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The Journal of Neuroscience, July 1, 1999, 19(13):5493-5505
Prospective Coding for Objects in Primate Prefrontal Cortex
Gregor
Rainer,
S. Chenchal
Rao, and
Earl K.
Miller
Department of Brain and Cognitive Sciences, Center for Learning and
Memory, and RIKEN-MIT Neuroscience Center, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139
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ABSTRACT |
We examined neural activity in prefrontal (PF) cortex of monkeys
performing a delayed paired associate task. Monkeys were cued
with a sample object. Then, after a delay, a test object was presented.
If the test object was the object associated with the sample during
training (i.e., its target), they had to release a lever. Monkeys could
bridge the delay by remembering the sample (a sensory-related code)
and/or thinking ahead to the expected target (a prospective code).
Examination of the monkeys' behavior suggested that they were relying
on a prospective code. During and shortly after sample presentation,
neural activity in the lateral PF cortex primarily reflected the
sample. Toward the end of the delay, however, PF activity began to
reflect the anticipated target, which indicated a prospective code.
These results provide further confirmation that PF cortex does not
simply buffer incoming visual inputs, but instead selectively processes
information relevant to current behavioral demands, even when this
information must be recalled from long-term memory.
Key words:
prefrontal cortex; pair association; working memory; associative memory; physiology; vision; long-term memory; recall; monkey
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INTRODUCTION |
When a delay is imposed between two
behaviorally related events, animals can bridge this gap by remembering
the event before the delay or anticipating the event after the delay.
Consider a visual delayed paired associate (DPA) task in which there
are two sample objects (e.g., "S1" and "S2") and two choice
objects ("C1" and "C2"). If S1 is the sample, the animal must
choose C1 after the delay, and if S2 is the sample, the animal must
choose C2. Animals could solve this task by maintaining over the delay a memory of the samples (S1 or S2) or a memory of the anticipated choice stimuli (C1 or C2). The former (memories of events in the recent
past) is called retrospective processing, and the latter (anticipation
of future events) is called prospective processing (Roitblat, 1980 ;
Honig and Thompson, 1982).
Prospective processing allows animals to optimize behavior by preparing
in advance for critical events. They can, for example, act more quickly
and direct attention to catch a fleeting event that might otherwise be
missed. Given such advantages, it is not surprising that behavioral
evidence for prospective processing has been found in several animal
species, including monkeys and pigeons (Gaffan, 1977; Roitblat, 1980;
Honig and Thompson, 1982; Colombo and Graziano, 1994). Studies
exploring prospective processing have used associative tasks, such as
the DPA task described above. These tasks thus allow perceptions and
memories of the sample to be distinguished from expectation of events
after the delay. For example, Colombo and Graziano (1994) trained
monkeys on an auditory-visual matching task in which the samples were
tones and the matches were objects associated with the tones. They
found that visual, but not auditory, interference in the delay
disrupted performance, which suggested that monkeys had generated a
prospective code of the anticipated object.
Neurophysiological evidence for prospective processing has been found
in extrastriate visual areas thought to be involved in visual memory
and perception. During the delay of visual DPA tasks, the activity of
some neurons in inferior temporal (IT) cortex reflects the object
expected after the delay (Sakai and Miyashita, 1991). During the delay
of cross-modal DPA tasks, many visually responsive neurons in area V4
show activity after a haptic sample that seems to reflect the visual
stimulus associated with it (Haenny et al., 1988; Maunsell et al.,
1991).
Another region likely to be involved in prospective processing is the
prefrontal (PF) cortex. It is critical for a wide range of visually
guided behaviors (Goldman-Rakic, 1987; Passingham, 1993; Petrides,
1994; Fuster, 1995; Miller, 1999). Furthermore, the lateral PF cortex
is interconnected with extrastriate visual cortex, including areas in
which prospective processing is evident (the IT cortex and area V4).
Although PF neuronal activity has been shown to reflect prospective
coding for anticipated rewards (Watanabe, 1996) and actions (Quintana
and Fuster, 1992; Asaad et al., 1998), its role in prospective
processing of visual information has not been tested. To explore the
role of the lateral PF cortex in prospective coding for objects, we
trained monkeys to perform a visual DPA task.
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MATERIALS AND METHODS |
Subjects. Two adult rhesus monkeys (Maccaca
mulatta), one female and one male, weighing 6 and 8 kg, respectively, were used in this study. Using previously described
methods, monkeys were implanted with a scleral search coil to monitor
eye movements, a head bolt to immobilize the head during recording, and
recording chambers (Miller et al., 1993). Penetration sites were
determined using magnetic resonance imaging. The recording
chambers were positioned stereotactically over the lateral prefrontal
cortex such that the principal sulcus and surrounding cortex were
readily accessible. All surgeries were performed under sterile
conditions while the animals were anesthetized with isoflurane. The
animals received postoperative antibiotics and analgesics and were
always handled in accord with National Institutes of Health guidelines and the recommendations of the Massachusetts Institute of Technology Animal Care and Use Committee.
Recording techniques. Neural activity was isolated using
arrays of two to eight independently movable tungsten microelectrodes (FHC Instruments, Bowdoin, ME). The electrodes were advanced using custom-made screw mini-microdrives mounted on a plastic grid (Crist Instruments, Damascus, MD). Neural activity was amplified, filtered and
stored for off-line cluster separation (DataWave Technologies, Longmont, CO). We did not prescreen neurons for task-related responses. Instead, we advanced each electrode until the activity of one or more
neurons was well isolated and then began data collection. 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. On average, we collected
data from approximately six cells per recording session.
