Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, Maryland 20892-4415
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ABSTRACT |
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Liu, Zheng and Barry J. Richmond. Response Differences in Monkey TE and Perirhinal Cortex: Stimulus Association Related to Reward Schedules. J. Neurophysiol. 83: 1677-1692, 2000. Anatomic and behavioral evidence shows that TE and perirhinal cortices are two directly connected but distinct inferior temporal areas. Despite this distinctness, physiological properties of neurons in these two areas generally have been similar with neurons in both areas showing selectivity for complex visual patterns and showing response modulations related to behavioral context in the sequential delayed match-to-sample (DMS) trials, attention, and stimulus familiarity. Here we identify physiological differences in the neuronal activity of these two areas. We recorded single neurons from area TE and perirhinal cortex while the monkeys performed a simple behavioral task using randomly interleaved visually cued reward schedules of one, two, or three DMS trials. The monkeys used the cue's relation to the reward schedule (indicated by the brightness) to adjust their behavioral performance. They performed most quickly and most accurately in trials in which reward was immediately forthcoming and progressively less well as more intermediate trials remained. Thus the monkeys appeared more motivated as they progressed through the trial schedule. Neurons in both TE and perirhinal cortex responded to both the visual cues related to the reward schedules and the stimulus patterns used in the DMS trials. As expected, neurons in both areas showed response selectivity to the DMS patterns, and significant, but small, modulations related to the behavioral context in the DMS trial. However, TE and perirhinal neurons showed strikingly different response properties. The latency distribution of perirhinal responses was centered 66 ms later than the distribution of TE responses, a larger difference than the 10-15 ms usually found in sequentially connected visual cortical areas. In TE, cue-related responses were related to the cue's brightness. In perirhinal cortex, cue-related responses were related to the trial schedules independently of the cue's brightness. For example, some perirhinal neurons responded in the first trial of any reward schedule including the one trial schedule, whereas other neurons failed to respond in the first trial but respond in the last trial of any schedule. The majority of perirhinal neurons had more complicated relations to the schedule. The cue-related activity of TE neurons is interpreted most parsimoniously as a response to the stimulus brightness, whereas the cue-related activity of perirhinal neurons is interpreted most parsimoniously as carrying associative information about the animal's progress through the reward schedule. Perirhinal cortex may be part of a system gauging the relation between work schedules and rewards.
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INTRODUCTION |
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Ablation experiments in monkey have established
that inferior temporal cortex is critical for normal visual pattern
recognition (Iwai and Mishkin 1968; Mishkin
1982
; Mishkin et al. 1997
). However, inferior
temporal cortex is not a single homogeneous region.
Electrophysiological studies so far have found that two directly
connected inferior temporal areas, TE and perirhinal cortex
(Saleem and Tanaka 1996
; Suzuki and Amaral
1994a
), are very similar in neuronal response properties
despite a large body of behavioral and anatomic evidence indicating
that they are distinct. In this study, we identify striking differences
in the neuronal response properties between these two areas related to
association of the stimulus with predictable reward schedules.
Selective ablations of TE and perirhinal cortex indicate that their
roles in pattern recognition are different (Buckley et al.
1997). Removal of the perirhinal cortex impairs performance of
a short-term memory task but not a color-discrimination task, whereas
removal of area TE impairs performance of a fine color-discrimination task but not a short-term memory task. Anatomic evidence also indicates
that these areas should be considered distinct. Area TE is connected
directly with cortical area V4, whereas perirhinal cortex is not.
Perirhinal cortex is connected with entorhinal cortex, whereas area TE
is not (Suzuki 1996
; Suzuki and Amaral 1994b
; Witter 1993
). Perirhinal cortex is also
strongly connected to brain areas related to reward and motivation,
such as ventral striatum (Van Hoesen 1981
; Witter
and Groenewegen 1986
) and ventral tegmental region (Akil
and Lewis 1993
, 1994
; Insausti et al. 1987
), whereas area TE is not. In addition, surveys of cortex list perirhinal cortex among two or three regions with the densest distribution dopamine carrying fibers and dopamine receptors (Akil and Lewis 1993
, 1994
; Berger et al. 1988
; Richfield
et al. 1989
).
Given the anatomic and behavioral results related to these two areas,
it seems reasonable to expect substantial differences in signals
carried by the single neurons in them. Thus far, however, physiological
recordings in these two areas have found little difference between
them. Neurons within both areas show great stimulus selectivity for
complex visual patterns (Baylis et al. 1987;
Desimone et al. 1984
; Gross et al. 1972
;
Nakamura et al. 1994
; Riches et al. 1991
;
Richmond and Sato 1987
; Tanaka et al. 1991
). In both areas, these stimulus-elicited responses are
modulated by several factors, including display sequence in a delayed
match-to-sample (DMS) task (Eskandar et al. 1992
;
Li et al. 1993
; Miller et al. 1993
),
attention (Desimone 1996
; Richmond et al.
1983
), and stimulus familiarity (Gross et al.
1979
; Miller et al. 1991
; Riches et al.
1991
).
