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The Journal of Neuroscience, April 15, 2003, 23(8):3407
Internal State of Monkey Primary Visual Cortex (V1) Predicts
Figure-Ground Perception
Hans
Supèr1, 2,
Chris
van der Togt1, 2,
Henk
Spekreijse1, and
Victor A. F.
Lamme1, 2, 3
1 Graduate School Neurosciences Amsterdam, Department
Visual System Analysis, Academic Medical Center, University of
Amsterdam, 1100 AA Amsterdam, The Netherlands, 2 The
Netherlands Ophthalmic Research Institute, 1105 BA Amsterdam, The
Netherlands, and 3 Department of Psychology, University of
Amsterdam, 1018 WB, Amsterdam, The Netherlands
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ABSTRACT |
When stimulus information enters the visual cortex, it is rapidly
processed for identification. However, sometimes the processing of the
stimulus is inadequate and the subject fails to notice the stimulus.
Human psychophysical studies show that this occurs during states of
inattention or absent-mindedness. At a neurophysiological level, it
remains unclear what these states are. To study the role of cortical
state in perception, we analyzed neural activity in the monkey primary
visual cortex before the appearance of a stimulus. We show that,
before the appearance of a reported stimulus, neural activity was
stronger and more correlated than for a not-reported stimulus. This
indicates that the strength of neural activity and the functional
connectivity between neurons in the primary visual cortex participate
in the perceptual processing of stimulus information. Thus, to detect a
stimulus, the visual cortex needs to be in an appropriate state.
Key words:
cortical state; multiunit recording; neurophysiology; V1; primary visual cortex; figure-ground; perception; visual processing; monkey
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Introduction |
Vision depends on the cerebral
cortex. Retinal information enters the primary visual cortex (V1) via
the thalamus and, from there on, is transferred to the higher visual
areas of the cerebral cortex. At these higher levels, more and more
elaborate processing of the visual information occurs. In addition,
feedback connections (Mignard and Malpeli, 1991 ; Salin and Bullier,
1995) and horizontal connections (Gilbert, 1993) provide recurrent
interactions between localized, low-level information and more global,
high-level information (Lamme and Roelfsema, 2000). These kinds of
interactions have been identified as the basis for various modulatory
influences on the neuronal activity in V1 (Gilbert and Wiesel, 1990;
Zipser et al., 1996; Hupé et al., 1998). The observed modulations
often reflect relatively high-level perceptual attributes of the
stimuli that fall within the small receptive fields of the
neurons. For example, perceived brightness (Rossi et al., 1996),
perceptual grouping (Kapadia et al., 1995), or figure-ground
segregation (Lamme, 1995) may modify the response to an otherwise
identical receptive field stimulus.
We reported recently a direct link between these modulations and the
animal's percept. More specifically, we showed that, when
figure-ground modulation in V1 does not occur, the animal does not
perceive the figure (Supèr et al., 2001). Assuming that these
modulations depend on recurrent interactions within V1 and between V1
and higher areas (Lamme et al., 1998), this suggests that the proper
occurrence of recurrent interactions determines whether a stimulus is
processed up to a perceptual level. The question that we address here
is what prevents the normal evolution of such interactions, resulting
in the animal's failure to report the stimulus.
Psychophysical studies in humans show that a stimulus remains unnoticed
during specific states of the subject, such as inattention or
absent-mindedness (Rock et al., 1992; Block 1996). This implies that
the success of stimulus detection depends on the state of the subject,
i.e., on the internal state of the visual cortex. In this study, we
tested monkeys in a figure-ground detection task and we analyzed the
neural activity in the primary visual cortex before the appearance of
the stimulus. This allowed us to determine the influence of the state
of the cortex on stimulus detection without the interference of
stimulus-evoked activity. We show that, for a detected stimulus, the
preceding neural activity was stronger than for a not-detected
stimulus. In addition, the amount of synchrony between neurons was
stronger before correctly reported stimuli than before not reported
stimuli. Thus, the strength of neural activity and the functional
connectivity between neurons in the primary visual cortex predict
whether a stimulus will be perceived or not. Apparently, an appropriate
internal state of the primary visual cortex is essential for the
processing of stimulus information up to a perceptual level.