Behavioral task. Monkeys performed alternate blocks of a DPA
task and a delayed match-to-sample (DMS). Each block consisted of 50 trials, and blocks were presented in alternation for an average of
three repetitions of each type of block (a total six blocks). Both DPA
and DMS trials used the following protocol, shown schematically in
Figure 1A. Each trial
began when the monkey grasped a metal bar and initiated fixation on a
small (0.3°) spot of light (fixation spot). Their gaze needed to
remain with 1.5° of the fixation spot throughout the trial. After
2000 msec of fixation, a sample object was presented for 500 msec at
the center of gaze. After a 1000 msec delay, a test object was
presented for 500 msec. If the test object was the correct choice
object (the target), animals were required to release the metal bar
within 500 msec to obtain a juice reward. The fixation point was
superimposed on the sample and test stimuli so that the monkeys could
maintain central gaze during stimulus presentation.
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Figure 1.
A, Schematic diagram of
the DPA task. After fixating for 2 sec, a sample object was presented
at the center of gaze for 500 msec. After a subsequent 1000 msec delay,
a test object was presented, which could be either a target or
nontarget. The sequence of events was identical for the DMS task.
B, Sample-target associations for both tasks. For each
task, the test stimulus could either be a target (match) or
nontarget (nonmatch). Stimuli for both tasks were chosen such that two
of the three were similar to each other. Similarity is depicted as
vertical proximity, such that S1 and S2, and C2 and C3 were similar and
each were different from S3 and C1, respectively.
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For both tasks, a test object could either be a target or a nontarget.
On DMS trials, it was a target if it was identical to (i.e., matched)
the sample object. Three stimuli were used, each as a sample-target
and as a nontarget (nonmatching) test object on different trials. On
DPA trials, a test object was the target if it was the object that had
been associated with the sample object during several months of
training. If the test object was a nontarget, monkeys were required to
hold the bar for the duration of test object presentation and for an
additional 1000 msec delay (data not shown in Fig.
1A), which was always terminated by the presentation
of a target. Monkeys did not have to hold object-specific information
in memory during this second delay; it was included in both the DMS and
DPA task to ensure that monkeys made a behavioral response on every
trial. That way, we could be sure that the monkey was actively engaged
by the task on every correctly performed trial. The test object was
always extinguished as soon as the monkey released the metal bar.
Rewards were withheld on incorrect responses, and a 2 sec time-out
period was imposed. In both tasks, the same behavioral response (lever
release) was required on each trial, and it always led to the same
reward (apple juice). In contrast, the samples and expected targets
were varied randomly from trial to trial.
Stimuli. All objects were multicolored pictures of real
world objects, each ~2 × 2° in size. Their average luminance
was 15.3 cd/m2 (SD of 0.7 cd/m2)
and had an eight-bit color depth. They were presented at the center of
gaze on a uniform background with a luminance of 0.8 cd/m2. We used three pairs of associated objects in
the DPA task. The samples will be referred to as S1, S2, and S3 and the
choices as C1, C2, and C3. Monkeys needed to choose C1 if S1 was the
sample and so on (i.e., S1 C1, S2C2, S3C3). The three choice
stimuli were also used as nontarget test stimuli. In the DMS task, the three choice stimuli were used as both samples and matches (i.e., C1C1, C2C2, C3C3). They also appeared as nonmatching test
stimuli on different trials. Note that, across the DMS and the DPA
tasks, monkeys always chose the same targets (C1, C2, C3). The tasks only differed in which samples preceded these targets. Thus, the monkeys did not need explicit cueing as to which task (DPA or DMS) they
were performing. This type of design in which a larger group of samples
is used to instruct choice of a smaller set of targets has been shown
previously to foster prospective processing (Zentall et al., 1993).
The objects were chosen such that two of the samples (S1 and S2) and
two of the targets (C2 and C3) were similar. The two similar samples
(S1 and S2) were associated with two dissimilar choice stimuli (C1 and
C2), and the two dissimilar samples (S2 and S3) were associated with
two similar choices (C2 and C3). This relationship is depicted in
Figure 1B. Similarity was assessed in pilot studies
with human observers. The stimuli that to humans looked physically
similar also seemed so to the monkeys; their pattern of errors in the
DMS task reflected this similarity (see Results and Fig.
3B). The "confusion matrix" design used in the DPA task
has been used in previous studies to explore whether animals adopt a
prospective strategy or a retrospective (sensory-related) strategy
(Gaffan, 1977; Roitblat, 1982). The pattern of errors is thought to
reflect which stimulus (the sample or target) the animal actively holds
in working memory. If errors reflect the similarity of the targets (C2
and C3), this would suggest a prospective code of the targets. In
contrast, if errors reflect the similarity of the samples (S1 and S2),
this would suggest that animals were maintaining a sensory-related code
of the samples. In the former (prospective) strategy, recall of the
targets from long-term memory would take place before presentation of
the test object. In the latter (retrospective) strategy, recall would
be initiated by presentation of the test object, when monkeys need to
determine whether it is the correct target.
We used complex objects because previous studies have shown they
readily elicit stimulus-selective activity from PF neurons (Miller et
al., 1996). Complex stimuli, however, do not lend themselves to an
objective measure of similarity. Thus, to control for the possibility
that the similar samples were more similar to one another than the
similar targets were to one another (or vice versa), we trained one
monkey to perform the task with sample and target objects reversed.
That is, the objects used as C1, C2, and C3 for one monkey were used as
S1, S2, and S3 for the other and vice versa. This counter-balances the
effects of similarity of specific stimuli across the two monkeys. It
also allows us to control for any misclassification of stimuli with
regard to their similarity. Any misclassification would result in a
different pattern of results across the two monkeys. As will be seen
below, this was not the case. Similar results were seen in both monkeys.
Data analysis. We calculated the firing rates of PF neurons
in four epochs. These epochs were selected by inspecting the average activity of all cells in the entire population, as shown in Figure 2B. Neural activity
during sample presentation was calculated over the entire 500 msec
period of sample presentation, offset by 100 msec to compensate for the
latency of PF neurons. For analysis of activity during the delay, we
used the last 700 msec of the 1000 msec delay. As in previous studies,
we excluded the first part of the delay to exclude any effects related
to the offset of the sample. Analysis of responses to the test object
(test epoch) was restricted to a 150 msec period starting 100 msec
after test object presentation (a period before the bar release) to exclude any effects related to the behavioral response. Baseline or
spontaneous activity was assessed over 800 msec before sample onset. We
used data from correctly executed trials only. There was an average of
50 correct trials per sample condition. The monkeys did not accumulate
enough error trials to allow analysis of neural activity during
errors.