In our search for differences in the neuronal response properties
between these two areas, two observations influenced us: the connection
of perirhinal cortex, but not area TE, to the ventral striatum
(Van Hoesen 1981; Witter and Groenewegen
1986
) where neurons carry information about reward and
motivation (Apicella et al. 1991
; Bowman et al.
1996
; Schultz et al. 1992
; Shidara et al.
1998
; Williams et al. 1993
) and the structurally
organized and dense dopamine carrying fibers and dopamine receptors in
perirhinal cortex (Akil and Lewis 1993
, 1994
;
Berger et al. 1988
; Richfield et al.
1989
). Dopamine is thought to play a central role in signaling reward (Schultz 1997
, 1998
). We hypothesized that the
responses of perirhinal neurons could be modulated by signals related
to those seen in the ventral striatum.
To allow differentiation of factors related to reward and motivation
from factors related to pattern recognition, we combined a behavioral
paradigm used previously to study ventral striatal neurons, visually
cued reward schedules (Bowman et al. 1996;
Shidara et al. 1998
), with a behavior paradigm
frequently used to study visual pattern recognition, DMS. In the task
here, the monkeys were required to complete schedules requiring one,
two, or three correct DMS trials to obtain a reward. The reward
schedules were randomly interleaved. The schedule in effect and
progress through it were signaled by the brightness of a visual cue (a
simple bar) located above the more complex stimulus patterns used for
the DMS trials.
Neurons in both areas showed responses related to both the patterns used in the DMS trials and the visual cues. Some response properties such as DMS pattern-related stimulus selectivity were similar. However, TE and perirhinal neurons also show strikingly different response properties. The latency distribution of perirhinal responses is centered 66 ms later than the distribution of TE responses. Furthermore when the stimuli, here the visual cues, were associated explicitly with the reward schedule, the cue-related responses were very different across these two areas. Neurons in TE either responded to all cues or did not respond to any of the cues, regardless of the schedule. In contrast in perirhinal cortex, responses related to the cue occurred only at some parts of the schedule, even differentiating across parts of the schedule where the cue's brightness and the monkey's performance were identical. Thus, neurons in area TE carry signals emphasizing stimulus identity, whereas neurons in perirhinal cortex carry additional strong signals about associative behavioral significance of stimuli related to the progress through a predictable schedule of trials.
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METHODS |
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Subjects, behavioral task, and visual stimuli
Two adult rhesus monkeys (Macaca mulatta), weighting 7.5 and 8 kg, respectively, were used in this study. The monkey was seated in a primate chair facing a rear projection screen (90 × 90°) located 57 cm away. A black-and-white random dot background covered the whole screen.
The monkeys had to perform a series of sequential DMS trials. These
were grouped into reward schedules of one, two, or three trials. Reward
was delivered only after the monkey correctly performed the last trial
in a schedule. Each trial in a schedule could be referred to by its
state within a schedule (i.e., the current trial position in a schedule
divided by the length of the current schedule). The schedule
states were 1/3, 2/3, 3/3 for a three-trial schedule, 1/2, 2/2 for
a two-trial schedule, and 1/1 for a one-trial schedule. The progress
through a schedule was indicated by a cue (a simple bar of light). The
brightness of the cue varied from white to black in direct proportion
to the fractional value of the schedule state (Fig.
1A). Reward trials were
signaled by the same black bar, even when they ended schedules of
different lengths (1/1, 2/2, and 3/3 = 1). The cued-schedule
aspect of the task has been used previously to study the effect of
motivation on ventral striatal neuronal activity (Bowman et al.
1996; Shidara et al. 1998
).
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We imposed no requirement for the monkey to notice or use the cue during the task, and there was no explicit punishment for incorrect trials. However, the schedule state advanced and the cue changed brightness only after a correct trial. After an error, the schedule state did not change, and the same cue reappeared in the next trial. A reward was delivered after successful completion of the final trial of a schedule. A new schedule was picked pseudorandomly after a reward. There was no relationship between the specific DMS pattern appearing on a given trial and progress through the schedule.
Three sets of visual stimuli were used. 1) A small gray dot (0.5° in visual angle) was used as fixation spot. This was located directly in front of the monkeys at the center of the screen. 2) Eight two-dimensional (8.5 × 8.5°) black-and-white patterns were used as stimuli presented in the DMS trials (Fig. 1B), referred to as the DMS patterns throughout. These also appeared at the center of the screen. When a pattern appeared it obscured the fixation point. 3) Four gray bars (4 × 75°) of varying brightness were used as visual cues (Fig. 1C), referred to as the cue throughout. The cue was displayed 26° above the center of the screen.
A two-trial reward schedule is shown in Fig. 1A. For each
trial, the monkey started the trial by contacting a touch bar (labeled Touch Bar in Fig. 1A). Immediately after the touch bar was
contacted (20 ms), a visual cue was displayed near the top of the
projection screen and remained on throughout the trial without changing
(labeled Cue in Fig. 1A). A fixation spot appeared in the
center of the screen 220 ms after the onset of the visual cue. The
monkey was required to fixate loosely (within ±5° of the fixation
spot) for the whole trial. Both the cue and fixation spot were
displayed for 900-1,000 ms before the trial progressed to the DMS
phase. In the DMS phase of the trial (labeled DMS in Fig.
