stimulus, P > 0.01; direction, P < 0.01; interaction, P > 0.01). However, during the spatial task, when the identical saccades were cued, it was not selective. Thus its ability to convey information about the saccade is task dependent. We also noted that, across our population of neurons, spatial selectivity took longer to appear in the associative than in the spatial task (Table 3). This latency difference is visible in the cell in Fig. 5A. It presumably reflects the additional time needed to recall the saccade direction associated with the cue object.
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To determine the extent to which neural activity was modulated by
taskparticularly in relation to its modulation by "external" factors such as the cues or responses
we calculated a selectivity index (see Fig. 6 legend) for each epoch
of each neuron found to display significant modulation by stimulus
identity, response direction, or task (according to the ANOVAs, see
the preceding text). These were computed separately for each task, and
the resulting indices are plotted in Fig. 6. Points lying along the
diagonal represent instances of equal selectivity across tasks; the
further from the diagonal, the more modulated was the neuron by task. In both the cases, of cue stimulus selectivity by task (6A)
and direction selectivity by task (6B), the general pattern
of points is elongated along the diagonal, suggesting that the stimulus and the response were the dominating influences (the divergence of cue
period selectivities in 6B is a trivial consequence of the
different stimuli used across these tasks). The influence of task on
neural activity, therefore seems to be a biasing of responses away from
the "external" sensory- or motor-driven levels. It is perhaps
possible that the use of tasks which again shared the same cues or
responses, but differed more radically in the rules to be applied,
could result in even stronger biases.
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Consistent with previous reports (Fuster et al. 1982;
Rainer et al. 1998a
; Rao et al. 1997
;
White and Wise 1999
), neurons selective for object
and/or spatial information (saccade direction) were intermixed
throughout the recording locations (areas 46, 45, 12, 9) as were
neurons modulated or not modulated by task.
To make certain that sensory and motor conditions across the object and
associative tasks were truly equivalent, we considered the possible
influence of eye position and microsaccades. For these analyses, we
examined 60 neurons recorded during the seven sessions in which we
acquired continuous eye-position data (sampled at 100 Hz) from the
second monkey. Microsaccades were not found to differ significantly
across the object and associative tasks in number, velocity, or
amplitude in any of the seven sessions (P > 0.1). Eye
position, however, was found to differ significantly across the object
and associative tasks in five of these seven sessions. However, the
average magnitude of this difference in these five cases was only
0.16°. Such a difference is quite small when considered in the
context of the reported size of prefrontal receptive fields, which are
on average about 10° across (Rainer et al. 1998).
Equivalent differences in eye position across tasks were found during
the inter-trial-interval in six of seven sessions, suggesting that
these differences may result from slow drift, perhaps due to mechanical
variations (e.g., head position) or animal fatigue.
Nevertheless to be confident that these differences in eye position did
not influence our neural data, we examined directly the effect of eye
position on neural activity within a single task. To do this, we
divided trials into those in which the average fixation position was
less than the mean fixation position (in either the x or
y dimension, in separate analyses) or greater than this
mean. Between these two groups of trials, this induced an average
distance of 0.33°about twice the actual distance observed across
tasks. However, in only 3 of 60 cases did this manipulation result in a
significant difference in neural activity. This is not surprising,
given the large size of prefrontal receptive fields. Differences in eye
position, therefore were not contributing notably to the reported
differences in neural activity across the object and associative tasks
during the cue presentation (which were found in approximately half of
all cells).
Similarly, to determine if differences in the animals' responses could be contributing to differences in neural activity across the spatial and associative tasks, we examined the metrics of the saccadic responses and reaction times. No difference in saccade velocity, amplitude, or accuracy was observed in any of the seven sessions in which continuous eye-position data were recorded. However, we found that reaction times did indeed differ across these tasks in 13 of the 31 total sessions (9/21 in the 1st monkey, and 4/10 in the 2nd). In these cases, the monkeys responded slightly more quickly (~14 ms, on average) in the associative than in the spatial task, possibly as a result of their greater experience with the associative task.