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Figure 2.
A, Location of recording sites. The
recording sites were located around the principal sulcus and on the
inferior convexity of the prefrontal cortex. Recording sites in the two
monkeys were intermixed and nonoverlapping. The circles
and triangles depict where prospective and
sensory-related cells were found. Recording sites from monkey A are
shown as thick symbols, and those from monkey B are
shown as thin symbols. B, Average
histogram for all recorded cells. The curve represents
the mean firing rate (bin width, 10 msec). The shaded gray
area represents the period of sample object presentation. The
black vertical lines delineate the sample and delay
epochs described in Materials and Methods.
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For some of the comparisons of neural selectivity, we computed a
measure of the difference of activity between the tasks normalized by
the variance:
where Fi is the average firing
rate, and  i2 is the variance of
neural activity to the i-th sample object. The vertical bars denote the absolute value operator. The normalized difference d allows the direct comparison of activity differences for
neurons with widely different baseline firing rates. If neural activity reflected prospective processing, then, across the two tasks, neural
responses to samples predicting the same targets should be similar. For
example, the responses to sample S1 (DPA task) should be similar to
responses to sample C1 (DMS task), because in both cases the monkey
needs to select target C1. We assessed this by computing two numbers: a
normalized difference to associated objects (NDA) and a normalized
difference to nonassociated objects (NDN). The NDA was the average
difference in activity between DMS and DPA trials that predicted the
same targets. We computed the absolute values of the mean differences
in activity between trials in which S1 and C1 were samples (both
predict target C1), between trials in which S2 and C2 (target C2) were
samples, and between trials in which S3 and C3 (target C3) were samples
and then calculated their average. In contrast, the NDN was the mean of
the differences in activity between all possible pairs of samples that
predict different targets (e.g., S1 vs S2, S1 vs S3, S1 vs C2, S1 vs
C3, etc). Thus, the NDA is a measure of how similar activity is on
trials in which the same target is expected, whereas the NDN is a
measure of the general selectivity of the neuron, computed by comparing
trials in which different targets were expected.
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RESULTS |
Behavioral data
Pattern of errors
Two monkeys completed a total of 52 recording sessions, with a
mean number of 342 correct trials per session. For behavioral analyses,
we computed the average error rate for each session. The monkeys were
well trained on both the DPA and DMS tasks; their average performance
across the sessions was >95% correct. They made few false negative
responses; on trials in which the test object was a target, average
performance across all sessions was 98% on both tasks. Most errors
were false alarms: that is, incorrect responses to a nontarget object
as if it were a target. False alarm rates for both monkeys from the DPA
task are shown in Figure 3A.
The pattern of errors was consistent with previous reports of
prospective errors (Gaffan, 1977; Roitblat, 1982; see caveats in
Discussion). That is, errors were based on the similarity of the target
objects and not on the similarity of the sample objects. For example,
after sample S3, monkeys made many errors responding to test C2, which
was physically similar to C3 (Fig. 3A, second horizontal bar from top). On the other hand, few sensory-related errors, those confusing similar samples, were made. For example, there
were few false alarms to test C2 when it followed sample S1, although
S1 was physically similar to S2, the sample associated with C2 (Fig.
3A, third horizontal bar from top). Using the
error rate for each session as an observation, we found that the
difference between prospective and sensory-related errors was
significant for both monkeys (t test; p < 0.0001).
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Figure 3.
Average false alarm rates pooled across both
monkeys for DPA (A) and DMS
(B) tasks. Each bar gives the
false alarm rate for the condition described on the
left. Shown in bold are the sample and
the test object chosen in error for that trial. The
arrow points to the correct target for that sample.
Thus, the top bar in A shows the error
rate when the sample was S2 and the monkey chose C3 instead of the
correct target S2. The error bars show the SEM. Performance of monkey A
is shown on the left, and monkey B is shown on the
right.
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An examination of performance during the DMS task confirmed that the
physical similarity of the target objects was indeed contributing to
the pattern of errors observed during the DPA task. False alarm rates
for both monkeys from the DMS task are shown in Figure 3B.
More errors were made when the test object was similar to the target
than when it was dissimilar. Overall, the DPA task was more difficult
than the DMS task. On average, monkeys made more errors on the DPA task
than the DMS task (t test; p < 0.00001),
making false alarm errors on 19% and 11% of trials, respectively.
Reaction time
When the test object was a target, monkeys needed to release the
bar during the 500 msec of the presentation of this target. Histograms
of these reaction times are shown in Figure
4. This figure is based on 9665 correctly
executed trials from both monkeys. First, note that reaction times were
shorter to target C1 than to C2 and C3. This presumably reflects the
fact that C1 was dissimilar from C2 and C3, and thus C1 could be
discriminated more rapidly. More importantly, note that, for the same
targets, reaction times were similar across the tasks. There was no
significant difference between reaction times in the DPA (solid
lines) and DMS (dashed lines)
(t test; p > 0.1). In fact, reaction times
were identical to target C1 on DMS and DPA trials, although they were
shorter to this target than the other targets. The reaction time was
318 ± 27 (mean ± SE) msec for C1, 342 ± 32 msec for C2, and 338 ± 29 msec for C3. The absence of a reaction
time difference between DPA and DMS tasks is suggestive of prospective
processing. If monkeys were simply maintaining a memory of the sample
objects during the delay, one might expect the target decision to take longer in the DPA task because the recall of the target would have to
take place during the presentation of the test object. The fact that
reaction times were identical across the two tasks suggests that the
recall step had already been completed before the test object
presentation; that is, the monkeys were using prospective
processing.
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Figure 4.
Histograms of bar release reaction times to the
test object presentation pooled across both monkeys. Solid
lines and dashed lines correspond to DPA and DMS
tasks, respectively. The bin width was 10 msec.