1A), a sample pattern, S, replaced the fixation point. Then
a random number (0, 1, or 2) of nonmatching patterns, NM, appeared in
sequence before the original pattern (matching pattern) reappeared, M. Sample and nonmatching stimuli were displayed for 500-1,000 ms. The
interstimulus interval was 300-800 ms. When the original pattern reappeared, the monkey was required to release the bar within 2 s
to indicate a match. A reward was delivered after the monkey performed
the last trial in the schedule correctly (labeled Reward in Fig.
1A). A trial was counted as correct if the monkey released the bar within 2 s; otherwise an error was registered. An error also was registered if the monkey moved its eyes beyond the fixation limit. The mean reaction times were 500 ms (see RESULTS).
We also used a version of the same task in which the cue was shuffled randomly with respect to the schedule. In this shuffled task, the cue no longer reflected the schedule state. In the following text, the visually cued task is referred to as the cued condition, and the randomly shuffled task is referred to as the shuffled condition.
Training procedures
Monkeys initially were trained to perform DMS with each correct trial being rewarded (a 1-trial schedule). The cue was present, but didn't change. After the monkey learned to perform DMS trials (>90% correct), randomization among the three schedules was started abruptly. Within a few minutes, the monkeys' behavior began to show the influence of the cue. The effect of the cue on the monkey's behavior stabilized within 1 wk.
The shuffled condition of the task was introduced when the monkeys' performances of the cued task were stable. In the shuffled condition, the monkeys performed as if the cue was ignored (see RESULTS). The monkeys performed the shuffled task on the day it was introduced. Switching the task between cued and shuffled conditions then was introduced. The cued and shuffled tasks were run in blocks of trials. When the condition was switched, it was switched without warning. After one or two sessions of experience, the monkeys' behavior switched as soon as they discovered that the cue had become meaningful or not, depending on the direction of the switch. Single neuronal recording began after the monkeys were experienced in the switching.
Before the surgical preparation of the monkeys, there was no requirement for the monkeys to fixate. Once the monkeys' behavioral performance stabilized, the monkeys were prepared for electrophysiological recording.
Surgical preparation
After the monkeys were trained to perform the behavioral task, a
cylinder for microelectrode recording and a head holder were affixed to
the skull during an aseptic surgical procedure performed with the
animal under isoflurane anesthesia. A scleral magnetic search coil for
measuring eye movement was implanted during the same surgery
(Judge et al. 1980; Robinson 1963
). The
monkeys were given a 2-wk postoperative recovery period. The monkeys
were retrained to the task with a loose fixation requirement (within
±5° of the fixation spot).
Single-neuron recording
Single-neuron data and behavioral data were collected while the
monkeys performed the cued reward schedules in both the cued and
shuffled conditions. A hydraulic microdrive was mounted on the
recording cylinder, and tungsten microelectrodes with impedance of
1.5-1.7 M (Roboz-Microprobe, Rockville, MD) were inserted through a
stainless steel guide tube. Experimental control and data collection
were performed by a PC, using the REX real-time data-acquisition
program (Hays et al. 1982
) adapted for the QNX operating
system. Single-neuron activities were isolated by first calculating
principal components then thresholding their values (Abeles and
Goldstein 1977
; Gawne and Richmond 1993
).
Single-neuron activities and all relevant behavioral data were stored
with 1-ms time resolution.
All of the experimental procedures described here were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Animal Care and Use Committee of the National Institute of Mental Health.
Recording sites localization
We used magnetic resonance imaging (MRI) to confirm the
single-neuron recording locations (Saunders et al.
1990). A microelectrode was inserted into the monkey's cortex
before MRI as a landmark to indicate the recording locations. The
recording areas, on the lateral-medial plane, for both perirhinal
cortex and TE of one monkey are shown in Fig.
2. On the anterior-posterior plane, TE recording was carried out in the area from +14 to +17, whereas perirhinal recording was from +18 to +23. Neurons were recorded from
comparable areas in a second monkey.
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Data analysis
Behavioral performance was measured using both reaction time and error rate. The reaction times were measured from the onset of the match stimulus to bar release. The behavioral performances were calculated for each schedule state in the cued condition or each cue brightness in the shuffled condition.
The stimulus-related neuronal responses were measured by counting the number of spikes during a 350-ms interval starting 80 ms after onset of the stimulus (either a cue or a DMS pattern) for perirhinal neurons and starting 50 ms after stimulus onset for TE neurons. Different starting times for spike counting were used in measuring neuronal responses in TE and perirhinal cortex because perirhinal neurons had longer latencies. Spontaneous activity was measured during 350 ms before the onset of the cue. Statistical significance of the results was evaluated at the 0.05 level.
A cue-related response was defined to be the neuronal response elicited by the cue during the time period when the cue was displayed alone, i.e., the 500 ms immediately after the cue's onset. A DMS pattern-related response was defined to be the neuronal responses elicited by a DMS pattern in the DMS trial.