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Materials and Methods |
Stimulus and task. Stimuli were presented on a 21 inch monitor screen driven by TIGA software. The display
resolution was 1024 × 768 pixels, and the refresh rate 72.3 Hz.
The monkey was seated in a primate chair and placed in a dark room 75 cm from the monitor screen. The screen subtended 28 × 21o of visual angle. In each trial (Fig.
1), a textured figure of (3 × 3o square), defined by a difference in
line orientation, was randomly presented at one of three possible
locations at an eccentricity of 2.74-4.4o
from the fixation point (a central red spot of
0.2o). Before the appearance of the
stimulus screen, the screen consisted of randomly orientated line
segments (prestimulus screen). Onset of figure-present trials consisted
of the abrupt transition from this texture of randomly oriented line
segments into a texture of oriented line segments with a
90o orientation difference between figure
and ground. On catch trials, all line segments had the same
orientation, so that no figure appeared. Line segments were 16 × 1 pixels (0.44 × 0.027o), and the
density was five line segments per square degree.
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Figure 1.
Illustration of the prestimulus screen and
figure-ground display (stimulus screen) and sequence of visual
stimulation. A, The prestimulus screen consisted of
randomly orientated line segments. In the stimulus screen, a
90o difference in orientation of the line segments
results in a figure-ground percept. The figure could appear in one of
three possible locations. Illustrations are not at scale.
B, Animals started to fixate 300 msec before onset of
the stimulus screen and identified the presence or absence of a figure
after stimulus onset by making an eye movement toward it or maintaining
fixation, respectively.
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Two monkeys (Macaca Mulatta) were trained to fixate at the
fixation point on the monitor and to make a saccade toward the figure
location, as soon as it appeared (300 msec after fixation onset) or to
maintain fixation when no figure appeared (catch trials; 20% of the
trials). Trials in which the eye left the fixation window (1 × 1o) before figure appearance were aborted
and discarded. After stimulus appearance, figure-present trials were
considered correct ("reported") when a saccade entered the figure
window (at the approximate size and position of the figure) within 500 msec. Otherwise the trial was incorrect ("not reported"). Catch
trials were considered correct when the animal maintained fixation for
500 msec after stimulus onset and incorrect when the fixation window
was left before 500 msec. Eye movements were monitored using scleral
search coils with the modified double-magnetic induction method and
digitized at 400 Hz. (Bour et al., 1984).
Recordings and data analysis. Multiunit neural activity was
recorded through microwire electrodes (16 electrodes per animal, selected out of ~40 implanted ones, impedances of 100-350 k , at
1000 Hz) that were surgically implanted into the operculum of area V1.
The obtained signals were amplified (40,000×), bandpass filtered
(750-5000 Hz), full-wave rectified, and then low-pass filtered (<200
Hz). The resulting low-frequency signal represents the amount of
spiking activity (Legatt et al., 1980). Before the experiments,
aggregate receptive field size and position at each electrode was
determined, using moving bars. Receptive field size ranged from 0.4 to
1.0°, and eccentricity ranged from 1.25 to 5.7°. For each monkey,
figure positions and electrodes were chosen such that the figure in one
location covered the receptive fields of the 16 electrodes
simultaneously ("figure" condition). Therefore, many recorded
neurons had overlapping receptive fields (Supèr et al.,
2001). In the other two figure locations, the receptive fields were
covered by ground ("ground" condition). For the analysis, we
averaged the data obtained from all figure positions, i.e., both figure
and ground conditions.
We subtracted the DC component (average baseline activity from 0 to 30 msec after stimulus onset) from the responses. Moreover, the average
responses at each electrode were normalized; at each electrode, the
responses were divided by a constant factor, which was the maximum
response found for any of the conditions (i.e., correct, incorrect,
figure, ground, etc.), obtained within a 500 msec recording period,
starting from 300 msec before stimulus onset until 200 msec after
stimulus onset (to avoid contamination attributable to
saccades). This way, each electrode contributed equally to the
population average, yet relative differences between conditions were
maintained despite the normalization. Data were obtained during several
sessions, and figure-present trials were randomly interleaved with 20%
catch (figure-absent) trials.