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Neural data: general properties
A total of 347 neurons (252 from monkey A, 95 from monkey B) were
recorded in the left lateral prefrontal cortex of two monkeys. Neurons
were not screened for responsiveness and thus represent an unbiased
sample of prefrontal activity. Figure 2B illustrates the average activity across the entire population of 347 PF cells. Note
that there is a marked increase in activity over the delay as the time
of test object presentation approaches. Such "climbing activity"
has been suggested to reflect prospective coding (Quintana and Fuster,
1992; Miller et al., 1996). In principle, climbing activity could
reflect any end-of-trial event, the test object, the reward, or the
behavioral response. In this task, however, the behavioral response and
reward were constant across trials, whereas the expected target object
was varied. As will be demonstrated below, many PF neurons showed
activity near the end of the delay that varied with the anticipated
test object. Of course, expectation of the reward or of the behavioral
response could have contributed to any nonselective delay activity. In
this experiment, modest climbing activity was also evident before
sample presentation (Fig. 2B). Although the monkey
could not predict which sample would appear, it could predict when it
would appear. This activity may reflect that expectation.
We assessed responsiveness by comparing activity within a trial (sample
and delay intervals) to baseline, or spontaneous, activity using paired
sample t tests (evaluated at p < 0.01). Based on this criterion, a total of 181 of 347 cells (52%) were responsive. To assess selectivity, we performed ANOVAs (evaluated at
p < 0.01) on the sample and delay periods separately
for all neurons. Both tasks were treated together, so that there were six levels in the ANOVA corresponding to each of the possible sample
objects in DMS and DPA. We tested for selectivity for samples rather
than targets because it would reveal cells that showed any selective
activity, whether for the samples or targets. According to this
criterion, 146 cells showed activity that varied significantly with the
sample during the sample period, 149 during the delay, and 87 during
both periods. Because the main focus of this report will be on changes
in neural activity as the animal perceives the sample and then waits
for the test object presentation, most analyses will focus on the 87 cells (58 from monkey A, 29 from monkey B) that were selectively
modulated during both epochs.
Figure 2A shows the locations where these neurons
were recorded. Object-selective cells were found both near and around
the principal sulcus (area 46) and on the inferior convexity (area 12/45). Both regions receive inputs from the inferior temporal cortex,
a region known to analyze object features (Barbas, 1988; Ungerleider et
al., 1989). Area 46 also receives inputs from the posterior parietal
cortex (Goldman-Rakic and Schwartz, 1982), a region that also contains
object-selective neurons (Sereno and Maunsell, 1998). Previous studies
have shown that these prefrontal regions contain object-selective
neurons (Watanabe, 1981; Fuster et al., 1982; Wilson et al., 1993; Rao
et al., 1997; Asaad et al., 1998; Rainer et al., 1998a,b; O
Scalaidhe et al., 1998).
Prospective coding
Single cell analyses
We defined prospective activity as that reflecting a forthcoming
target object. Because the target must be recalled from memory, prospective activity is mnemonic in nature. Sensory-related activity is
defined as that reflecting the samples and thus could either be a
result of a visual response to the sample and/or its maintained memory. In other words, sensory-related activity reflects a current or
recent sensory event (the sample), whereas prospective activity reflects the expectation of a sensory event (the target). Both types of
activity were evident in the PF cortex. The cells illustrated in Figure
5A had activity consistent
with prospective coding. The cell on the left was
nonselective during sample presentation (ANOVA; p = 0.232). Then, during the second half of the delay, it showed high
levels of activity (over twice as high as baseline firing rate) when
the monkey needed to choose target C1, irrespective of whether the
preceding sample had been C1 itself (DMS) or its associate S1 (DPA).
Activity was lower for all other DMS and DPA conditions. Thus, the
activity of this cell seemed to reflect the target. This
activity did not appear to be related to other end-of-trial events,
such as the reward or behavioral response. The reward and behavioral
response were the same for all trials, whereas the activity of this
neuron varied significantly with which object was the target. On DPA
and DMS trials, the activity was significantly greater when the target
was C1 than during other DPA and DMS conditions (t test;
p < 0.0001). On the right of Figure 5A is another prospective cell. On DPA trials when S1 was
the sample and C1 the target, activity in the second half of the delay climbed to the same level of activity evident throughout the delay of
DMS trials when C1 was both the sample and target.
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Figure 5.
Single cell examples of prospective
(A) and sensory-related (B)
memory. The cells on the right were recorded from monkey
A, and the cells on the left were recorded from monkey
B. Each colored line represents the firing rate of a
single neuron after the sample object shown in the legend. The
gray shaded area represents the period of sample object
presentation, which is followed by 1000 msec of delay shown to the
right of the gray area. Histograms were
binned at 20 msec and smoothed with a 20 msec Gaussian.
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Whereas the cells in Figure 5A showed activity that
reflected the expected target, the cells shown in Figure 5B
had activity consistent with an object-selective sensory response to
the sample. During the DPA task, the cell on the right of
Figure 5B showed a visual response to sample object S3 that
lasted into the early delay. In contrast, on DMS trials when C3 was the
sample (and target), there was little or no activity. This suggests
that the high level of activity on DMS trials reflected a visual
response to sample S3 and not an expectation of target C3. Similarly,
the cell on the left of Figure 5B showed a strong
response early in the delay of DPA trials when S1 was the sample (and
C1 the target) but almost no activity on DMS trials when C1 was the
sample and target.
