Cognitive Brain Research 13 (2002) 377–392
www.elsevier.com / locate / bres
Research report
Effects of picture repetition on induced gamma band responses,
evoked potentials, and phase synchrony in the human EEG
¨
Thomas Gruber*, Matthias M. Muller
Cognitive Neuroscience, Department of Psychology, University of Liverpool, Eleanor Rathbone Building, Liverpool L69 7 ZA, UK
Accepted 10 December 2001
Abstract
Repeated experience with an object due to prior exposure to that object is commonly referred to as perceptual or repetition priming.
One possible neuronal mechanism for repetition priming is ‘repetition suppression’ within a cell assembly coding the stimulus. Recently,
induced gamma band responses (GBRs) were discussed as a possible physiological correlate of activity in such a cell assembly. The
present EEG study was designed to investigate the modulation of induced GBRs when line drawings were presented either once or
consecutively two or three times. Results showed a broad distribution of spectral gamma power and synchrony after initial picture
presentation. Repeated presentations of the same picture led to a decrease of induced gamma power and less synchronized activity
between distant electrode sites. The decrease of induced GBRs and synchrony after repeated picture presentations may be linked to a
‘neural savings’ mechanism within a cell assembly representing an object. Furthermore, the visual evoked potential, which was modulated
by priming, showed a topographically different distribution compared to induced GBRs.  2002 Elsevier Science B.V. All rights
reserved.
Theme: Neural basis of behavior
Topic: Learning and memory: systems and functions
Keywords: Human high density EEG; Induced gamma band response; Synchrony; Repetition priming; Repetition suppression; Vision
1. Introduction
Cortical object representations are considered to be
established by synchronized neuronal activity within cell
assemblies, which integrate neural activity from different
cortical areas processing different aspects of an object [25].
Repeated experience with an object due to prior exposure to that object is commonly referred to as perceptual or
repetition priming [63]. Perceptual priming is (a) preserved
in amnesia [17], (b) unaffected by semantic processing and
encoding [40], (c) resistant to manipulation of particular
stimulus attributes [69], and (d) can occur in the absence
of conscious perception [27]. Thus, it was suggested that
implicit forms of memory could be clearly dissociated
from explicit recollection processes [50,52]. Schacter and
Tulving proposed that repetition priming and implicit
*Corresponding author. Tel.: 144-151-794-6706; fax: 144-151-7942945.
E-mail address: [email protected] (T. Gruber).
memory largely reflect changes in a cortically based,
presemantic perceptual re-presentation system (PRS), composed of several domain-specific subsystems [49]. Behaviourally, the PRS improves processing of previously
seen objects, relative to novel stimuli, resulting in shorter
reaction times for repeated objects [6,69]. A possible
neural mechanism for perceptual priming is the so-called
‘repetition suppression’ effect [51], i.e., a reduction of
neuronal firing rate to repetition of a stimulus [2,4,5].
Recently, Desimone [7] suggested that repetition suppression is a by-product of a ‘sharpening’ of the stimulus
representation in the cortex. This ‘sharpening’ provides a
neuronal network that becomes sparser and more selective
with repeated experience with the stimulus. Neurons,
which show a decreased response when stimuli recur, drop
out of the population of activated cells coding the stimulus
and, thus, yield a more efficient cell assembly representing
the particular object. With respect to the visual system it
has been suggested that such a cell assembly is widespread
with members in a number of visual areas [9,62,70].
0926-6410 / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved.
PII: S0926-6410( 01 )00130-6
378
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
Furthermore, there is empirical evidence for two main
streams of information processing [30,62,70]. The ventral
pathway is thought to be specialized for the analysis of
object features like color and shape, whilst the dorsal
pathway is specialized for the analysis of motion and
spatial relationships between objects [46,64,65]. Given
these findings, the question arises as to the mechanism that
integrates the neuronal activity within and between the
elements of a network representing a stimulus. Theoretical
considerations and intracortical recordings in animals
suggest that synchronized bursts of action potentials in a
frequency range above 20 Hz (i.e., the gamma band) are
the key neural mechanism for the constitution of such a
network [11,12,33,34,53,54]. It has been found, that these
synchronized neural activities are neither phase nor timelocked to stimulus onset [8]. Therefore, they are termed
induced gamma band responses (GBRs) in order to
distinguish them from stimulus locked evoked responses,
such as the event-related potential (ERP) and the evoked
gamma band response. In a number of previous studies it
has been shown that induced GBRs in the human EEG are
(a) modulated by the features of a stimulus and, thus, are
closely linked to visual information processing
[26,32,37,38,45,59,61], (b) modulated not only by the
features of a stimulus but also by visual spatial attention
[16,36], and (c) can be induced by recalling a learned
representation of a stimulus [14,15,43]. Recapitulating the
above findings, induced GBRs may be regarded as a
signature of a Hebbian cell assembly formed on the simple
rule: ‘‘cells that fire together wire together’’ [19]. This
means synchronized neural activity is necessary to establish a stimulus representation in the brain.
Recently, it has been shown, that gamma power alone is
an insufficient marker for synchronous activity between
different cortical areas [29,35,45]. These authors suggested
that phase synchrony between pairs of electrodes, independent of amplitude, provides a better measure of
synchronized neural activity establishing a cell assembly.
In a perceptual learning task, an increase in gamma power
at electrode sites over visual cortical areas, which coexisted with an increase in phase synchrony was shown
[15]. However, Rodriguez et al. [45] demonstrated that
desynchronization co-existed with periods of above-average gamma power. Thus, changes in gamma band spectral
power should not be simply confounded with phase
synchrony.
To study the effects of picture repetition on induced
GBRs and phase synchrony we have designed an EEG
experiment in which we presented our subjects line
drawings of the Snodgrass and Vandervart picture set [56]
either once or consecutively two or three times. Following
our argumentation above we hypothesize that a visually
presented stimulus is processed in a widespread cell
assembly. In line with previous experiments in humans
using visual stimuli (for reviews see Refs. [25,58]) we
expected to find a topographically widespread distribution
of gamma power having a maximum over posterior
electrode sites when subjects were confronted with an
object for the first time. Furthermore, we predicted to find
synchronized activity between pairs of electrodes at posterior sites during the first presentation. Any further presentation of the same object should lead to a sharpening of
the network, which codes the stimulus. In macroscopical
EEG recordings, due to spatial averaging, activation of
fewer neurons representing a stimulus must result in a
reduction of induced gamma power. A further consequence
of this sharpening process might be visible in the phase
synchrony between pairs of electrodes. A sparser network
should lead to a reduced number of electrode pairs
exhibiting significant synchronous activity.
To demonstrate that a modulation of GBR power is
linked to induced and not evoked neural activity we have
also analyzed the evoked GBR. Due to the jitter in latency
of high-frequency activity in single trials, we expected no
significant differences between initial and repeated picture
presentations. Furthermore, an important question is
whether or not induced GBRs reflect the same neural
mechanism as opposed to ERPs. Based on earlier eventrelated potential studies using repetition priming
paradigms, it might be expected to find a modulation of the
ERP comprising topographically distinct components, as
demonstrated for example in studies on picture priming
[47,71]. Earlier studies examining high-frequency brain
activity suggest that the ERP may play functionally
different roles in perception as compared to induced
gamma band responses [15,58,59]. Thus, we expect a
topographical difference between repetition effects in the
ERP and the GBR.