For the analysis of coherent activity, two-dimensional
cross-correlograms with time versus lag on the x-axis and
y-axis and correlation strength on the vertical (color) axis
[joint peristimulus time histograms (J-PSTHs)] were calculated. The
activity from an electrode i is represented as
Sir(t)
for the r-th trial (Brody, 1999). P represents
the averaged response or PSTH. We calculated a matrix of covariances
for all combinations of electrodes averaged over all trials.
Shuffle-corrected covariance matrices are represented as follows:
This denotes the averaged (over all trials, r)
cross-product of the responses from electrode i and
j, minus the cross-product of the averaged responses. The
cross-product of the PSTHs has been termed the shuffle predictor and is
used to reduce common input attributable to the stimulus (Palm et al.,
1988). This equation is also known as the un-normalized JPSTH (Aertsen
et al., 1989; Brody, 1999) and was used in this study for the
calculation of the correlations. To reduce covariances attributable to
common changes in excitability, we also subtracted the DC from each
individual response. The time-dependent SE of the electrode
response i is deduced from the auto-covariance matrix of
i:
This is the square root of the values on the main diagonal of
the auto-covariance matrix. Normalized covariance matrices or
normalized J-PSTHs can then be defined as follows:
In this equation, division with the cross-product of the
time-dependent SDs of the i-th and j-th electrode
is used to normalize the covariance matrix to obtain a
cross-correlation matrix.
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Results |
Stimulus detection
Monkeys were trained to report the presence or absence of a figure
in a figure-ground detection task. Animals fixated a small central red
dot on the prestimulus screen, which consisted of a texture of randomly
orientated line segments (Fig.
1A). At stimulus onset (t = 0 msec),
a textured figure-ground display (figure-present trials) appeared, and
the monkey was rewarded after making a saccade toward the location of
the figure (Fig. 1B). In catch trials, no figure was
presented, and the monkey was rewarded when maintaining fixation for
500 msec. This way, the monkey could report that he either perceived or
did not perceive the stimulus (Moore et al., 1995; Supèr et al.,
2001).
On average, the detection performance as measured by d' was
2.35. The animals made a correct eye movement (hits, reported condition) in 84% of the figure-present trials and failed to
report the figure (misses, not-reported condition) in 16% of the
figure-present trials. Total number of hits was 4948, and total number
of misses was 954. In these not-reported figure-present trials, i.e.,
the misses, the monkey either maintained fixation or made an incorrect eye movement (and, in that sense, the term miss is not used in the
standard Signal Detection Theory connotation). In the figure-absent (catch) trials, the animals scored 88% correct (correct rejections) and 12% incorrect (false alarms). Although the location of the figure
varied across trials, important factors for detection, such as its
eccentricity or shape, or the difference in texture orientation between
figure and background, were the same on every trial. Therefore, failure
to detect the figure did not relate to the stimulus (e.g., the saliency
of the figure) but was attributable to the subject.
Prestimulus responses and stimulus detection
To understand how the internal state of the subject affects
sensory processing, we analyzed neural activity of the primary visual
cortex just (300 msec) before the onset of the figure-ground stimulus
for both reported and not-reported trials. Analyzing the prestimulus
responses allowed us to study the state of the cortex in relation to
stimulus detection without the interference of stimulus-evoked
activity. During the first 200 msec after the start of fixation, the
average strength of neural activity was similar for reported and
not-reported figure-present trials (p = 0.72;
Mann-Whitney U test) (Figs.
2, 3A). However, starting 100 msec before the appearance of the
stimulus, the activity for the reported
figure-present trials increased (compared with the activity during the
200 msec after fixation) and, moreover, was significantly stronger than
for the not-reported figure-present trials (p < 0.01; Kruskal-Wallis test) (Figs. 2, 3B). Thus, the strength of the neural activity just before the presentation of a
stimulus relates to whether that stimulus will be reported or not.
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Figure 2.
Example of a normalized average neural response
obtained at a typical recording site. Time 0 equals stimulus onset. The
animal started to fixate 300 msec before stimulus onset, causing an
initial decrease and subsequent increase of activity. The thick line
represents the average response for hits (reported figure-present
trials), and the thin line represents the average response for the
misses (not-reported figure-present trials). Dots represent the SEM of
the reported trials response.
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Figure 3.