In the DMS and the DPA tasks, the targets were the same; the tasks only
differed in which stimuli were used as samples. A cell with prospective
activity, then, should show a similar pattern of selectivity on DMS and
DPA trials that required the same target to be chosen. For example, say
that during the DPA task, a neuron shows the greatest activity on
trials in which S1 was the sample and C1 the target. If this activity
reflected a prospective code, it would reflect C1 rather than S1. Thus,
this neuron should also show a high level of activity on DMS trials in
which C1 was both the sample and the target. A cell with
sensory-related activity, in contrast, would show no correspondence
between the tasks because they use different samples. To classify
whether single cells had activity patterns consistent with prospective
or sensory-related coding, we used a two-way ANOVA. One factor was the
target for a given trial (TARGET factor, C1, C2, or C3), whereas the
other factor was which task the animal was performing (TASK factor, DMS
or DPA). If cells with target-selective activity were involved in
sensory processing (i.e., activity was related to the samples rather
than to the targets), there should be an interaction between TARGET and
TASK factors because the sample that preceded a given target would
depend on which task the animals were performing. On the other hand, if
cells with target-selective activity were involved in prospective
coding (i.e., activity was related to the targets rather than to the
samples), there should be no interaction between TASK and TARGET
factors. That is, the task would not influence the pattern of
selectivity of the cell because both tasks required that the same
targets be chosen. Thus, prospective activity was defined as having a
significant effect of TARGET (p < 0.01) and no
significant interaction between TARGET and TASK
(p > 0.1). Sensory-related activity was defined
as exhibiting both effects of TARGET and a significant interaction
between TARGET and TASK (p < 0.01).
Figure 6 shows how many cells were
classified as prospective or sensory-related in each of the epochs
using these criteria. Figure 6A shows data from the
entire population of 181 responsive cells. Figure 6B
is based on the 87 cells that showed selective activity in both
epochs. Both figures show similar trends. There was a decrease in
the number of sensory-related cells and an increase in the number of
prospective cells from the sample to the delay ( 2,
p < 0.01). This trend was also apparent in cells from
both monkeys considered separately. This suggests that activity related
to processing of information about the samples dominated during sample presentation, but during the delay, there was an increase in
prospective processing related to the anticipated targets. Figure
2A shows the locations in which we found cells that
showed either sensory-related or prospective delay activity. These
cells were intermixed throughout the recording sites.
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Figure 6.
Histograms of cell counts classified as
prospective and sensory-related in the population of 181 responsive
cells (A) and in the 87 cells that showed
selective activity in both the sample and the delay intervals
(B). Classification is based on an ANOVA (see
Results).
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It is important to note that the classification of activity into
"sensory-related" or "prospective" categories is somewhat artificial. In this experiment, as in most others, neuronal selectivity varied along a continuum. In addition, cells selective for one of the
sample objects but none of the target objects must necessarily be
classified as sensory-related, although they do not argue against prospective coding for objects that were not included as targets. The
remaining analyses, then, will focus on average activity across a
population of cells, without categorizing activity as sensory-related or prospective. As we will see below, the trend for neural activity to
"switch" from sensory-related to prospective coding can be observed
at the population level.
Population analyses
In the DMS task, two of the samples, C2 and C3, were physically
similar and were different from C1. We first wanted to establish whether this physical similarity, which manifested itself in the monkeys' behavior, was also reflected in PF activity. To assess the
similarity of neural activity, we computed for each cell average differences in normalized firing rate (see Materials and Methods) between trials for each of the three possible combinations of sample
objects (C1-C2, C1-C3, and C2-C3). If physical similarity was
reflected in neural activity, these values would be small for the
physically similar stimuli (C2-C3, or the SIMILAR comparison) and
larger for physically dissimilar stimuli (C1-C2 and C1-C3, or the
DIFFERENT comparisons). Figure
7A shows that this is indeed the case. It illustrates the average differences for the 87 neurons that exhibited selectivity in both intervals. The difference values are
smaller to similar samples (red line) than to
dissimilar samples (black and blue lines).
We computed a two-way repeated measures ANOVA that used comparison and
time bin as factors. Differences between the two DIFFERENT comparisons
(black and blue lines) did not reach
significance at any time point (repeated measures ANOVA; p > 0.1). However, the differences between the SIMILAR
and each of the DIFFERENT comparisons (black and red
lines, blue and red lines) were
significant for the time bins that included 1100 to 1500 msec (repeated
measures ANOVA with post hoc contrasts;
p < 0.01). The fact that the three curves do not cross
indicates that selectivity is similar throughout the DMS trial. In
other words, it indicates that, on average, stimulus preference was maintained across all three epochs. We conclude that physically similar
sample objects tend to elicit similar neural activity in PF cortex and
that this difference is apparent in both visual response and mnemonic
activity (sample and delay epochs, respectively).
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Figure 7.
Time course of differences in firing
rate between different combinations of conditions in the DMS
(A) and DPA (B) task. Each
plot corresponds to the average normalized difference in firing rate
between conditions described in the legend (see Materials and Methods).
Differences were calculated using a bin width of 100 msec. The average
differences and SEs are shown for the population of 87 cells that
showed selective activity in the sample and delay epochs. The
gray shaded area represents the time of sample object
presentation.
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|
Having established the effect of physical similarity in the DMS task,
we now turn to the DPA task. They key concept in the DPA task is that
physically similar sample objects (S1 and S2) instruct choice of
dissimilar targets (C1 and C2) and that dissimilar samples (S2 and S3)
instruct similar targets (C2 and C3). Thus, there are two critical
comparisons. One is between the activity on trials in which two similar
sample objects instructed two dissimilar target objects (S1C1 vs
S2C2). This will be referred to as the SIMILAR DIFFERENT
comparison. The other comparison is between activity during trials in
which two dissimilar sample objects instructed two similar target
objects (S2C2 vs S3C3; i.e., the DIFFERENT SIMILAR comparison).
If activity reflected sensory-related processing, then the comparisons
should reflect the similarity of the samples and not the targets. That
is, the DIFFERENT SIMILAR (Fig 7B, blue
line) comparison should yield larger difference values than
the SIMILAR DIFFERENT comparison (red line). If
prospective processing predominated, then the reverse should occur; the
comparisons should reflect the similarity of the targets. That is, the
SIMILAR DIFFERENT comparison (red line) should
yield larger difference values than the DIFFERENT SIMILAR
comparison (blue line). Figure 7B shows the
plots of the average values for the 87 neurons that showed selectivity
in the sample and delay intervals. During sample presentation and the
early portion of the delay, activity reflected the samples. The
DIFFERENT SIMILAR values (blue line) are higher
than the SIMILAR DIFFERENT values (red line). Near
the end of the delay, however, this situation reverses. Then, activity
reflected the targets. The SIMILAR DIFFERENT values (red
line) are larger than the DIFFERENT SIMILAR values
(blue line). The remaining comparison is between the
activity on trials in which dissimilar samples instruct dissimilar
targets (S1C1 vs S3C3, the DIFFERENT DIFFERENT comparison).