2. Materials and methods
2.1. Subjects
Twelve healthy, right-handed university students (eight
female, four male) received class credits or a small
financial bonus for participation. Their age ranged from 20
to 29 (mean, 23.34 years; S.D. 2.61 years). All had normal
or corrected-to-normal visual acuity. Informed consent was
obtained from each participant.
2.2. Stimuli and task
Stimuli were 260 line drawings taken from the Snodgrass and Vandervart inventory [56] in their complete
version, which were presented in the center of a 19-inch
computer screen placed 1.5 m in front of the subjects with
a frame rate of 70 Hz. A white fixation square of 0.330.38
of visual angle was always present in the center of the
screen. The line drawings covering a visual angle of
approximately 4.535.28 were shown in white on a black
background. Picture onset was synchronized to the vertical
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
retrace of the monitor. The line drawings were presented
either once or consecutively two or three times. Pictures
were chosen randomly from the picture pool. Subjects
were instructed to react to rare targets (pictures of insects)
by pressing a button to ensure attention to the presented
objects (see Fig. 1-I for a schematic representation of the
experimental set-up). The likelihood that a picture was
shown only once was 0.29. With the same likelihood a
picture was presented two or subsequently three times.
With a likelihood of 0.13 a target was presented at
unpredictable positions in the stream of line drawings
(again once or consecutively two or three times). The
experiment consisted of 549 trials, from which target trials
were excluded from further analysis (approximately 70
trials).
The design resulted in two experimental conditions: (1)
379
initial picture presentations, and (2) repeated picture
presentations. Conditions were presented in randomized
order and were subdivided according to their presentation
numbers: (A) initial presentation without repetition; (B)
initial, and (C) a subsequent second presentation of the
same stimulus without a third presentation; (D) first, (E)
second, and (F) third presentation of the same picture (see
Fig. 1-I). For every initial presentation a new picture was
chosen randomly from the Snodgrass picture pool. Subjects received immediate feedback upon performance, i.e.,
correct or false detection of a target or a miss of a target.
Each trial consisted of a 500-ms baseline period (black
screen), 700-ms picture presentation time and an interstimulus interval between 1100 and 1600 ms. Whenever
the subject pressed the button to indicate the detection of a
target, the next trial was started with an additional delay of
Fig. 1. (I) Schematic representation of the experimental set-up. Pictures are presented either once or consecutively two or three times: (A) initial
presentation without repetition; (B) initial, and (C) second presentation of the same stimulus without a third presentation; (D) initial; (E) second; and (F)
third presentation of the same picture). (II) Repeated measurement ANOVA model used for initial statistical analysis (29 refers to electrodes 1–29 as
indicated in Fig. 2).
380
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
500 ms. This was done in order to avoid contamination of
the following 500-ms black screen baseline period by the
post motor potential [39]. Subjects were instructed to avoid
eye movements and blinking while attending the pictures
and to maintain their gaze on the fixation square in the
middle of the screen. To allow subjects to have breaks
within the EEG session, the experimental design was
divided into three blocks of 183 trials each.
2.3. Electrophysiological recordings
EEG was recorded continuously with an EGI (Electrical
Geodesics, 1998) 128-electrode array. A schematic representation of the electrode array and corresponding extended international 10–20 electrode sites is given in Fig. 2.
The vertex (recording site Cz) was chosen as reference. As
suggested for the EGI high input impedance amplifier,
impedances were kept below 50 kV. Sampling rate was
500 Hz and all channels were pre-processed on-line by
means of a 0.1–200-Hz band-pass filter. In addition,
vertical and horizontal eye movements were monitored
with a subset of the 128 electrodes. Further data processing
was performed off-line.
cedure uses a combination of trial exclusion and channel
approximation based on statistical parameters of the data.
Firstly, global artifacts stemming from single electrodes
are detected using the recording reference (Cz) and the
average reference. In the next interactive step, distinct
sensors from particular trials are removed on the basis of
the distribution of their amplitude, standard deviation and
gradient. The information of eliminated electrodes is
replaced with a statistically weighted spherical interpolation from the full channel set. In the last step, the variance
of the signal across trials is computed to document the
stability of the average waveform. The limit for the
number of approximated channels was set to 20 channels.
With respect to the spatial arrangement of the approximated sensors, it was ensured that rejected sensors were
not located within one region of the scalp, as this would
make interpolation for the area invalid. Single epochs with
excessive eye-movements and blinks on more than 20
channels containing artifacts were discarded. Using this
method, two subjects were excluded due to excessive
artifacts. For the remaining 10 subjects the average rejection rate was approximately 20% resulting in about 70
remaining trials per condition. For further analysis the
average reference was used.
2.4. Data reduction and analysis
2.5. Data analysis: induced spectral changes
EEG was segmented to obtain epochs containing 500 ms
prior to and 1200 ms following stimulus onset. Artifact
correction was performed by means of the ‘statistical
correction of artifacts in dense array studies’ (SCADS)
¨
procedure developed by Junghofer
et al. [24]. This pro-
Fig. 2. Schematic representation of 128 channels montage. Extended
10–20 sites used for statistical analysis are given. Note: 10–20 sites were
approximated to the closest electrode position on the net.
A given EEG-epoch can be modeled by the sum of the
evoked response plus the trial-by-trial fluctuation around
the mean [42]. Since the present analysis focused on non
phase-locked oscillatory activity, the evoked response (i.e.,
¨
the ERP) was subtracted from each trial (see also Muller
et
al. [37], for a similar procedure). Spectral changes in
gamma band oscillatory activity during the experiment
were analysed by means of a wavelet analysis of artifactfree single epochs. In particular, complex Morlet wavelets
were used for frequency analysis to overcome certain
problems with constant FFT window length. This method
provides a good compromise between time and frequency
resolution [55], in particular time resolution of this procedure increases with frequency, whereas frequency resolution decreases. Thus, this technique is especially suited
for detecting induced high-frequency oscillations that may
occur during brief periods of time. The present procedure
has been proposed by Bertrand and Pantev [3] and is
described in detail elsewhere, e.g., by Tallon-Baudry et al.
[60,61]. In brief, the method provides a time-varying
magnitude of the signal in each frequency band, leading to
a time by frequency (TF) representation of the signal. TF
energy is averaged across single trials, allowing one to
analyze non-phase-locked high-frequency components. To
that end, complex Morlet wavelets g can be generated in
the time domain for different analysis frequencies f0
according to
g(t, f0 ) 5 A9 e
t2
2]
2 s 2t
e 2i p f0 t
(1)
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
with A9 depending on the parameter sf , specifying the
width of the wavelet in the frequency domain, the analysis
frequency f0 and the user-selected ratio m:
] ]]
m
A9 5 sfŒ2p 3 ]]
f0Œ]
p
œ
(2)
with
f0
m5]
sf
(3)
Thus, given a constant ratio m, the width of the wavelets
in the frequency domain, sf , changes as a function of the
analysis frequency f0 . In order to achieve good time and
frequency resolution the wavelet family used is defined by
a constant m 5 f0 /sf 5 7 with f0 ranging from 4.88 to 96.04
Hz in 0.49-Hz steps. Wavelets of this family were normalized in order to have equal amounts of energy. For each
epoch, time-varying energy in a given frequency band was
calculated, this being the absolute value of the convolution
of the signal with the wavelet for each epoch and each
complex spectrum. An epoch from 400 to 100 ms prior to
stimulus onset was used as an estimate of general noise.