Latency measurement
Except for the special case in which there is no ongoing activity preceding stimulus onset, determining the latencies of neuronal responses to that stimulus remains a difficult issue. Overall, probably the best way to estimate latency is by eye. However, we wished to have some objective quantitative estimate. We used a procedure to estimate latency of a response using the average spike density from all of the trials related to one stimulus. We avoided the additional difficulty of estimating trial-by-trial latency.
In the method used here, the average spike density function was formed
for the responses related to each stimulus by convolving the responses
with a Gaussian having a fixed standard deviation (Richmond et
al. 1987). For this average spike density function, we
identified the period of the largest monotonic rise (or fall) in the
500 ms after stimulus onset. For each stimulus, we then identify the
first point in the monotonic rise that was higher than the highest
point of activity during the 200 ms before stimulus onset. The time of
this first point was the estimated latency for the response elicited by
this stimulus.
Obviously, the standard deviation of the Gaussian used to form the spike density function strongly influences the latency estimation. If the bandwidth is too wide (that is preserving too much high-frequency information), fluctuations due to high-frequency noise will interfere with identifying the overall trend thus interfering with the estimate of the largest monotonic rise. The onset of stimulus-related responses should occur at a more consistent time than any background fluctuation, rising or falling at about the same time across trials. Thus the response onset should be observable across a wide range of bandwidths. Therefore average spike density functions were formed using Gaussians having standard deviations ranging from 5 to 45 ms in 5 ms steps. For each Gaussian standard deviation, we formed a vector of latency estimates from all of the stimulus conditions being considered e.g., for all of the DMS pattern-related responses. These vectors then were correlated with the vectors obtained from the next larger Gaussian standard deviation. Typically as the Gaussian becomes wider (the bandwidth becomes lower) the correlation rises and eventually reaches an asymptotic value (Fig. 3). Our final estimates of the latencies are taken from the data filtered with the narrowest Gaussian reaching the asymptotic correlation value. To account for noncausality of the Gaussian, we added half the standard deviation of the final Gaussian to each latency value, making it possible to compare values from different Gaussians. The Gaussian standard deviations were typically 20-30 ms.
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This procedure works well for these data, giving values that are consistent with values we would have chosen by eye (see Fig. 7). The same procedure can be and was applied to periods of inhibition.
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RESULTS |
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Behavioral and electrophysiological data were obtained while the two adult rhesus monkeys (M. mulatta) performed randomly interleaved reward schedules of one, two, or three DMS trials in both the cued and shuffled conditions.
Behavior
Although the monkeys were free to ignore the cue indicating the schedule progress, their behavior was influenced consistently by it. In the cued condition, both the mean reaction times and the mean error rates were strongly related to the schedule states (Fig. 4). As the end of a schedule approached (indicated by the brightness of the cue), the monkeys released the touch bar more quickly and made fewer errors. The monkeys showed the shortest reaction times and fewest errors when the cue (a dark bar) indicated that a reward would be delivered if the current trial was completed successfully. For both monkeys, the mean reaction times and mean error rates were the same on the final trial (i.e., the rewarded trial) of all three schedules (1, 2, or 3; single-factor ANOVA, NS). Thus for behavioral analysis, we can treat all of the final trials of all schedules as if they are the same. When that is done, there is a strong linear relation between the brightness of the cue and both the mean reaction time [linear regression, F(1,2) = 35.45, P < 0.05, Fig. 5A] and mean error rate [F(1,2) = 385.70, P < 0.05, Fig. 5B]. In addition, almost all of the variance in either the mean reaction times or mean error rates can be explained by the cue's brightness (linear regression, reaction time: R2 = 0.95; error rate: R2 = 0.99). When the cues were shuffled randomly so that the brightness of the cue no longer indicated the schedule state of the current trial (the shuffled condition), the cue's brightness no longer affected the monkeys' behavior [single factor ANOVA, mean reaction time: F(1,2) = 0.92, NS; mean error rate: F(1,2) = 4.28, NS; Fig. 5].
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This result shows that the monkeys treated the shuffled condition of
this task as a task with a variable-ratio reward schedule (Mackintosh 1983). In past reports, when monkeys were
asked to perform a similar behavioral task in which each trial was a
color discrimination (Shidara et al. 1997
), the monkeys
were maximally motivated in the shuffled condition. Here, however, the
mean reaction times of the final trials in the cued condition were
faster than the mean reaction times of all trials in the shuffled
condition (Wilcoxon rank sum test, W = 30, P < 0.05; 1-tailed test), and the mean error rates of
the final trials in the cued condition were smaller than the mean error
rates of all trials in the shuffled condition (Wilcoxon rank sum test,
W = 22, P < 0.05; 1-tailed test). So
the monkeys' behavior in the shuffled condition was poorer than that
of the maximally motivated states (the final trials) in the cued
condition. Thus it appears that the monkeys were less than maximally
motivated on a trial-by-trial basis in the shuffled condition.