Here, we expect difference values to remain high throughout the trial.
As can be seen in Figure 7B, they do (black
line).
The time bin 100 msec after sample offset (centered on 600 msec in Fig.
7B) showed the most consistent sensory-related activity. The
SIMILAR DIFFERENT values (red line) were
significantly lower than both the DIFFERENT SIMILAR values
(blue line) and the DIFFERENT DIFFERENT values
(p < 0.01). There was no significant difference between the latter two values (p = 0.24). In
contrast, the last two time bins of the delay (centered on 1400 and
1500 msec in Fig. 7B) showed evidence of prospective
activity. The DIFFERENT SIMILAR values (blue line)
were significantly less than the SIMILAR DIFFERENT values
(red line) and the DIFFERENT DIFFERENT values
(black line) (both p < 0.01). At
these times, there was no significant difference between the latter two
values (blue vs black lines; p > 0.1). Thus, in the DPA task, sensory-related activity was strongest
just after sample offset, and prospective activity was strongest near
the end of the delay. This is consistent with the notion that
prospective coding increases as the time for the expected target approaches.
To investigate prospective activity in more detail, we tested whether
samples that predicted the same targets (e.g., S1 and C1, which both
predict target C1) elicited similar activity relative to samples that
predicted different targets (e.g., S1 and C2). For both the sample and
delay epochs, we computed two numbers for each cell: NDA and NDN
(see Materials and Methods). The NDA value reflects similarity of
activity on DPA and DMS trials that used associated objects as samples
(e.g., S1 and C1): that is, when the same target was expected (e.g.,
C1). The smaller the difference, the more similar the activity. Of
course, if a cell was not strongly stimulus-selective, NDA would also
be low because activity would tend to be similar on all trials. Thus,
it is important to also have a measure of selectivity of each neuron to
other (nonassociated) objects. The NDN provides this. It reflects the average difference in activity between trials that used nonassociated samples: that is, those that predicted different targets. The greater
the NDN value, the more selective the activity. The lower NDA value
relative to the NDN value, then, the more similar was the activity of
the cell when the same targets were expected relative to when different
targets were expected. In other words, the lower the ratio of NDA to
NDN, the more prospective was the activity of the cell.
Figure 8, A and B,
shows the NDA plotted against the NDN values in the sample and delay
epochs for each of the 87 cells that showed selectivity in both epochs.
The black symbols represent the cells that met the
single neuron criteria for prospective coding in that epoch (the
two-way ANOVA described above). Neurons from monkey A are depicted as
circles, and neurons from monkey B are depicted as
triangles. The insets show histograms of the distribution of the NDA minus NDN values for each cell (NDA  NDN). More similarity in activity on trials that predicted the same
targets (i.e., prospective coding) should yield lower NDA values
relative to NDN values and thus more data points falling in the
top left of the scatterplot and more values in the
distribution falling to the left of zero.
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Figure 8.
Scatterplots showing the normalized difference of
each cell to associated objects (NDA) plotted against the average
normalized difference to nonassociated associated objects (NDN) for the
sample (A) and delay periods
(B). The insets in
A and B show the distribution of NDA NDN values. Neurons from monkeys A and B are shown as
circles and triangles, respectively. The black
symbols represent cells classified as prospective using an
independent method (two-way ANOVA; see Results). C,
Vectors connecting change in corresponding NDA and NDN values from the
sample to the delay interval, i.e., the change in the data point
position for each cell from A to B. This
is shown for the cells classified as prospective during the delay
period (gray arrows). The black
arrow represents the average vector. The inset
shows the distribution  NDN NDA values (see Results) for
the population of 87 cells with selective activity in both intervals
(gray bars) and for the cells classified as
prospective in the delay (black bars).
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|
We again found that prospective activity was more prevalent in the
delay than in the sample interval. During the sample epoch, only 11 of
87 cells (13%) (Fig. 6B) were classified as
prospective, and the distribution of NDA NDN values was not
significantly different from zero (t test; p = 0.70). Many of the data points obtained from the sample epoch cluster
around the diagonal, indicating that selectivity to associated and
nonassociated samples was approximately similar. During the delay,
however, more cells (26 of 87, or 30%) were classified as prospective,
and more data points fall to the left of the diagonal. This
indicates that, for these cells, activity after associated samples
(that predicted the same targets) was more similar than activity to
nonassociated samples (that predicted different targets). This is
further illustrated by the distribution of the NDA NDN values,
which was significantly shifted to the left of zero
(t test; p = 0.0014). If the cells were
simply becoming less selective, the data points in the scatterplot
would have fallen along the diagonal closer to the origin, and the
distribution of NDA NDN would center at zero. For both epochs,
the NDA NDN distributions computed with neurons from the two
monkeys were not significantly different (t test;
p > 0.1).
Figure 8C illustrates, for the 26 cells that met the
statistical criterion for prospective coding in the delay, the change in their relative NDA and NDN values from the sample to the delay epoch. That is, it shows the change in position of a cell's data point
from Figure 8A (sample epoch) to Figure
8B (delay epoch). A leftward shift indicates a
decrease in NDA, i.e., an increase in similarity of activity to samples
predicting the same targets. Almost all showed a shift toward the
left or top left of the scatterplot, toward
values indicating prospective coding. Note that there is relatively
little decrease in NDN (a shift downward). This means that, even as
activity to associated samples becomes more similar in the delay,
activity to nonassociated samples remains different or (in the case of
upward shifts) becomes even more different. In fact, the average vector
(large black arrow) illustrates that, on
average, NDA values decrease as NDN values increase from the sample to
the delay. That is, as responses to associated samples become more
similar, responses to nonassociated samples become more dissimilar.