The mean of this baseline epoch was subtracted from TF
matrices for each frequency and time point for each
electrode, respectively. After wavelet analysis, mean spectral power averaged across posterior electrode sites (corresponding 10–20 positions: Cp1, Cp2, P3, Po3, Pz, Po4, P4,
P7, Po7, O1, Poz, O2, Po8, P8; see Fig. 2) was represented
in TF-plots for the gamma range and a lower frequency
range. Electrode sites used for TF plots were selected on
the basis of previous findings regarding visual information
processing [58]. TF plots averaged across initial and
repeated picture presentations, respectively, were used in
order to identify latency and frequency range of induced
gamma power peaks. Furthermore, TF plots for every
picture presentation number (A–F, see Fig. 1) were
presented. For further statistical analysis a time window
and frequency band centered around the gamma peak and
29 electrode sites corresponding to the extended international 10–20 system was used. Fig. 2 depicts the utilized
10–20 sites, which were approximated to the closest
electrode position on the net. We calculated a repeated
measurement ANOVA with the factors Condition (initial
presentations versus repeated presentations)3Presentation
Number (3)3Recording Site (29 electrodes). A schematic
graph of the ANOVA model is given in Fig. 1-II. The
Presentation Number factor was introduced to test the
reliability between the three first picture presentations and
to examine differences between second and third presentations. To analyze the time course of spectral gamma
power an average across the same posterior electrodes as
used for TF plots described above was computed. A
repeated measurement ANOVA for this regional mean with
the factors Condition (first presentations versus repeated
presentations)3Presentation Number (3)3Time Window
381
(before peak, peak, and after peak) was calculated. The
time windows had the same length as the gamma peak
window and did not overlap with the peak window. Posthoc tests were calculated by means of paired t-tests.
Furthermore, in every time window initial and repeated
picture presentations were tested against baseline by means
of one-group t-tests. Furthermore, we tested the baseline
(mean power 400–100 ms prior to stimulus onset) with a
Condition
(initial
presentations
versus
repeated
presentations)3Presentation Number (3)3Recording Site
(29 electrodes) ANOVA to ensure that the expected
reduction in gamma power is not due to differences in
baseline values.
To control for effects in other frequency bands the same
ANOVA model was applied to the alpha band (8–12 Hz)
and a gamma range above and below the GBR showing
maximal spectral power.
2.6. Data analysis: ERP and evoked gamma band
response
In order to investigate the ERP, a 20-Hz low-pass filter
was applied to the data before all event-related potential
(ERP) analysis. Baseline correction was performed by
subtracting the mean of the signal during the time window
from 400 to 100 ms prior to stimulus onset. Based on
previous findings regarding repetition priming [47] four
ERP components were defined on the basis of the grand
mean evoked potential (see Fig. 5): Two early components
P1 (90–120 ms) and N1 (160–190 ms) and two late
components L1 (230–400 ms) and L2 (430–600 ms).
Amplitudes were averaged across the defined time windows. Mean amplitudes at electrode sites corresponding to
the extended international 10–20 system were analyzed
using a Condition (initial presentations versus repeated
presentations)3Presentation Number (3)3Recording Site
(29 electrodes) repeated measurement ANOVA for each of
the described components. Post-hoc comparisons were
calculated for the three electrode sites showing maximal
difference between initial and repeated stimulus presentations by means of paired t-tests.
To verify that our findings were due to induced and not
evoked activity, the same analysis used for induced gamma
responses was applied to the spectra of the averaged and
unfiltered evoked response. Because visual inspection of
TF-plots for the evoked gamma response revealed no clear
peak in the frequency domain, the same frequency band as
identified for the induced response was used for statistical
analysis.
2.7. Topographical distribution of induced spectral
changes and the ERP
To depict the topographical distributions of induced
spectral changes and evoked potentials, difference topographies (initial minus repeated presentations) were gener-
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
382
ated on the basis of all 128 electrode sites. These topographies were calculated for the gamma peak, the alpha
band (8–12 Hz) in the same time window as the gamma
peak, and the L1 ERP component. To allow a direct
comparison between topographies of spectral power
(gamma and alpha) and amplitude values (L1), data for the
L1 component were squared before generating the difference topography.
Where appropriate, P values were adjusted by Huynh
and Feldt correction in all ANOVA models. Means and
standard errors are presented.
3. Results
2.8. Data analysis: phase synchrony
3.1. Behavioral data
Phase synchrony analysis was performed, elaborating on
a procedure suggested by Rodriguez et al. [45], which
provides a method of measuring synchronous oscillatory
activity independent of the signal’s amplitude. For each
subject, phase synchrony was computed for a distinct
frequency f0 of his / her maximal gamma activity. Phase
was measured by convoluting the signal with a complex
Morlet wavelet designed for f0 (see above). A complex
phase value r was than computed at frequency f0 , for each
electrode, each time bin, and each trial by dividing the
result of the convolution by the magnitude of this result.
According to Rodriguez et al. subsequently a phase-locking value was computed as:
Between 73 and 81 targets were presented to the
subjects. On average, participants missed only up to three
of the pictures depicting an insect (mean, 1; S.D. 1.15),
with an average detection rate for targets of approximately
99%.
O
u ri 2 rj u
rij 5 ]]]
N
(4)
where N is the number of trials, i and j are the index for
the pair of electrodes to be compared. For the sake of data
reduction, phase locking values were computed only for a
subset of the 128-channel set, corresponding to 29 electrode sites of the extended 10–20 system (see Fig. 2). rij
results in a real value between one (constant phase
differences) and zero (random phase differences). These
values were normalized by subtracting the mean value of
the baseline period (black screen; 400–100 ms before
stimulus onset) and dividing by the standard deviation of
this time window. To provide a topographical representation of phase locking values over individual pairs of
electrodes in a distinct time window a statistical randomization technique was used. Time windows were chosen
which were the same as those in the analysis of induced
GBRs. Averaged phase synchrony for these time windows
(Wij ) between electrodes i and j were calculated. For each
of theses averages 200 values were analogously computed
on shuffled data. Shuffling was done by calculating
synchrony over time windows not phase-locked to stimulus
onset. The average Wij was retained as statistically significant if it was greater than the maximum (synchrony) or
less than the minimum (desynchrony) of the 200 shuffled
values, therefore indicating a two-tailed probability value
of P50.01. On a topographical template of the electrode
layout any significant value Wij was indicated by a line
from electrode i to electrode j. For this analysis, data of all
subjects were pooled in the randomization test. To ensure
that our findings were not due to effects of a minor number
of subjects the same analysis were calculated for single
subjects. The number of single subjects showing a significant phase locking value between pairs of electrodes is
given.