Prestimulus activity for reported and not-reported
trials of all of the figure-present trials. A,
B, Mean strength of activity during the 200 msec period
after fixation (A) and during the interval of 100 msec before stimulus onset (B) for each
electrode, plotted for reported trials against not-reported trials.
Different symbols represent different animals.
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In a previous study, we showed that the late part of the
stimulus-evoked response also correlates with the detection of the figure-ground stimulus, whereas the early, transient response does
not; more specifically, we showed that, when figure-ground modulation
(which is a difference in response to figure elements compared with
ground elements, starting at ~80 msec) is absent, animals do not
detect the figure. Here, we averaged over figure and ground responses
(because we used all figure positions), and figure-ground modulation
cannot be observed (for that analysis, see Supèr et al., 2001).
To know whether the prestimulus responses are equally strong for all
stimulus conditions, we analyzed the responses for each stimulus
condition separately. These results show that the strength of the
prestimulus activity is not significantly different between the figure
and ground figure present trials (p = 0.68;
Mann-Whitney U test) (Fig.
4A,C).
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Figure 4.
Prestimulus activity for figure-present and
figure-absent (catch) trials. A, Average activity during
the epoch between  100 and 0 msec relative to stimulus onset for
reported figure trials versus reported ground trials. B,
Average activity during the epoch between 100 and 0 msec relative to
stimulus onset for correct catch trials against incorrect catch trials.
Different symbols represent different animals. C,
Population data for correct and incorrect figure (Fg), ground (Gr), and
catch (Ct) trials.
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Analysis of the figure-absent (catch) trials showed that, for
one animal ("Uri"), the prestimulus activity for correct catch trials (in which fixation was maintained) was significantly stronger than for incorrect catch trials (p = 0.001;
Mann-Whitney U test) (Fig.
4B,C). For the other animal, we
observed no difference. A possible explanation could be that the latter
animal frequently went through periods of low arousal or inattention
and then merely maintained fixation. This would have resulted in many
correct catch trials and, correspondingly, in many not-reported
figure-present trials. Assuming that higher than default prestimulus
activity is needed for proper perception, this would then weaken the
prestimulus effects for the catch trials but not for the figure-present trials.
To further support the contingency between the strength of the
prestimulus activity and behavioral performance, we sorted prestimulus
activities of all trials into ascending order and then divided them
into eight bins, each containing the same number of trials. Each trial
within a bin is associated with both the strength of prestimulus
activity and the task-related behavioral data (i.e., figure-present or
figure-absent trials, and correct or incorrect responses). For each
bin, we computed the average prestimulus activity and computed
d' (Green and Swets, 1988; Ress et al., 2000). These results
show that the prestimulus activity was quantitatively related to
behavioral performance (Fig. 5). Linear
regression showed a significant positive relationship (for each animal,
p < 0.05) between strength of prestimulus activity and
d'. This only applied to the activity immediately (100 to 0 msec) preceding stimulus onset. The early prestimulus activity (300 to 100 msec before stimulus onset) showed no correlation with
performance.
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Figure 5.
Quantitative relationship between prestimulus
activity and detection performance. Trial-to-trial prestimulus activity
was binned into eight different levels of strength, and detection
performance (d') was calculated for each of the bins.
Data shown represent the average of two animals. Line is the linear
regression.
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A relationship between prestimulus and poststimulus activity
Our present and previous (Supèr et al., 2001) findings
combined suggest that there are periods before stimulus onset (100 to
0 msec) and after stimulus onset (>100 msec) that both correlate with
figure detection, with a period in between (the response transient,
30-100 msec) that shows much less correlation with perception. This
suggests that there might be a relationship between these two periods
of neural processing. We therefore conducted a correlation analysis
between the different prestimulus and poststimulus intervals. We
computed, per trial, the average prestimulus activity (100 to 0 msec)
and the activity during two poststimulus periods. The findings show
(Fig. 6) that the strength of the
prestimulus activity correlates better with the late part of the
stimulus-evoked response (100-200 msec) than with the early, transient
response (0-100 msec). Thus, the early part of the response is
dominated by the stimulus, whereas the late response reflects the
influence of both the stimulus and the internal state of the
animal.
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Figure 6.
Relationship between different epochs of neural
responses during stimulus detection. Shown are the strengths of
correlation between the mean responses levels during three periods
relative to stimulus onset: 100 to 0 msec (Pre), 0 to 100 msec
(Post1), and 100-200 msec (Post2).