Figure 8C, inset, shows a histogram of
NDN NDA: that is, the difference between the NDA NDN distributions from the sample and delay epochs. Note that the
distribution is shifted to the left, especially for the 26 cells that
were significantly prospective in the delay (black
bars), indicating an increase in prospective coding from the
sample to the delay epochs. The shift was significantly different from
zero for the 26 prospective cells (t test; p < 0.0001) and approached significance for the entire population of 87 cells (t test; p = 0.09).
To examine the time course of prospective processing in more detail, we
defined a prospective index (PI) as the NDN NDA values. The
greater the value of the PI, the more similar was the activity of the
cell when the same targets were expected relative to trials in which
different targets were expected: that is, the more strongly prospective
was the activity. Figure 9 shows the average value of the PI over time for the entire population of 87 cells. Again, prospective activity dominated near the end of the delay.
The PI values were significantly different from zero for the last three
time bins of the delay (t test; p < 0.01). This indicates that, as the end of the delay and presentation of the
expected targets neared, there was an increase in prospective activity.
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Figure 9.
PI as a function of time. Each
bar shows the value of the PI calculated over a 400 msec
bin. The PI value for each cell was calculated by first subtracting the
NDA from the NDN value at each time bin. These values were then
averaged across the 87 cells with selective activity in both the sample
and delay epochs. High PI values indicate more similarity in activity
when the same target object is expected relative to trials when
different targets were expected. That is, the higher the PI value, the
more prospective the activity. The gray shaded area
corresponds to the period of sample object presentation. The
asterisks indicate a significant difference between NDN
and NDA values, evaluated at p < 0.01 using a
t test.
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Match effects
Previous studies have shown that, during DMS tasks, many neurons
in the PF cortex and in extrastriate visual cortex respond differently
to a test stimulus depending on whether or not it matches the sample
(Gross et al., 1979; Miller et al., 1991, 1993, 1996; Riches et al.,
1991; Lueschow et al., 1994; Miller and Desimone, 1994; Constantinidis
and Steinmetz, 1996). Some cells show stronger responses to matches
relative to nonmatches (match enhancement), and others show the
opposite effect (match suppression) (Miller and Desimone, 1994; Miller
et al., 1996). In this study, we found that these effects also occurred
for symbolic matches (targets in the DPA task). For each stimulus and
each task, we compared activity when that stimulus appeared as a target
("match") and as a nontarget ("nonmatch"). For each stimulus
and each neuron, we compared activity to the stimulus when it was a
match with activity when it was a nonmatch using t tests.
During the DMS task, 137 of the comparisons (of 543 possible
comparisons) yielded a significant difference in activity to matches
and nonmatches (t test; p < 0.05). Of
these, 85 showed match suppression, and 52 stimuli showed match
enhancement. On the DPA task, a similar number of comparisons (145)
yielded significant differences in activity to matches and nonmatches.
For this task, however, there was a significantly greater incidence of
match enhancement (84 comparisons) than match suppression (61 comparisons; 2; p < 0.01). Bar graphs
showing average firing rates to matching and nonmatching stimuli are
shown in Figure 10. The incidence of match-nonmatch effects was similar in both monkeys.
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Figure 10.
Match-nonmatch effects in the DMS
(A) and DPA (B) tasks. Each
pair of bars shows the average firing
rate to matching (targets) and nonmatching (nontargets) test objects
for stimuli that elicited significant enhancement or suppression of
responses to matches relative to nonmatches. The error bars show the
SD.
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Match enhancement was stronger on DPA trials than DMS trials. On DPA
trials, there was an average 30% increase in responses to matches
relative to nonmatches (t test; p = 0.05),
whereas on DMS trials, the average enhancement was weaker (a 23%
increase) and did not reach significance across the population
(t test; p = 0.29). In contrast, the
opposite was true for match suppression; it was stronger on DMS trials
than DPA trials. On DMS trials, there was an average 38% decrease in
responses to matches relative to nonmatches (t test;
p = 0.03), whereas on DPA trials, there was a 28%
decrease in responses to matches (t test; p = 0.18). Thus, in terms of both incidence and strength of effects,
match enhancement predominated on DPA trials, and match suppression predominated on DMS trials. A given neuron, however, did not
necessarily show consistent effects across the DMS and DPA tasks. There
were only 25 incidents (of a possible 137 comparisons) in which a given stimulus elicited significant match effects from a given neuron during
both the DPA and DMS task. For only approximately half (13 of 25) were
the effects consistent across the tasks (both suppression or both
enhancement). Thus, the DPA and DMS tasks seem to elicit suppression
and enhancement effects from somewhat different populations of neurons.
| |
DISCUSSION |
Many neurophysiological studies of visual memory have used
standard, or identity, matching-to-sample tasks in which monkeys are
cued with a sample object and then, after a brief delay, must select
that object from a display. It is widely assumed that behavior is
guided by a matching process in which sensory inputs are compared with
the maintained memory of the sample. In the real world, however, sought-after objects are rarely available for inspection shortly before
we search for them. When we look for our missing keys, for example, we
must first recall what they look like. Then, incoming visual inputs can
be compared against this recalled image. The ability to recall stored
information in anticipation of its use is referred to as prospective memory.
The results of this study show for the first time that the activity of
PF neurons can reflect prospective coding for expected objects. During
the DPA task, average PF activity first reflected the sample objects.
Then, beginning before test object presentation, this activity began to
reflect the target object associated with the sample. Behavioral
results were also consistent with prospective coding. The monkeys'
reaction times to a given target object were similar regardless of
whether the monkey was cued with the object itself (DMS task) or its
paired associate (DPA task). This suggests that, in both cases, the
monkey had recalled the target from long-term memory before the test
object appeared. The monkeys' pattern of errors was based on the
similarity of targets rather than the similarity of the samples. This
also suggests that the monkeys' behavior was guided by a prospective
code of the target rather than a sensory-related code of the sample.