3.2. Induced spectral changes
Fig. 3-I depicts the time by frequency (TF) plots for an
average across posterior electrode sites for initial and
repeated picture presentations. The gamma range and a
lower frequency range are given in separate TF-plots.
Baseline corrected spectral power for initial picture presentations showed a maximum in a time window from 260
to 380 ms after stimulus onset in a frequency range
between 53 and 71 Hz. Alpha power centered around 10
Hz showed a suppression starting about 150 ms after
stimulus onset, which was larger for repeated as compared
to initial presentations. Furthermore, in Fig. 3-II the six
picture presentation numbers (A–F, see Fig. 1) averaged
across posterior electrode sites are represented separately.
As revealed by a main effect of Condition (F(1,9)513.13,
P,0.01) this maximum was significantly higher in the
initial as compared to repeated stimulus presentations. No
interaction revealed a significant effect. This suggests no
significant differences among the three initial stimulus
presentations (A, B, and D in Figs. 1 and 3-II) and no
significant differences between further presentations (C, E,
and F in Figs. 1 and 3-II). The analysis of the time course
of spectral gamma power based on power averaged across
posterior electrode sites revealed a significant interaction
Condition3Time Window (F(2,18)54.99; P50.01). Post
hoc tests showed a significant difference between initial
and further picture presentations for the time window
260–380 ms (t(9)53.34; P,0.01), but also for the time
window from 420 to 540 ms (t(9)53.13; P50.01). The
time course of induced gamma power averaged across
posterior electrode sites for these three time windows is
depicted in Fig. 4.
As can be seen in Fig. 4, the repetition of pictures led to
an augmentation in gamma power as well, but this increase
was smaller compared with the first presentations. Post hoc
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
383
Fig. 3. (I) Time by frequency plots averaged across posterior electrode sites for initial (I-A) and repeated picture presentations (I-B) for the gamma and
lower frequency ranges. (II) Grand mean baseline corrected time by frequency plots for the six presentation numbers (A–F) averaged across posterior
electrode sites. Note: time and frequency windows used for further analysis in the gamma range are indicated by rectangles.
384
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
Fig. 4. Time course 53–71-Hz band (1standard error) averaged across posterior electrode sites.
tests showed that this increase is different from baseline in
the peak time window (260–380 ms) for initial and
repeated picture presentations (initial: t(9)54.37; P,0.01;
repeated: t(9)52.57; P,0.05). In the time window from
420 to 540 ms this difference from baseline remains
significant for initial picture presentations (t(9)54.13; P,
0.01) only.
No significant effect was found for non-baseline corrected values in an epoch from 400 to 100 ms prior to
stimulus onset (F(1,9),1) suggesting that the reported
effects are not due to differences in baseline values.
Furthermore, no significant effects were found for the
gamma ranges between 29–46 and 78–95 Hz. The induced
alpha band showed a reduction in all conditions as
compared to the black screen (see Fig. 3-I). For any
repeated picture presentations the alpha reduction in the
time window of the gamma peak (260–380 ms) was larger
as compared to initial stimulus presentations (F(1,9)5
19.36; P50.001).
was found to be reversed at anterior sites. Post hoc tests for
three electrode sites showing a maximal difference between initial and repeated presentations (see Fig. 6)
revealed an effect at electrode P3 (t(9)52.32; P,0.05),
electrode F3 (t(9)5 22.50, P,0.05), and a trend at
electrode P4 (t(9)52.14; P50.06). The late component L2
(430–600 ms) revealed a main effect of Condition
(F(1,9)56.20; P,0.05) reflecting a relatively broad distribution of the repetition effect, along with a significant
Condition3Electrode interaction (F(28,252)54.93; P,
0.0001) with a maximum at electrode sites Cp1 (t(9)5
7.15; P,0.0001), Cp2 (t(9)55.19; P,0.001) and P3
(t(9)55.76; P,0.001).
With respect to the evoked gamma band response we
found no obvious peak in the TF representations of the six
experimental conditions. Thus, we used the same frequency bands as for induced GBRs for statistical analysis.
This analysis revealed no significant effects.
3.3. ERP and evoked gamma band response
3.4. Topographical distribution of induced spectral
changes and the ERP
None of the analysed ERP components showed a
significant difference between the three initial stimulus
presentations, nor among further stimulus presentations.
Therefore, in Fig. 5 the ERPs at 10–20 electrode sites are
averaged across presentation numbers A, B, and D (initial
presentations) and presentation numbers C, E, and F
(repeated presentations).
We found no significant differences for the P1 and N1
visual evoked potential between the experimental conditions. The late component L1 (230–400 ms) revealed a
significant Condition3Electrode interaction (F(28,252)5
2.66; P,0.0001), reflecting a reduction in positivity for
repeated pictures at posterior electrode sites. This effect
Difference topographies between initial and repeated
presentations of gamma band power (260–380 ms),
squared L1 amplitude (260–380 ms), and alpha power
(260–380 ms) are depicted in Fig. 6.
Given a wavelet duration in the 53–71-Hz band of
approximately 40 ms the gamma peak covers a time
window from approximately 240–400 ms in the time
domain. Therefore, the time windows of the L1 component
and the gamma peak are comparable in the time domain.
For the alpha band the wavelet length was 220 ms, which
results in a time window from approximately 150–500 ms
in the time domain. Positive values in alpha band difference are related to a stronger alpha suppression for
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
385
Fig. 5. Grand mean baseline corrected ERP for initial (solid line) and repeated (dashed line) picture presentations at 10–20 electrode sites.
repeated picture presentations. To allow for a closer
topographical comparison, difference bar graphs based on
the grand mean are also presented for 10–20 electrode
sites. As can be seen from Fig. 6 induced GBR showed a
broad posterior difference distribution, whereas differences
in L1 amplitude showed two distinct peaks around electrode sites P3 and P4. Alpha power difference revealed a
maximum at electrode site Po4.
3.5. Phase synchrony
Fig. 7 depicts phase synchrony and desynchrony between extended 10–20 sites for first and repeated stimulus
presentations in the same three time windows used for the
analysis of induced GBRs. For each subject, phase
synchrony was computed for a distinct frequency f0 of
his / her maximal gamma activity (mean, 60.80 Hz; S.D.
386
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
Fig. 6. Left: differences between initial and repeated picture presentations at 10–20 electrode sites for induced gamma power (260–380 ms), the squared
L1 visual evoked component (230–400 ms), and induced alpha power (260–380 ms). Right: spherical spline topographical difference distributions between
initial and repeated picture presentations for the 53–71-Hz band (260–380 ms), the squared L1 visual evoked component (230–400 ms), and the induced
alpha band (260–380 ms). Averages across 10 subjects are presented.
12.66 Hz). Synchrony between pairs of electrodes is
indicated by solid lines, desynchrony by dashed lines, each
drawn only if the synchrony value is beyond the distribution of shuffled data (P,0.01). For the initial picture
presentations the most dense pattern of significant values
of synchrony was found in the time window 100–220 ms
after stimulus onset predominantly among distant posterior
and temporal electrode sites, indicating synchronous neural
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
387
Fig. 7. Synchrony (solid lines) and desynchrony (dashed lines) between 10 and 20 electrode pairs for initial and repeated picture presentations. Lines are
drawn only if the phase-locking value is beyond the distribution of shuffled data (P,0.01).
activity in a broadly distributed posterior network. Less
phase synchrony was observed for repeated pictures in all
time windows.