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Motivation of the animal and stimulus detection
It is possible that motivation of the animal influenced
performance. Animals could be better motivated at the start of the recording session than at the end, when satiated with rewards or tired.
If that were influencing our results, one would expect most of the
not-reported trials at the end of a recording period. Therefore, we
analyzed the performance of the figure-present trials throughout the
recording sessions. We divided each recording session (34 in total)
into 20 equal time bins, counted the number of hits (reported
figure-present trials) and misses (not-reported figure-present trials)
per bin, and calculated percentage correct. Through these data points,
we fitted a linear regression line to determine a possible correlation
between accuracy and session time. These results show that the
performance remained at a constant level throughout the recording
session (Fig. 7A), which
indicates that the motivation of the animal remained constant during
the experiment. This finding therefore indicates that the failure to
detect the stimulus is not attributable to monotonic changes in the
motivational state of the animal. In addition, we analyzed the strength
of the prestimulus activity in data collected during the first, middle, and final thirds from each session (Fig. 7B). These results
show that the increase in prestimulus activity for reported
figure-present trials, compared with the prestimulus activity for
not-reported trials, is not significantly different for the three
periods (p > 0.1; Kruskal-Wallis test). The
performance remained constant during these three periods (83, 86, and
82% correct, respectively). These observations agree with the
suggestion that the difference in prestimulus activity between reported
and not-reported figure-present trials is not attributable to the
monotonic changes in the animal's motivation.
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Figure 7.
Performance and prestimulus responses throughout
the recording session. A, Each recording session was
divided into 20 time slices, and percentage reported figure-present
trials was calculated for each slice. A linear regression line is
plotted through the data points. B, Differences in the
average prestimulus activity (100 to 0 msec) between reported
figure-present trials and not-reported figure-present trials, for the
first, middle, and final periods of the recording session. Error bars
are SEM.
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Eye movements
An additional potential concern was that the position or movements
of the eyes could differ in some subtle respect during the fixation
period and that this caused the differences in the prestimulus
response. To control for differences in fixation behavior, we separated
the eye movements of the figure-present trials according to response
type (reported and not-reported) and analyzed the 100 msec interval
before stimulus onset. We calculated the SDs of the x and
y coordinates of the eye position during each trial. The
higher this value, the less accurate fixation was maintained. The
results show that the average SDs for reported and not-reported figure-present trials were not different [horizontal, 0.073 (reported trials), 0.064 (not-reported trials); vertical, 0.087 (reported trials), 0.089 (not-reported trials); p > 0.2;
Kruskal-Wallis test]. In addition, no significant difference in the
mean eye position and the SD of the average eye position was found
between correct and incorrect responses [mean ± SD; horizontal,
0.056 ± 0.036 (reported trials), 0.059 ± 0.052 (not-reported trials); vertical, 0.080 ± 0.050 (reported trials),
0.086 ± 0.057 (not-reported trials); p > 0.05;
ANOVA]. Therefore, these observations indicate that differences in
prestimulus activity between reported and not-reported trials are not
the result of differences in eye movements.
Synchrony and stimulus detection
To test whether the changes in prestimulus firing rate were
accompanied by other manifestations of a change in cortical state, we
investigated the functional connectivity between neurons by calculating
the strength of correlated firing over time. We constructed auto- and
cross-covariance matrices for each possible pair of recording sites. By
normalizing these matrices with the cross-product of the SDs of the
PSTHs, we obtained J-PSTHs. These J-PSTHs show the variation in time of
the strength of the correlation between two neurons (Aertsen et al.,
1989; Vaadia et al., 1995). The diagonal of such a matrix represents
the correlation strength at zero-time lag, and the points above and
below this diagonal represent positive and negative time delays between
the two neurons. Analysis of these J-PSTHs revealed that before the
presentation of the stimulus, correlated activity between neurons was
observed for both the reported and not-reported conditions, whereas
after stimulus onset, these correlated responses tended to reduce or
disappear (Fig. 8). Thus, correlated
firing of neurons in the primary visual cortex was present before the
onset of the figure-ground texture.
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Figure 8.
Correlated activity and behavioral response.