Although the magnitude of this reaction time difference was small, we
nevertheless examined the 107 neurons recorded in these 13 sessions to
be certain that differences in neural activityparticularly during the
presaccadic epoch
were not related to differences in reaction time. In
a manner analogous to that employed in the preceding text, we divided
trials within a task according to whether reaction time was faster or
slower than the mean reaction time. This induced an average difference
in reaction time of 63 ms, which is greater than the observed
difference across tasks of only 14 ms. Despite this, only 12 of these
107 neurons were found to display significant differences in neural
activity depending on reaction time. Furthermore only five of these
also displayed task-specific activity that was of the same sign as the
influence of reaction time on neural activity would predict, as
determined for each. These considerations led us to conclude that the
observed small differences in reaction time were not contributing
significantly to the task-selective responses that were prevalent
across our population of neurons.
Consistency of neural task preferences across trial epochs
Often task selectivity was maintained across the three main trial epochs (cue, delay, and presaccade). Across the object and associative tasks, 133 of 244 neurons (54.5%) had consistent task preferences in all three epochs. In comparison, consistent cue stimulus preferences were less common (68/210 neurons or 32.4%). Likewise, across the spatial and associative tasks, consistent task preferences across these epochs were observed in 158 of 275 neurons (57.4%). This was similar to the proportion of neurons showing consistent direction preferences in these tasks (110/244 or 54.5%). These data suggest that the task-selective signal is at least as robust, over time within a trial, as stimulus or response selectivity.
Task-related "climbing" activity
Another frequently observed task difference occurred toward the end of the memory delay and into the presaccadic period: Neuronal activity "ramped-up" more often during the object task than during the spatial or associative tasks. An example of one such single neuron is shown in Fig. 7A. Note that beginning shortly before the saccadic response, activity in the object task (green line), but not in the spatial (blue) or associative (red) tasks, began to ramp up, apparently culminating in a response to the choice objects (which did not appear in the other tasks). To assess the prevalence of this type of activity across our population of neurons we fit a least-squares line to a 300-ms time period (divided into 1-ms bins) at the end of the delay and during the presaccadic period for each and every recorded neuron. This revealed that the slopes were more positive (i.e., there was more climbing activity) for the object task than for both the spatial and associative tasks (ANOVA with post hoc contrasts, P < 0.001 for 1 animal and P = 0.06 for the other). For every cell in our population, we plotted the slope obtained for the object task against the slopes from each of the other two tasks (Fig. 7B). That most of the points lie above the diagonal reflects the generally greater spike rate acceleration at the end of the delay of the object task.
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Within the object task, a negative correlation was observed between the
degree of ramping and the magnitude of cue stimulus selectivity during
this epoch (correlation coefficient = 0.15, R2 = 0.36, and P < 0.01 for the 1st animal, ml =
0.22,
R2 = 0.51, and P < 0.01 for the 2nd animal). This suggests that ramping activity serves
not simply to augment a stimulus-selective signal to be used for the
upcoming choice but rather has a still undetermined function.
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DISCUSSION |
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These results show that for many PF neurons, activity was
influenced by the task being performed. This influence included changes
in their baseline firing rates, modulations of neuronal activity
related to particular stimuli and behavioral responses, and differences
in their firing rate profiles (shape of the responses over time). This
suggests that the formal demands of behavior are represented within PF
activity and thus supports the hypothesis that one PF function is the
acquisition and implementation of task context and the "rules" used
to guide behavior (Cohen and Servan-Schreiber 1992;
Grafman 1994
; Miller 1999
;
Passingham 1993
; Wise et al. 1996
).
To perform efficiently, the animals needed to keep track of which task
was current. This was reflected in modulations in the baseline activity
of many neurons. The spike rate differences were relatively small, but
because this effect was evident in over half of the recorded cells and
represented a sizeable fraction of their over-all level of activity
(~30%), this may nevertheless provide a means through which PF
cortical activity can bias processing in other brain regions.