In Table 1 the number of single subjects showing a
significant phase locking value between pairs of electrodes
is given. Pairs of electrodes presented in Table 1 were
chosen on the basis of significant values in the grand mean
(see Fig. 7).
As one can see from Table 1 the differences between
initial and repeated picture presentations are not due to
effects in only one or two subjects. Significant values of
phase synchrony in our pooled results, could be found in
approximately 80% of the single subjects.
4. Discussion
The present study was motivated by the hypothesis that
repeated experience with an object is linked to ‘repetition
suppression’ of neuronal activity within a cell assembly
representing a stimulus. Such a cell assembly is established
by synchronized activity among the elements of the
network in the gamma band range. After the first pre-
sentation of an object, induced gamma power showed a
broadly distributed increase above baseline level. Furthermore, synchronized activity was found between distant
posterior cortical areas. After the second or third repetition
of the same object we found a decrease in induced gamma
power and phase synchrony between distant electrode
sites.
In the visual system it was shown that the cortical
representation for a perceived object is widespread across
multiple visual areas [18]. In 1949, Hebb [19] proposed
that specific functions of different brain areas are integrated by dynamical binding of cell assemblies. Neuronal
synchronization might integrate the activity within and
between the elements of such a network [33]. Induced
gamma band responses (GBRs) were discussed as a
signature of activity within such a cell assembly in the
human EEG [14,35,43]. A series of studies have reliably
shown that induced GBRs in the human EEG are closely
linked to cortical object representations and visual information processing (for a recent review see Ref. [25]). If
no unequivocal object representation can be established
neither induced GBRs nor phase-synchrony show an
increase as strong as in conditions in which object repre-
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
388
Table 1
Number of single subjects showing a significant phase locking value
between pairs of electrodes. Pairs of electrodes are chosen on the basis of
significant values in the grand mean (see Fig. 7)
100–220 ms
Initial
Fz–Cz
C3–P8
C3–Po4
C3–Poz
C3–O2
T7–C3
T7–Cz
T7–Cp2
T7–P8
Cp5–Cp2
Cp5–Po4
Cp5–O2
Cp5–Po3
Cp5–Poz
Cp1–Poz
Cp1–O1
Cp1–Poz
Cp1–Po7
Cp1–P7
P7–Cp2
P7–Po4
Po7–Cp2
P9–Cp2
P9–P4
O1–Cp2
O2–Cz
P4–Cz
Po8–Cz
P8–C4
P8–Cz
P8–C3
P8–T7
Repeated
T7–P3
T7–Po7
Cz–Cp6
Fz–F8
260–380 ms
6
8
7
7
7
10
8
8
8
6
7
6
5
7
8
8
8
9
8
7
9
6
7
7
6
8
7
7
7
7
8
8
Initial
F4–O1
F8–Iz
T8–Pz
T8–Po7
T8–O1
T8–Iz
Cp6–P4
Cp6–Po4
Cp6–O1
O1–P8
C4 –P8
Fz–T7
Fz–O2
Fz–Po8
Repeated
Cp5–Poz
Po3–P4
Pz–P4
F7 –C4
Fz–Po8
Fz–P10
Po4 –P10
F4 –Po8
420–540 ms
6
6
7
7
8
4
5
7
8
7
6
5
5
8
Initial
Fz–Cz
F8–Cz
P8–Pz
F8–P3
F8–P7
F8–O1
F8–Poz
Cp5 –Poz
Repeated
P4–P8
P8–O1
O1–Iz
4
5
6
6
7
5
5
7
9
7
8
5
8
10
4
7
8
9
9
5
5
7
7
Pairs given in italics refer to desynchrony.
sentations can easily be established [15,45]. We assume
that the broad increase in gamma activity and synchrony
that was observed after the first picture presentation is
linked to a cortical object representation of the stimulus.
This interpretation is supported by theoretical assumptions
on object identification. For instance Hummel and Biederman [22] proposed that their so-called ‘geons’ are established by synchronization of oscillatory outputs of units
representing features of one ‘geon’. Grossberg [13] proposed in his ‘adaptive resonance’ theory that spatially
distributed features are linked to an object by means of
synchronous oscillations. Recently, Desimone [7] suggested that after repeated presentations of a stimulus,
‘repetition suppression’ is a possible neuronal mechanism
for this form of perceptual priming. The author stated that
a neuronal network representing features of an object
becomes sparser and more selective with repeated ex-
perience of the stimulus. Neurons showing a decreased
response when stimuli recur are possibly dropping out of
the population of activated cells, which code the stimulus
and, therefore, are yielding a more efficient cell assembly
coding an object. Due to the fact that the EEG measures
neuronal mass activity, a reduction in the number of
neurons representing a stimulus should result in a decrease
in induced gamma power. Thus, we assume that the
decrease in gamma power and phase synchrony that we
have observed after the second and third consecutive
representation of an object may be a signature of this
‘neural saving’ process. Importantly, gamma power after
further stimulus presentations is still above baseline level.
This is in line with our hypothesis: even though the cell
assembly representing an object may be sparser, the
stimulus still has to be represented by synchronized
activity.
As induced GBRs are known to be modulated by
attention [16,36], one might argue that our findings are due
to higher levels of attention paid to initial picture presentations as compared to picture repetitions. In order to
control for that effect, we introduced our target detection
task in which the occurrence of a target was unpredictable
and, thus, subjects had to pay the same amount of attention
to every picture presentation. On the basis of an average
target detection rate of 99% we concluded that it is
unlikely that subjects paid less attention to repeated
presentations. Obviously, the same argument is true for
possible effects of habituation, i.e., the disappearance of
responsiveness to accustomed stimulation. Nevertheless,
concerns about possible effects of attention and habituation
have to be addressed in a follow-up study, in which stimuli
should be repeated after intervening different stimuli.
Regarding the topographical distribution of induced
GBRs and the pattern of phase synchrony in the present
study the integration of activity across multiple visual
areas may have contributed to our findings. Although, the
idea of a synchronized cortical network involving widespread cortical areas fits well with our current hypothesis,
it has to be mentioned that this interpretation is speculative
at the moment, because scalp recordings do not allow us to
draw direct conclusions on underlying cortical generators.
On the other hand our findings are in line with decreased
activity in areas related to the temporal and dorsal stream
after repetition of stimuli as reported in human imaging
studies. Although, the cortical basis of blood flow in the
brain is still under discussion, recently, Logothetis et al.
[31] showed that the blood oxygen level-dependent
(BOLD) signal directly reflects changes in neural electrical
activity. Using fMRI, James et al. [23] showed a decrease
of BOLD response in the occipito-temporal region and the
intraparietal region for primed as compared to unprimed
objects. In a study using PET, Fischer et al. [10] reported a
decrease in regional cerebral blood flow (rCBF) in both the
secondary visual cortex and the right medial temporal
cortex during the repetition of complex visual stimuli. In
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
the field of animal research, Brown et al. [4] and Baylis
and Rolls [2] have shown that some neurons in the
monkeys’ ventral temporal lobe had a reduced firing rate
after repetition of a stimulus as compared to presentations
of novel stimuli.