Electrophysiology
Single neurons were recorded from both hemispheres of one monkey and one hemisphere of the other monkey. All of the stimuli, both cues and DMS patterns, elicited neuronal responses from some neurons of both TE and perirhinal cortex. Responses related to cue appearance are referred to below as cue-related responses. Responses related to DMS pattern appearances within the DMS trial are referred to in the following text as DMS pattern-related responses. Inspection showed that the neuronal responses were phasic. In every case, phasic cue-related responses ended well before the sample pattern in a DMS trial appeared, so there were no overlaps between cue-related responses (from the period when the cue is displayed alone) and DMS sample pattern-related responses (from the period when the sample pattern is displayed).
We recorded from 107 TE neurons (73 from monkey 1 and 34 from monkey 2) and 97 perirhinal neurons (45 from
monkey 1 and 52 from monkey 2). In all of the
analyses related to latency and response strength, the data from the
two monkeys were combined because there were no statistically
significant differences between them. Among the TE neurons, 3 (3%) had
responses related to the cue only, 16 (15%) had responses related to
both the cue and DMS patterns, and 34 (32%) had responses related to
one or more DMS patterns but not to the cue. The remaining 54 TE
neurons did not show stimulus-related responses. Among the perirhinal
neurons, 11 (11%) had responses related to the cue only, 22 (23%) had
responses related to both the cue and one or more DMS patterns, and 8 (8%) had responses related to one or more DMS patterns but not to the cue. The remaining 56 perirhinal neurons showed no stimulus-related responses. None of the perirhinal neurons or TE neurons studied showed
responses related to bar release or reward, as has been seen in ventral
striatum (Bowman et al. 1996; Schultz et al.
1992
; Shidara et al. 1998
).
To examine the latencies in area TE and perirhinal cortex, we measured the latency for every response that was significantly larger than the background (see METHODS). There was a surprisingly large difference in the latency distributions between TE and perirhinal neurons (Kruskal-Wallis test, P < 0.05; Fig. 6A) with the median being 66 ms longer in perirhinal cortex (TE: median 78 ms, interquartile range 60-115 ms, n = 282; perirhinal: median 144 ms, interquartile range 109-185 ms, n = 233). In contrast, the firing rate distributions overlapped almost completely (TE: median 14 spikes/s, interquartile range 10-20 spikes/s, n = 282; perirhinal: median 11 spikes/s, interquartile range 8-15 spikes/s, n = 233; Fig. 6B). There was no difference in distribution of either latency or firing rate between the cue-related responses and pattern-related responses in either TE or perirhinal cortex (Kruskal-Wallis test, NS). The background activity in these two areas (taken from the 350-ms period before the cue appeared when there was a significant response anywhere in the trial) was similar (TE: median 7.8 spikes/s, interquartile range 5.3-11.5 spikes/s, n = 53; Perirhinal: median 8.6 spikes/s, interquartile range 4.9-11.6 spikes/s, n = 41; Kruskal-Wallis test, NS).
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The strengths of stimulus-elicited responses for perirhinal neurons
were significantly lower than those for TE neurons (Kruskal-Wallis test, P < 0.05). Latency covaries with response
strength to a small degree in both areas (Linear regression,
P < 0.05; perirhinal: slope = 1.20, intercept
=150; TE: slope =
0.65, intercept = 95; Fig.
6C). The intercepts of these linear regressions were significantly different (t-test, t-value = 9.5, P < 0.05), and the slopes were statistically
indistinguishable (t-value = 0.63, NS). Thus the difference
in latency was consistent across the range of overlapping response strengths.
DMS PATTERN-RELATED RESPONSES AND INFLUENCE OF DMS PHASE ON THESE RESPONSES. Fifty TE neurons and 30 perirhinal neurons responded to DMS patterns displayed in the DMS phase of the trial. The neurons responding to the DMS patterns displayed as sample stimuli, referred to as sample responses, always responded to the same patterns when they were displayed as nonmatch or match stimuli.
Of the 50 TE neurons responding to the DMS patterns, 46 showed stimulus selectivity in sample responses (single factor ANOVA, R2 = 0.20 ± 0.03, mean ± SE, n = 46, P < 0.05; Fig. 7). The percentage of TE neurons showing stimulus selectivity is similar to that seen previously (Desimone et al. 1984
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INFLUENCE OF SCHEDULE STATES ON THE DMS PATTERN RESPONSES. To determine whether the schedule states influence the neurons' responses to the DMS patterns, we combined the responses to a given pattern from the sample period with the responses induced by the same pattern from the match period for the neurons that showed no significantly different responses in the two periods. If there was a difference between sample and match responses, then only the sample responses were used in the analysis. In addition, only neurons with at least five trials in each schedule state of any given stimulus were used in the analysis.
Neuronal responses from 23 TE neurons were analyzed; for 15 neurons, the responses from the sample and match periods were combined, and for the other 8, the responses from sample period only were used. Neuronal responses from 19 perirhinal neurons were analyzed; for 11 neurons, the responses from the sample and match periods were combined, and for the other 8, the responses from the sample period were used. The schedule states had a significant influence on the DMS pattern-related responses in 4 of 23 (17%) TE neurons. The TE neuron shown in Fig. 9A responded selectively to the DMS patterns in all schedule states. However, the schedule states influenced both the firing rate and selectivity of the neuron [interaction term of the 2-way ANOVA, F(35, 1490) = 2.53, R2 = 0.05, P < 0.05]. The averaged variance accounted for by the schedule states on the DMS pattern-related responses for the 4 TE neurons was 0.04 ± 0.01 (n = 4). For the remaining 19 TE neurons, the schedule had no influence on the DMS pattern-related responses (interaction term of the 2-way ANOVA, NS).