Previously, modulations of PF baseline (precue) activity were observed
when monkeys had to remember a particular stimulus between trials
(Rainer et al. 1998). In this case, however, there was
no sensory information to be remembered. Nor could this effect be
explained by a "prospective code" of the anticipated cue stimuli;
for many cells precue activity was significantly different between the
object and associative tasks (which used the same cues). Thus the
information conveyed was necessarily more abstract. More generally,
when the same stimuli are involved in several possible behaviors, this
sort of task-specific activity could provide a signal that allows
ambiguous or conflicting sensory information to be mapped to the
appropriate motor output (Cohen and Servan-Schreiber
1992
; Fuster 1995
). Conversely, task-specific activity in the PF cortex could function, via "top-down" signals, to bias the activity of sensory systems toward the representation of
relevant information (Desimone and Duncan 1995
). Along
the same lines, the PF cortex is likely involved in the retrieval of
information from long-term memory (Buckner et al. 1996
;
Hasegawa et al. 1998
; Wagner et al.
1998
). A task-related signal may therefore contribute to the
phenomenon of context-dependent recall.
Stimulus or saccade-direction selectivity whose magnitude differs with
the current task indicates that some PF neurons do not simply reflect
single stimuli or forthcoming actions. Rather this suggests that
behavioral context (i.e., information associated with the cue or
saccade that is unique to a particular task or the manner in which it
is used) modulates PF activity. For example, a neuron apparently
selective during a "pure" object memory task (the object task) does
not necessarily exhibit selectivity for the same objects in other
contexts (e.g., the associative task). That many neurons did reflect a
given object or saccade regardless of task indicates that both sensory
information and convergence toward motor output are indeed present in
the PF cortex. But the existence of task-specific selectivity suggests
that the PF cortex also has information about what is "in between,"
i.e., the mechanisms for mapping sensory input to motor output
(Fuster 1990). Results from recent neurophysiological
studies lead to the same conclusion. The responses of neurons in the
lateral PF cortex and frontal eye fields to a visual target can differ
dramatically depending on the rule used to acquire the target
(Ferrera et al. 1999
; Hoshi et al. 1998
;
White and Wise 1999
). Similarly, we have previously shown that the activity of many PF neurons simultaneously reflects a
visual cue and the particular action it instructs (Asaad et al.
1998
). In addition, the existence of task-specific signals could provide an alternative explanation for the differences in neural
activity previously observed across the spatial and associative tasks
(Wilson et al. 1993
). It had been suggested that these
differences were due entirely to the use of patterned stimuli versus
simple spatial cues, whereas now it is apparent that even identical
cues could result in different patterns of PF activity if each is
embedded within a different task context. Therefore one must consider
the possibility that differences in the nature of the stimulus-response mapping
the rule
contributed to differences in neural activity.
A ramp-up of neural activity was observed near the end of the delay and
into the presaccadic period in predominantly the object task. This
might anticipate the impending choice, perhaps serving in some manner
to prepare for the stimulus comparison about to be performed
(Rainer et al. 1999). Alternatively, this activity may
function to inhibit the actions associated with the cues during the
associative task because, unlike the object task, the associative task
did not require the animals to withhold a response until a target
(match) was located. This ramping is unlikely to reflect simply the
expectation of a visual stimulus because the degree of ramping was much
weaker preceding the appearance of the cue.
Together, these results support the notion that the information conveyed by PF neurons is not limited to discrete sensory events or motor plans. Rather the behavioral context in which the animals were engaged had a pervasive influence on PF activity. This abstracted representation of information within the PF cortex may provide the necessary foundation for the complex forms of behavior observed in primates in whom this structure is most elaborate.
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ACKNOWLEDGMENTS |
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The authors thank A. Lee, K. Louie, N. Kanwisher, M. Mehta, S. Ro, T. Siapas, and M. Wicherski for valuable comments.
This work was supported by the RIKEN-MIT Neuroscience Research Center and by National Institute of Neurological Disorders and Stroke Grant NS-35145.
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FOOTNOTES |
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Address for reprint requests: E. K. Miller, MIT, Bldg. E25, Rm. 236, Cambridge, MA 02139 (E-mail: ekm{at}ai.mit.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 27 August 1999; accepted in final form 22 March 2000.
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