Example of correlated firing of two neurons over time. Each pixel of
the color-coded matrices depicts the normalized correlation coefficient
at a particular correlation lag and time delay relative to stimulus
onset. Left matrix shows data from hits (reported trials), and right
matrix shows data from misses (not-reported trials). Color scale (as
shown in the middle bar) is from blue (minimum) to red (maximum). The
prestimulus and poststimulus neural responses are shown as PSTHs (see
Fig. 2) and are plotted for comparison in black along the
x-axis and y-axis. The white squares
indicate the windows for the calculations of the average
cross-correlograms (see Fig. 9).
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To quantify the strength of these prestimulus correlations, we
calculated the average cross-correlation functions of the 100 msec
period before stimulus onset. The strength of the correlation was
estimated by the area under the peak within a window ranging from
12.5 to 12.5 msec lag. Of all possible electrode pairs (120 per
animal), 96% had significant (p < 0.01)
positive correlation (mean ± SEM correlation strength; monkey
"Toni," 0.065 ± 0.003; monkey "Uri," 0.039 ± 0.002)
before stimulus onset in correctly reported figure-present trials.
Before not-reported trials, only 85% of the electrode pairs showed
significant (p < 0.01) positively correlated
activity (mean ± SEM correlation strength; monkey "Toni," 0.052 ± 0.005; monkey "Uri," 0.034 ± 0.005). In
addition, the strength of prestimulus correlation was lower for
not-reported trials than for reported trials (Fig.
9). Of all the electrode pairs, 73%
showed stronger correlation in the reported condition than in the
not-reported condition (Toni, p < 10 11; Uri, p < 106; paired t test). The
average cross-correlation functions thus show that, during the
prestimulus period, neural activity is more synchronous in the reported
trials than in the not-reported trials.
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Figure 9.
Average cross-correlograms for all electrode
pairs. A, The cross-correlogram for the electrode pair
in Figure 8. Thick lines represent hits (reported trials), and thin
lines represent the misses (not-reported trials). Dots represent SEM of
the reported trials. B, Scatter plot showing the average
cross-correlation coefficients (Corr. Coeff.) during the 100 msec
preceding stimulus onset for incorrect trials versus correct trials for
all electrode pairs. Different symbols represent different
animals.
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Discussion |
In summary, our present results indicate that both the strength
and the amount of synchrony of neural activity in the primary visual
cortex during a ~100 msec period before the appearance of a
behaviorally important stimulus predicts whether that stimulus will be
detected or not. Note that these are independent results, because we
calculated the amount of synchrony by normalizing the correlation
coefficients for overall activity level. Together, our results provide
direct evidence for the idea that the internal neural state of the
subject at the moment of stimulus arrival affects subsequent stimulus detection.
It has been shown that states of attention and expectancy (Egeth and
Yantis 1997; Pashler, 1998) are accompanied by a general increase of
synchronous neural activity and neural responses (Cardoso de Oliveira
et al., 1997; Riehle et al., 1997; Steinmetz et al., 2000; Ress et al.,
2000; Fries et al., 2001). In addition, different states of arousal
have been associated with different firing patterns of cortical neurons
(Evarts, 1964; Steriade et al., 1993), and dynamical switches between
these states have been shown to occur in the awake animal (Nunez, 1995;
Sherman, 2001). The present results are in accordance with these
findings, in the sense that attention, arousal, or an interaction
between these two (Coull, 1998) may explain our observations. Note,
however, that our effects are not location specific: enhanced and more
synchronous activity at the site of the recording electrodes promotes
the detection of a stimulus at any location. Variations in focused
attention therefore do not explain our results. It is more likely that
spontaneous variations in the general state of the cortex underlie our findings.
We found that the activity immediately (~100 msec and not earlier)
preceding the onset of the stimulus was related to the animal's
perception of that stimulus. This indicates that a switch in cortical
state occurs within a relatively short time frame (much shorter than a
single trial or else we should have found activity to be related
already at fixation). Apparently, the visual cortex has to quickly
attain an appropriate state before the stimulus information enters the
cortex. Enhanced and synchronized activity preceding stimulus
presentation results in correctly detected stimuli in which the failure
to detect the stimulus follows a moment of reduced and less
synchronized activity. The failure to develop an appropriate cortical
state may thus represent a neurophysiological correlate of a moment of
inattention or reduced expectancy or a state of low arousal of the
subject. This deviates a little from the standard concept of arousal.