Other studies (Gaffan, 1977; Roitblat, 1982) have used the confusion
matrix design used in this study and found a similar pattern of
errors. In principle, interpretations involving a confusion matrix
depend on increasing the delay length so that errors can be
attributable to increased mnemonic demands (Gaffan, 1977; Roitblat,
1982) rather than different behavioral demands between test stimulus
and sample stimulus presentation. Hence, some caution is required in
interpreting these errors in our study. However, the pattern of errors
we observed is likely to reflect prospective coding given their
similarity to those of previous studies that have used increasing
delays and also given our other behavioral and neural results. Finally,
we also found evidence of sensory-related activity that reflected the samples seen at the start of the trial. This has been found in previous
studies (Fuster et al., 1982; Funahashi et al., 1989; di Pellegrino and
Wise, 1991; Wilson et al., 1993; Miller et al., 1996; Rao et al., 1997;
Asaad et al., 1998; Rainer et al., 1998a,b).
Further support for prospective coding comes from the observation that
match enhancement and suppression was apparent to both actual matches
(DMS task) and symbolic matches (DPA task), suggesting that a similar
comparison process was used to evaluate the test object in both the DMS
and DPA tasks. Miller and Desimone (1994) suggested that suppression
effects reflected an automatic mechanism sensitive to repetition,
whereas enhancement reflected a volitional mechanism related to
matching per se. Results from this study are consistent with this
interpretation. Suppression effects were more common in the DMS task
than in the DPA task, further supporting the idea that suppression is a
passive process related simply to stimulus repetition. Enhancement
effects, in contrast, predominated in the DPA task, which needed to be
solved by an active, symbolic, matching process involving the recall of
target information from long-term memory. Suppression and enhancement
effects related to identity matching are well established in the PF
cortex and extrastriate visual cortex (Gross et al., 1979; Miller et
al., 1991, 1993, 1996; Riches et al., 1991; Lueschow et al., 1994; Miller and Desimone, 1994; Constantinidis and Steinmetz, 1996).
A possibly related form of prospective processing in the PF cortex was
described by Watanabe (1996). He found that the activity of many PF
neurons reflects an expected reward (e.g., raisins, cabbage, potato).
He suggested that this might be because of visual, gustatory, or
olfactory images of the reward. The prospective activity we observed
reflected an expected object and were thus visual in nature. This
suggests that the expectancy signals observed by Watanabe may be, at
least in part, a result of visual prospective coding for the expected
rewards. Prospective coding is likely to occur for a wide variety of
stimuli, however. Activity reflecting anticipated odors has been
recently reported in the orbitofrontal cortex of rats, for example
(Lipton et al., 1998).
Evidence for prospective processing of objects has also been reported
in the IT cortex. Miyashita and colleagues found prospective coding for objects in tasks similar to the DPA task used here (Sakai
and Miyashita, 1991; Naya et al., 1996). Erickson et al. (1998) also
found prospective coding for objects in the perirhinal cortex, although
most of the neurons studied had properties consistent with
sensory-related coding. In addition, Sakai and Miyashita (1991)
reported that neurons in the IT cortex tended to have similar visual
responses to pairs of objects that were associated in training ("pair-coding neurons"). They interpret this finding as a mechanism for storing the associations between the objects. Interestingly, we did
not find a similar effect in PF cortex in our task. Rather, in the PF
cortex, visual similarity of stimuli appeared to be the sole
determinant of activity during the sample presentation. This difference
may reflect an important functional dissociation between IT and PF
cortex, perhaps indicating the greater role of the IT cortex in
long-term storage of the associations. The PF cortex, in contrast, may
be primarily involved in the recall and maintenance of memories. This
role is further suggested by the observation that humans with PF damage
have severe impairments in source memory, the ability to recall the
circumstances in which a certain item was learned (Janowsky et al.,
1989). Functional imaging studies have also implicated the human PF
cortex during recall (Buckner et al., 1996).
The recent study by Hasegawa et al. (1998) is particularly relevant.
They found that, during a DPA task when both sample and target objects
were presented in the same visual hemifield, monkeys' performance was
unaffected by section of both the posterior and anterior corpus
callosum. However, when sample and target were presented in opposite
hemifields, performance was intact for the posterior section but
devastated after section of the anterior corpus callosum. The authors
argue that this implies a role for the PF cortex in recalling the
target from storage in the temporal cortex, a conclusion supported by
the results of this study. Further support comes from Gutnikov and
colleagues (1997), who found that severing connections between the PF
and temporal cortex disrupts monkeys' ability to perform
object-object association tasks.
Prospective memory processing is critical for planning complex
behavior. It allows animals to anticipate important events and modify
their behavior or plans accordingly. The results of this study and
others indicate that the PF cortex is involved in the prospective
processing of a wide variety of information, including expected visual
stimuli, rewards, and actions. This property seems fitting for a region
at the apex of the perception-action cycle (Fuster, 1995) and thought
to be involved in planning and organizing goal-directed behavior. Our
results provide further confirmation that PF cortex does not simply
buffer incoming visual inputs but instead selectively processes
information relevant to current behavioral demands. Previously, we have
demonstrated that PF neurons selectively represented only the
information from a cluttered display that was relevant for task
performance (Rainer et al., 1998b). Here, we show that PF
neurons selectively process relevant information, even when it needs to
be recalled from long-term memory.
| |
FOOTNOTES |
Received Feb. 19, 1999; revised April 15, 1999; accepted April 19, 1999.
This work was supported by a grant from the Whitehall Foundation. We
thank K. Anderson, W. Asaad, M. Metha, A. Siapas, R. Wehby, M. Wicherski, and M. Wilson for valuable comments on this manuscript and
M. Histed for expert help.
Correspondence should be addressed to Dr. Earl K. Miller, Building E25,
Room 236, Massachusetts Institute of Technology, Cambridge, MA 02139.
| |
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TOP
ABSTRACT
INTRODUCTION