We found no difference between initial and repeated
pictures for the P1 and N1 ERP component. In line with
other EEG studies examining the effect of primed and
unprimed picture presentations [47,71], the first modulation of the ERP was observed in a time window from 230
to 400 ms, with a maximum of difference in amplitude at
parietal electrode sites P3 and P4. Rugg et al. [47] reported
the first modulation in a time window from 200 to 400 ms
with a maximum at parietal electrode sites. As can be seen
in Fig. 6 the focused activity in the topographical distribution of the L1 ERP component differed clearly from
the distribution of induced GBRs. Gamma power showed a
broad posterior distribution, whereas the L1 component
exhibited two distinct peaks focused around parietal electrode sites. Although we cannot draw any conclusion on
neuronal sources, the obvious difference in the topographical distributions suggests different underlying cortical
sources of the two brain responses. Thus, visual evoked
responses may play a functionally different role in perception as compared to induced GBRs. Whereas induced
GBRs might be a signature of a cortical object representation over widespread cortical areas, the visual evoked
response in the latency between 230 and 400 ms may
reflect differences in the activity of more distinct repetition-sensitive brain structures [47]. The question if the
reduction of amplitude in the evoked response is due to a
‘sharpening’ process on a different level of processing
must be examined in future studies.
To verify that our results are due to induced and not
evoked activity in the gamma range, we transformed the
unfiltered averaged evoked response into the frequency
domain. Here, we found no significant differences between
initial and repeated picture presentations for the evoked
gamma band response in the same time and frequency
window as the induced gamma peak. Although, other
studies reported early evoked gamma activity [57,59],
evoked high-frequency activity was not modulated by
experimental manipulations. However, Herrmann et al.
[21] mentioned that early evoked gamma band activity
might be associated with visual classification mechanisms.
The authors stated that gamma activity might be related to
the comparison of the stimulus to a ‘working memory
template’. They mentioned that these matching processes
are related to early evoked gamma activity in a latency
range between 100 and 200 ms [20]. We have not found a
modulation of early evoked gamma responses in the
present study or in other studies [14,15], thus, our results
rather parallel the findings of Tallon-Baudry et al.
With regard to phase synchrony Varela et al. [66] stated
that phase locking independent of amplitude might be an
indicator of integration within a neural assembly which is
389
widespread in the human brain, but also between regions
whose separation falls in the intermediate spatial scale,
such as parietal and early visual areas. We found most of
the significantly synchronized responses between pairs of
electrodes over the ventral and the dorsal stream for initial
picture presentations. Surprisingly, the most dense pattern
of synchrony was observed in a time window before the
power peak. In the time window of maximal gamma power
we still found more significant synchronization for initial
as compared to repeated picture presentations. In a recent
study, using a rapid perceptual learning paradigm, the
pattern of phase synchrony between electrode sites and the
time window of maximal synchronization corresponded
well with the time course of power in the gamma band,
i.e., an increase in spectral power is closely linked to an
increase in phase synchrony in the same time window [15].
Rodriguez et al. [45] showed that desynchronization coexisted with periods of above-average gamma activity.
Therefore, changes in gamma band spectral power should
not be confounded with phase synchrony. Further research
will be needed to investigate the link between measurements of phase synchrony between electrode sites and
gamma power more closely.
We can rule out that our phase results are due to effects
in only one or two subjects showing the effect. Significant
values of phase synchrony in our pooled results, could be
found in approximately 80% of the cases in single subjects
as well. We cannot totally rule out the contribution of a
single neural source located at a distance from two testing
electrodes, which may create an artificial synchronization
of EEG signals by simple volume conduction. However,
Lachaux et al. [29] stated that volume conduction leads to
a diffusion in the measurement of synchrony. We did not
find such diffusion. Rather synchrony between distant
electrode sites, but not between electrode sites located
between the two was observed. Yet, it has to be mentioned
that this rule of thumb is not a reliable test to identify
conduction synchronies and cannot provide a complete
solution to the problem. Furthermore, another problem in
calculating synchrony is not only volume conduction but
also the choice of an appropriate reference electrode.
Lachaux et al. showed that the calculation of current
source density can create artificial synchrony. An alternative approach is to use the average reference [29].
Although, this method does not completely solve the
problem, we adopted this strategy for our experiment. For
the discussion of the influence of other possible sources of
artifacts like muscle activity we refer to Gruber et al. [16],
¨
¨
Keil et al. [26], Muller
et al. [36–38], and Pulvermuller
et
al. [44].
A further important point is that synchronized oscillatory brain activity was not only reported in the gamma
range, but also in lower frequency bands. For example in a
study by von Stein et al. [68] the presentation of line
drawings resulted in an increase of coherence between
temporal and parietal electrode sites in a frequency range
390
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
between 13 and 18 Hz. These results were interpreted as a
signature of binding of cell assemblies over distant cortical
areas. Furthermore, in a working memory paradigm
Sarntheim et al. [48] reported an enhancement in coherence in the theta band (4–7 Hz). In an animal study
measuring local field potentials von Stein et al. [67]
reported that significant interactions between distant cortical areas evolved mainly in a frequency range from 4 to 12
Hz, whereas interactions in the gamma range were observed predominantly locally, i.e., between sites within the
range of monosynaptic connections. Contrary to these
findings, but in line with other studies investigating
synchronized brain activity [29,35,45], we found significant interactions in the gamma range between electrode
sites over more distant cortical areas. However, we should
mention that we have not analyzed phase synchrony in
lower frequency ranges. A possible solution to the question
of synchronization in different frequency bands is given by
von Stein et al. [67]. They mention that high-frequency
interactions in the 20–100-Hz band reflect the processing
of novel unexpected stimuli, which would be comparable
to the initial picture presentations in our paradigm.
Interestingly, the induced alpha suppression that we
found after initial and repeated picture presentations shows
a similar pattern as a process commonly referred to as
event-related desynchronization [41]. Remarkably, the
alpha suppression we found was larger for repeated as
compared to initial picture presentations. In a different
experimental design Klimesch reported that inter-individual differences in long-term memory performance were
correlated with suppression of event-related alpha power
[28]. If one assumes that repeated picture presentation
leads to better memory performance as compared to a
single presentation of a picture, our results would be in line
with these findings. However, due to the fact that our study
investigated high-frequency synchronization, the interpretation of different levels of lower frequency desynchronization goes beyond the aims of our study and must be
subject to follow-up experiments.
In summary, the present experiment has several implications for the understanding of neural mechanism underlying repetition priming. Induced gamma band responses and
phase synchrony may be a signature of a cortical object
representation processed in a Hebbian cell assembly. Such
a cell assembly has to integrate neural activity from
different visual cortical areas processing different aspects
of a stimulus. The further presentation of a primed object
may lead to repetition suppression within this network, i.e.,
a sharpening in the cortical representation of a stimulus.
The ‘sharpening’ metaphor seems to be a useful working
hypothesis for future studies regarding oscillatory brain
activity related to priming processes.