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CUE-RELATED RESPONSES.
For all 19 TE neurons showing cue-related responses the responses
occurred in all schedule states (example in Fig.
10). Five of the TE neurons responded
identically to the cue's appearance regardless of the schedule state
or the cue's brightness (single-factor ANOVA, NS). The 14 remaining TE
neurons showed response modulation across cues. The same amount of the
response variance (R2 = 0.05 ± 0.01; n = 14) could be explained by the four cue
brightnesses as by the six schedule states (paired t-test,
NS; Fig. 11). Furthermore, for all of
those 14 TE neurons, the responses in the reward states (i.e., 1/1,
2/2, and 3/3 states in which the cues are the same dark bar) were
indistinguishable (single-factor ANOVA, NS). Thus the modulation of
cue-related responses exhibited by TE neurons appears to be related to
the brightness of the cue, suggesting, in line with previous
interpretations, that TE neurons respond to stimulus identity
(Tanaka 1996).
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DISCUSSION |
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In this study, we identified differences in neuronal response properties between TE and perirhinal neurons. We recorded single neurons from both areas while two monkeys performed delayed match-to-sample trials combined with visually cued reward schedules. The visual cue modulated the monkeys' behavior even though there was no requirement for monkeys to notice the cue. This result led us to believe that monkeys voluntarily adjusted their motivation levels according to the schedule. As expected, there are some similarities in the neuronal response properties of TE and perirhinal cortex. Neurons in both areas show similar response properties when the stimuli are only related to the stimulus recognition such as the DMS patterns. Neurons in both areas show stimulus selectivity to the DMS patterns, and the neuronal responses related to the DMS patterns show a small amount of modulation related to the behavioral context of the DMS trial. However, we also have shown here that there are large differences in the neuronal response properties of these two areas, particularly when the visual stimuli, here visual cues, are explicitly related to the reward schedule. First, the response latency distribution in perirhinal cortex is far later than would be expected given the large direct projection from area TE. Second, the responses of the perirhinal neurons related to the reward schedule cue seem to be interpreted most parsimoniously as carrying associative information about the reward schedule; in contrast, the cue-related responses of the TE neurons show modulation related to the visual cue that are best interpreted as conveying information about the cue's brightness. Perirhinal cortex may be important for establishing the relation between expected schedules of work and reward.
Motivation
We have used the monkey's behavioral performance to evaluate its motivational level, and it seems likely that the latter is influenced by both aspects of the task: schedule and individual trial. The influence of the schedule on motivation was very strong when the monkey was performing the task under the cued condition. The monkeys' error rate was low (<10%; see METHODS) when every correctly performed DMS trial was rewarded in the last training period before the schedule was introduced. After the schedule was introduced, the error rates were greatest (~20%) in the trial that was farthest from reward (1/3 schedule state) and lowest (~3%) in the trials closest to reward (1/1, 2/2, and 3/3 schedule states), suggesting that the monkeys are most motivated during trials in which they know the reward is forthcoming. In short, the monkeys' motivation level in the rewarded trial when they were performing the schedule was even higher than the motivation level when they were performing the task in which every correct trial is rewarded (the training condition).
The cue-related behavior of the monkeys in the visually cued schedule
task used here is similar to that seen in earlier studies (Bowman et al. 1996; Shidara et al.
1998
). The monkeys performed DMS trials in the present study
and color discrimination (red-to-green) trials in those previous
studies. Thus the schedule has a large influence on the monkeys'
behavior irrespective of the difficulty or complexity of the underlying
task (DMS vs. color discrimination).
Intuitively, it seems reasonable to expect the complexity of individual
trials also to play a role. The influence of trial complexity is seen
by comparing the monkey's behavior in the shuffled condition here to
the behavior seen in the shuffled condition by Shidara et al.
(1998) (DMS vs. red-to-green color discrimination). In both
cases, the monkeys treated the task as one with a variable-ratio reward
schedule (constant performance across trials) (Mackintosh 1983
). Although the monkeys are performing very well during the shuffled condition here (<10% error), their performance falls significantly short of the best observed (~3%), whereas in the Shidara et al. (1998)
study using color discrimination,
the monkeys performed at the maximum level (i.e., most quickly and
accurately) during the shuffled condition. One possible interpretation
is that DMS is more difficult than color discrimination and hence is
more aversive in some conditions (e.g., in the shuffled condition). However, under the cued condition, apparently the knowledge that a
correct response on the trial will be rewarded overrides the difficulty
and/or aversiveness associated with the individual trials. Thus the
balance between the appetitive and aversive aspects of individual
trials appears to be modulated by both the schedule and the difficulty
of the trials.