Changes in states of arousal as measured by EEG recordings, for
example, generally last for longer time periods (seconds to hours)
(Atlas Task Force of the American Sleep Disorders Association, 1992;
Coull, 1998; Drinnan et al., 1998). This is quite at odds with the fast
changes in cortical state that are shown here. These are more in line with temporal changes in EEG activity that have been associated with
changes in attention and discrimination (Vogel and Luck, 2000; Arnott
et al., 2001; Bastiaansen and Brunia, 2001) or with the dynamical
switches in neural spiking behavior (bursting vs tonic) that have been
shown to occur in the thalamocortical circuit of awake animals
(Sherman, 2001).
Recently, we reported about the neural activity recorded after onset of
the figure-ground display in this paradigm (Supèr et al., 2001).
We discerned two stages of processing after stimulus onset: the one
dominated by the early (<100 msec) response transient, the other
occurring at relatively longer latencies (> 100 msec). The early stage
is associated with feedforward processing and early feature extraction,
and the later stage is associated with recurrent processing and
higher-level visual processes such as perceptual grouping and
segmentation (Lamme and Roelfsema, 2000). For example, at a latency of
~100 msec, V1 single and multiunit responses are stronger when the
line segments within the receptive field of the neurons belong to the
figure compared with when they belong to the background, a phenomenon
termed contextual modulation (Lamme, 1995; Zipser et al., 1996; Lamme
et al., 1999).
In our previous study, we found that early stimulus driven activity
(0-100 msec) did not relate to whether the figure was seen or not
seen. However, when contextual modulation was absent, animals did not
see the figure (Supèr et al., 2001). Also, contextual modulation
is selectively suppressed in anesthetized animals, although responses
remain selective for low-level features such as orientation of texture
bars (Lamme et al., 1998a). Like the prestimulus activity reported
here, late-onset contextual modulation thus relates to the processing
of visual information up to a perceptual stage. A difference between
the two, however, is that higher and more synchronous prestimulus
activity promotes figure detection at any location, whereas
figure-ground contextual modulation is confined to the region of the
figure (Lamme, 1995; Lamme et al., 1999). In contextual modulation, the
response to figure elements is enhanced compared with background
elements when a figure is perceived, although this enhancement is
absent when a figure is not perceived (Supèr et al., 2001).
Apparently, V1 activity related to detection performance is initially
not confined to a particular spatial region but becomes spatially
selective during the late period of the stimulus-evoked response.
Whether these prestimulus and poststimulus response modulations
represent similar or related neural mechanisms remains to be investigated.
The late stimulus-driven response modulations representing
figure-ground segregation have been conjectured to depend on
horizontal connections within V1 and feedback connections between V1
and higher visual areas (Payne et al., 1996; Lamme et al., 1998b; Wang
et al., 2000). On that basis, we suggested that perception depends
strongly on recurrent interactions between visual areas (Lamme, 2000;
Supèr et al., 2001). Taking these and the present results
together, it appears that the different states of the brain preceding
stimulus onset (receptive vs unreceptive, so to say) have little or no
effect on the early activity that is evoked by the stimulus but are
specifically associated with the occurrence of later recurrent
interactions between areas, reflecting figure-ground perception. This
idea is supported by the finding that anesthesia has relatively little
effect on feedforward responses in V1, reflecting receptive field
tuning properties, whereas figure-ground modulation is abolished by
anesthesia (Lamme et al., 1998a).
| |
FOOTNOTES |
Received June 24, 2002; revised Dec. 23, 2002; accepted Jan. 2, 2003.
H.S. is supported by a grant from Medical Sciences, which is subsidized
by the Netherlands Organization for Scientific Research. We thank Kor
Brandsma and Jacques de Feiter for biotechnical support and Peter
Brassinga and Hans Meester for technical assistance.
Correspondence should be addressed to Hans Supèr, Graduate School
Neurosciences Amsterdam, Department Visual System Analysis, Academic
Medical Center, University of Amsterdam, P.O. Box 12011, 1100 AA
Amsterdam, The Netherlands. E-mail: h.super{at}ioi.knaw.nl.
| |
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TOP
ABSTRACT
Introduction