Further studies have to answer the question if this
‘sharpening’ is due to the fact that parts of an associative
network drop out completely, or if the extent of the
networks stays the same and only a number of neurons
drop out. Research in the field of oscillatory dynamics in
the human brain is still at its very beginning. Therefore, it
might well be that concentrating on one frequency band
along with a ‘binary decision’ regarding synchrony (synchronous versus non-synchronous) may be too simplistic.
To further investigate the dynamics of a presumably highly
complex oscillating system, in which other frequency
bands like theta, alpha, and delta oscillations might
contribute to a ‘resonant communication network’ through
large populations of neurons [1] provides the challenge for
the future.
The fact that we found no significant effects with respect
to the evoked gamma band response and a different
topographical distribution for the ERP indicates that
evoked responses may play a functionally different role in
perception as compared to induced gamma band responses.
Acknowledgements
We are grateful to Heidi Messmer for help in data
acquisition and Nicola Williams for editorial support.
Research was supported by grants from the Deutsche
Forschungsgemeinschaft.
References
¨
[1] E. Başar, C. Başar-Eroglu, S. Karakaş, M. Schurmann,
Gamma,
alpha, delta, and theta oscillations govern cognitive processes, Int. J.
Psychophysiol. 39 (2001) 241–248.
[2] G.C. Baylis, E.T. Rolls, Responses of neurons in the inferior
temporal cortex in short term and serial recognition memory tasks,
Exp. Brain Res. 65 (1987) 614–622.
[3] O. Bertrand, C. Pantev, Stimulus frequency dependence of the
transient oscillatory auditory evoked response (40 Hz) studied by
electric and magnetic recordings in human, in: C. Pantev, T. Elbert,
¨
¨
B. Lutkenhoner
(Eds.), Oscillatory Event-Related Brain Dynamics,
Plenum, New York, 1994.
[4] M.W. Brown, F.A. Wilson, I.P. Riches, Neuronal evidence that
inferomedial temporal cortex is more important than hippocampus in
certain processes underlying recognition memory, Brain Res. 409
(1987) 158–162.
[5] M.W. Brown, J.Z. Xiang, Recognition memory: neuronal substrates
of the judgment of prior occurrence, Prog. Neurobiol. 55 (1998)
149–189.
[6] R.L. Buckner, J. Goodman, M. Burock, M. Rotte, W. Koutstaal, D.
Schacter, B. Rosen, A.M. Dale, Functional-anatomic correlates of
object priming in humans revealed by rapid presentation eventrelated fMRI, Neuron 20 (1998) 285–296.
[7] R. Desimone, Neural mechanisms for visual memory and their role
in attention, Proc. Natl. Acad. Sci. USA 93 (1996) 13494–13499.
[8] R. Eckhorn, H.J. Reitboeck, M. Arndt, P. Dicke, Feature linking via
synchronization among distributed assemblies: simulations of results
from cat visual cortex, Neural Comput. 2 (1990) 293–307.
[9] D.J. Felleman, D.C. van Essen, Distributed hierarchical processing
in the primate cerebral cortex, Cereb. Cortex 1 (1991) 1–47.
[10] H. Fischer, T. Furmark, G. Wik, M. Fredrikson, Brain representation
of habituation to repeated complex visual stimulation studied with
PET, NeuroReport 11 (2000) 123–126.
¨
[11] C.M. Gray, A.K. Engel, P. Konig,
W. Singer, Stimulus-dependent
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
neuronal oscillations in cat visual cortex: receptive field properties
and feature dependence, Eur. J. Neurosci. 2 (1990) 607–619.
¨
C.M. Gray, P. Konig,
A.K. Engel, W. Singer, Oscillatory responses
in cat visual cortex exhibit inter-columnar synchronization which
reflects global stimulus properties, Nature 338 (1989) 334–337.
S. Grossberg, The attentive brain, Am. Sci. 83 (1995) 438–449.
¨
T. Gruber, A. Keil, M.M. Muller,
Modulation of induced gamma
band responses and phase synchrony in an associative learning task
in the human EEG, Neurosci. Lett. 316 (2001) 29–32.
¨
T. Gruber, A. Keil, M.M. Muller,
Modulation of induced gamma
band responses in a perceptual learning task in human EEG, J.
Cogn. Neurosci.
¨
T. Gruber, M.M. Muller,
A. Keil, T. Elbert, Selective visual-spatial
attention alters induced gamma band responses in the human EEG,
Clin. Neurophysiol. 110 (1999) 2074–2085.
S.B. Hamann, L.R. Squire, Intact perceptual memory in the absence
of conscious memory, Behav. Neurosci. 111 (1997) 850–854.
J.V. Haxby, M.I. Gobbini, M.L. Furey, A. Ishai, J.L. Schouten, P.
Pietrini, Distributed and overlapping representations of faces and
objects in ventral temporal cortex, Science 293 (2001) 2425–2430.
D.O. Hebb, in: The Organization of Behavior, Wiley, Chichester,
1949.
C.S. Herrmann, A. Mecklinger, Magnetoencephalographic responses
to illusory figures: early evoked gamma is affected by processing of
stimulus features, Int. J. Psychophysiol. 38 (2000) 265–281.
C.S. Herrmann, A. Mecklinger, E. Pfeifer, Gamma responses and
ERPs in a visual classification task, Clin. Neurophysiol. 110 (1999)
636–642.
J.E. Hummel, I. Biederman, Dynamic binding in a neural network
for shape recognition, Psychol. Rev. 99 (1992) 480–517.
T.W. James, G.K. Humphrey, J.S. Gati, R.S. Menon, M.A. Goodale,
Repetition priming and the time course of object recognition: an
fMRI study, NeuroReport 10 (1999) 1019–1023.
¨
M. Junghofer,
T. Elbert, D.M. Tucker, B. Rockstroh, Statistical
control of artifacts in dense array EEG / MEG studies, Psychophysiology 37 (2000) 523–532.
¨
A. Keil, T. Gruber, M.M. Muller,
Functional correlates of macroscopic high-frequency brain activity in the human visual system,
Neurosci. Biobehav. Rev. 25 (2001) 527–534.
¨
A. Keil, M.M. Muller,
W.J. Ray, T. Gruber, T. Elbert, Human
gamma band activity and perception of a gestalt, J. Neurosci. 19
(1999) 7152–7161.
J.F. Kihlstrom, D.L. Schacter, Anesthesia, amnesia, and the cognitive unconscious, in: B. Bonke, W. Fitch, K. Millar (Eds.), Memory
and Awareness in Anesthesia, Swets and Zeitlinger, Amsterdam,
1990.
W. Klimesch, Memory processes, brain oscillations and EEG
synchronization, Int. J. Psychophysiol. 24 (1996) 61–100.
J.P. Lachaux, E. Rodriguez, J. Martinerie, F.J. Varela, Measuring
phase synchrony in brain signals, Hum. Brain Mapp. 8 (1999)
194–208.
M. Livingstone, D. Hubel, Segregation of form, color, movement,
and depth: anatomy, physiology, and perception, Science 240 (1988)
740–749.
N.K. Logothetis, J. Pauls, M. Augath, T. Trinath, A. Oeltermann,
Neurophysiological investigation of the basis of the fMRI signal,
Nature 412 (2001) 150–157.