Latency
Although the shortest latency for perirhinal neuronal responses is
about the same as the shortest latency for TE neuronal responses (cf.
Fig. 6), the distribution of latencies for perirhinal neurons shifts
from a median of 78 ms in TE to 144 ms in perirhinal cortex, a shift of
66 ms. In area TE the latency has been reported to be 70-120 ms,
whereas in perirhinal cortex the latency has been reported to be as
short as 100 and averaging 150 ms (Baylis et al. 1987;
Nakamura et al. 1994
; Richmond et al. 1983
,
1987
; Xiang and Brown 1998
). Xiang and
Brown (1998)
also found a large latency difference (~70 ms)
across these two areas. The latency difference between these two
directly connected areas is a striking departure from the general
observation that latencies in sequentially connected visual cortical
areas shift by 10-15 ms (Baylis et al. 1987
;
Robinson and Rugg 1988
). Not every study has revealed a difference in latency across these two areas (Nakamura et al. 1994
).
There are presumably many possible explanations for this shift in latency distribution, including a requirement for feedback to perirhinal cortex via several other stages or a systematically high threshold that only can be overcome by prolonged integration of the input signal. Currently we have no evidence that sheds light on the mechanism responsible for the large delay in the latency of perirhinal neurons.
Influence of behavioral context
Overall only about one-quarter of the total response variance in these two areas can be related to the experimental factors (at least using ANOVA). The variance that is explained is distributed differently across the experimental factors in area TE than in perirhinal cortex (see Fig. 17). The variance related to the DMS patterns is larger (~20%) in TE than in perirhinal cortex (~10%). Furthermore the variance related to the cue is only ~5% in TE, and it is related to the cue's brightness, whereas the variance related to the cue is ~10% in perirhinal cortex when it is interpreted in relation to the cue's schedule states.
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It has been shown before and we confirm here that visually elicited
responses during DMS show modulations related to the phase of the DMS
trial, i.e., the responses are often significantly different in the
sample, nonmatch, and match phases (Eskandar et al.
1992; Gross et al. 1979
; Li et al.
1993
; Miller et al. 1993
; Riches et al.
1991
). Here we quantify this DMS phase-related modulation and
show that although it is significant, it is small. In the only previous
study in which the response variance related to the DMS phase was
quantified the amount of variance explained by DMS phase was also small
(Eskandar et al. 1992
). Other studies emphasized
response changes that took place as the stimuli became more familiar
(Li et al. 1993
; Miller et al. 1993
;
Riches et al. 1991
). The response became smaller as the
stimuli became more familiar. Here the stimuli were already very
familiar, and therefore increasing familiarity should play no part in
the small modulations that we report here.
Finally, in area TE the cue-related responses can be regarded as simply sensory driven because TE neurons always fired to the cue's appearance in all schedule states and no extra variance is explained by separating the three end-of-schedule states (1/1, 2/2, and 3/3 states; cf. Fig. 11). The cue-related responses of perirhinal cortex must be regarded as associative and not simply sensory because cue-related responses were differentiable in the three end-of-schedule states (1/1, 2/2, and 3/3 states); in all but two neurons, cue-related responses either disappeared or became indistinguishable in all schedule states under the shuffled condition; and for every neuron, the schedule states account for more variance than cue brightness alone (cf. Fig. 11). Furthermore in perirhinal cortex, the associative effect of the visual cue is as large as the effect related to the DMS patterns. Thus in the conditions used here, it is clear that the physiological properties of perirhinal neurons are distinct from those of TE neurons.
Relation of perirhinal neuronal responses to reward schedules
The responses of neurons in the anterior part of the temporal lobe
(including perirhinal cortex) can be modulated by many factors, such as
stimulus identity (Nakamura et al. 1994; Riches et al. 1991
) and attention (Desimone 1996
;
Richmond et al. 1983
). Here we found that more than half
of the cue-responding perirhinal neurons responded during only a subset
of the trials ending different schedules (Table 1) despite the fact
that the cue was identical for these trials. Furthermore shuffling
eliminated most of the cue-related perirhinal neuronal responses. Thus
perirhinal neurons do not specifically code the presence or absence of
reward nor what the cue looks like, i.e., its brightness, nor the level
of attention directed toward the stimuli. Perirhinal neurons do not respond to bar release or reward delivery as ventral striatum neurons
commonly do (Bowman et al. 1996
; Schultz et al.
1992
; Shidara et al. 1998
).
The most parsimonious interpretation of the cue-related responses of
perirhinal neurons is that these neurons as a population keep track of
progress through these predictable reward schedules. For example, a
neuron in class 3 (see Table 1) may signal the beginning of any
schedule, a neuron in class 2 may signal the beginning of schedules
longer than 1, and the sum of the responses of the two neurons may be
used to indicate the one trial in a single trial schedule. Thus
perirhinal neurons appear to code the associative meaning of the cue
for signaling progress through schedules in a manner similar to the
cue-related responses recorded in the ventral striatum by
Shidara et al. (1998).
Functional role of perirhinal cortex
It has been hypothesized that perirhinal cortex is a critical site
for consolidation and storage of information about objects (Buckley and Gaffan 1998a-c
; Mishkin et al.
1997
; Murray et al. 1998
; Suzuki
1996
). Electrophysiological studies show neurons in the
perirhinal cortex respond selectively to complex objects (Nakamura et al. 1994
; Riches et al.
1991
). Removing the perirhinal cortex produces severe
impairment in object recognition memory (Meunier et al.
1993
) and in the retention of preoperatively learned object discriminations (Buckley and Gaffan 1997
;
Gaffan and Murray 1992
; Thornton et al.
1997
).
An early clue that perirhinal cortex might be related to associative
learning came from Spiegler and Mishkin (1981), who
reported that removal of both area TE and perirhinal cortex produced
impairment in one-trial learning of object-reward associations,
suggesting that perirhinal cortex could play a role in attaching
associative meaning to objects. More recently, Murray et al.
(1993)
and Miyashita et al. (1996)
showed that
monkeys with perirhinal cortex lesions had severe impairments in visual
stimulus-stimulus associations. Other recent work also has shown that
the perirhinal cortex is central for other types of stimulus-stimulus
association as well (Murray and Bussey 1999
). The most recent
behavioral and pharmacological studies support the idea that perirhinal
cortex is important for associative learning (Herzog and Otto
1998
; Murray et al. 1998
). In a direct test, we
recently have found that rhinal cortex lesions severely impair learning
to associate new visual cues with reward schedules of the kind used
here (Liu et al. 1999
). In light of this behavioral
result, our physiological results here support an important role for
perirhinal cortex in the development of associative memories and extend
earlier behavioral findings by showing that association can involve
reward schedules.
Functional difference between TE and perirhinal cortex
On the basis of anatomic, behavioral and electrophysiological
results, the inferior temporal cortical areas are considered to be the
end of a stream of visual processing that emphasizes the identity of
objects, both their physical appearance and memories related to their
identity (Suzuki 1996, Tanaka 1996
). Past
studies of TE and perirhinal cortex generally have been designed to
investigate their role in either object identification or short-term
memory of object identity (Suzuki 1996
). Neurons in both
area TE and perirhinal cortex have shown stimulus selectivity
(Desimone et al. 1984
; Gross et al. 1972
;
Nakamura et al. 1994
; Riches et al. 1991
;
Tanaka et al. 1991
), and our findings in the DMS trials here are consistent with those findings.
Given how strongly perirhinal neurons code information about the
progression of a predictable schedule and given the prominent reciprocal connections between perirhinal cortex and area TE
(Saleem and Tanaka 1996; Suzuki and
Amaral 1994a
), it is surprising that we were only able to
detect signals related to the brightness of the cue in area TE.
Furthermore, because TE projects directly to perirhinal cortex
(Saleem and Tanaka 1996
), the transformation from
stimulus identity in area TE to stimulus meaning in perirhinal cortex
appears to occur in one feedforward processing step. It remains for
future work to identify how this transformation can occur.
Although most single-neuronal recording studies have failed to
distinguish between TE and perirhinal cortex, Buckley et al. (1997), using selective lesions, found behavioral differences between the two areas. Monkeys with removals of the perirhinal cortex
were impaired in performing a short-term memory task but not a
color-discrimination task, whereas the monkeys with removals of area TE
were deficient in performance on the color-discrimination task but not
the short-term memory task. Our results showing that the neuronal
responses of perirhinal neurons code the associative behavioral
significance of the stimulus lead us to suggest that perirhinal cortex
is also critical for associating behavioral meanings with visual
stimuli. At this point, we wonder whether unknown associations also
could give rise to the perirhinal responses seen in the DMS part of
this task. If that was the case, then only one mechanism would be
needed to interpret the responses of perirhinal neurons. In support of
the speculation, it has been shown that pattern selectivity develops
with stimulus-stimulus associations in inferior temporal cortex
(Miyashita 1988
; Sakai and Miyashita
1991
).
Finally, the perirhinal cortex is well-positioned anatomically to
contain the signals we have seen. The signals related to the progress
of a trial schedule could arise from the connections between perirhinal
cortex and areas primarily coding for visual identity such as area TE
(Saleem and Tanaka 1997) and areas related to motivation
and reward, such as amygdala (Aggleton et al. 1980
; Stefanacci et al. 1996
; Van Hoesen 1981
),
ventral striatum (Witter and Groenewegen 1986
), and
probably the ventral tegmental area (Akil and Lewis 1993
,
1994
; Insausti et al. 1987
). Through these connections, perirhinal cortex may be part of a system including ventral striatum and other areas with reward-related signals gauging the relation between work schedules and rewards.
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ACKNOWLEDGMENTS |
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We thank Drs. M. Basso, M. Baxter, M. E. Goldberg, M. Mishkin, E. A. Murray, M. Oram, and M. C. Wiener for valuable discussions of the manuscript.
This work was supported by the Intramural Research Program of the National Institute of Mental Health.
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FOOTNOTES |
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Address reprint requests to B. J. Richmond.
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 18 August 1999; accepted in final form 28 October 1999.
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REFERENCES |
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