¨
W. Lutzenberger, F. Pulvermuller,
T. Elbert, N. Birbaumer, Visual
stimulation alters local 40-Hz responses in humans: an EEG-study,
Neurosci. Lett. 183 (1995) 39–42.
C.v.d. Malsburg, W. Schneider, A neural cocktail-party processor,
Biol. Cybern. 54 (1986) 29–40.
P.M. Milner, A model for visual shape recognition, Psychol. Rev. 81
(1974) 521–535.
W.H.R. Miltner, C. Braun, M. Arnold, H. Witte, E. Taub, Coherence
of gamma-band activity as a basis for associative learning, Nature
397 (1999) 434–436.
391
¨
[36] M.M. Muller,
T. Gruber, A. Keil, Modulation of induced gamma
band activity in the human EEG by attention and visual information
processing, Int. J. Psychophysiol. 38 (2000) 283–299.
¨
[37] M.M. Muller,
J. Bosch, T. Elbert, A. Kreiter, M. Valdes Sosa, P.
Valdes Sosa, B. Rockstroh, Visually induced gamma-band responses
in human electroencephalographic activity—a link to animal studies,
Exp. Brain Res. 112 (1996) 96–102.
¨
¨
[38] M.M. Muller,
M. Junghofer,
T. Elbert, B. Rockstroh, Visually
induced gamma-band responses to coherent and incoherent motion:
a replication study, NeuroReport 8 (1997) 2575–2579.
[39] R. Neshige, H. Luders, H. Shibasaki, Recording of movementrelated potentials from scalp and cortex in man, Brain 111 (1988)
719–736.
[40] K.N. Ochsner, C.Y. Chiu, D.L. Schacter, Varieties of priming, Curr.
Opin. Neurobiol. 4 (1994) 189–194.
[41] G. Pfurtscheller, F.H. Lopes da Silva, Event-related EEG / MEG
synchronization and desynchronization: basic principles, Clin.
Neurophysiol. 110 (1999) 1842–1857.
[42] M.B. Priestley, in: Non-linear and Non-stationary Time Series
Analysis, Academic Press, London, 1988.
¨
[43] F. Pulvermuller,
Hebb’s concept of cell assemblies and the psychophysiology of word processing, Psychophysiology 33 (1996) 317–
333.
¨
[44] F. Pulvermuller,
N. Birbaumer, W. Lutzenberger, B. Mohr, Highfrequency brain activity: its possible role in attention, perception and
language processing, Prog. Neurobiol. 52 (1997) 427–444.
[45] E. Rodriguez, N. George, J.P. Lachaux, J. Martinerie, B. Renault,
F.J. Varela, Perception’s shadow: long-distance synchronization of
human brain activity, Nature 397 (1999) 430–433.
[46] P.E. Roland, B. Gulyas, R.J. Seitz, C. Bohm, S. Stone-Elander,
Functional anatomy of storage, recall, and recognition of a visual
pattern in man, NeuroReport 1 (1990) 53–56.
[47] M.D. Rugg, M. Soardi, M.C. Doyle, Modulation of event-related
potentials by the repetition of drawings of novel objects, Brain Res.
Cogn. Brain Res. 3 (1995) 17–24.
[48] J. Sarnthein, H. Petsche, P. Rappelsberger, G.L. Shaw, A. von Stein,
Synchronization between prefrontal and posterior association cortex
during human working memory, Proc. Natl. Acad. Sci. USA 95
(1998) 7092–7096.
[49] D.L. Schacter, Understanding implicit memory: a cognitive neuroscience approach, Am. Psychol. 47 (1992) 559–569.
[50] D.L. Schacter, R.D. Badgaiyan, Neuroimaging of priming: new
perspectives on implicit and explicit memory, Curr. Dir. Psychol.
Sci. 10 (2001) 1–4.
[51] D.L. Schacter, R.L. Buckner, Priming and the brain, Neuron 20
(1998) 185–195.
[52] D.L. Schacter, R.L. Buckner, W. Koutstaal, Memory, consciousness
and neuroimaging, Phil. Trans. R. Soc. London B Biol. Sci. 353
(1998) 1861–1878.
¨
¨
[53] W. Singer, C. Gray, A. Engel, P. Konig,
A. Artola, S. Brocher,
Formation of cortical cell assemblies, Cold Spring Harbor Symp.
Quant. Biol. LV (1990) 939–952.
[54] W. Singer, C.M. Gray, Visual feature integration and the temporal
correlation hypothesis, Annu. Rev. Neurosci. 18 (1995) 555–586.
¨¨ ¨
[55] J. Sinkkonen, H. Tiitinen, R. Naatanen,
Gabor filters: an informative
way for analysing event-related brain activity, J. Neurosci. Methods
56 (1995) 99–104.
[56] J.G. Snodgrass, M. Vanderwart, A standardized set of 260 pictures:
norms for name agreement, image agreement, familiarity and visual
complexity, J. Exp. Psychol. [Hum. Learn]. 6 (1980) 174–215.
[57] C. Tallon, O. Bertrand, P. Bouchet, J. Pernier, Gamma-range activity
evoked by coherent visual stimuli in humans, Eur. J. Neurosci. 7
(1995) 1285–1291.
[58] C. Tallon-Baudry, O. Bertrand, Oscillatory gamma activity in
humans and its role in object representation, Trends Cogn. Sci. 3
(1999) 151–162.
[59] C. Tallon-Baudry, O. Bertrand, C. Delpuech, J. Pernier, Oscillatory
392
[60]
[61]
[62]
[63]
[64]
[65]
¨
/ Cognitive Brain Research 13 (2002) 377 – 392
T. Gruber, M.M. Muller
gamma-band (30–70 Hz) activity induced by a visual search task in
human, J. Neurosci. 17 (1997) 722–734.
C. Tallon-Baudry, O. Bertrand, F. Peronnet, J. Pernier, Induced
gamma-band activity during the delay of a visual short-term
memory task in humans, J. Neurosci. 18 (1998) 4244–4254.
C. Tallon-Baudry, O. Bertrand, C. Wienbruch, B. Ross, C. Pantev,
Combined EEG and MEG recordings of visual 40-Hz responses to
illusory triangles in human, NeuroReport 8 (1997) 1103–1107.
R.B. Tootell, A.M. Dale, M.I. Sereno, R. Malach, New images from
human visual cortex, Trends Neurosci. 19 (1996) 481–489.
E. Tulving, D.L. Schacter, Priming and human memory systems,
Science 247 (1990) 301–306.
L.G. Ungerleider, Functional brain imaging studies of cortical
mechanisms for memory, Science 270 (1995) 769–775.
L.G. Ungerleider, J.V. Haxby, ‘What’ and ‘where’ in the human
brain, Curr. Opin. Neurobiol. 4 (1994) 157–165.
[66] F. Varela, J.P. Lachaux, E. Rodriguez, J. Martinerie, The brainweb:
phase synchronization and large-scale integration, Nat. Rev. Neurosci. 2 (2001) 229–239.
¨
[67] A. von Stein, C. Chiang, P. Konig,
Top-down processing mediated
by interareal synchronization, Proc. Natl. Acad. Sci. USA 97 (2000)
14748–14753.
[68] A. von Stein, P. Rappelsberger, J. Sarnthein, H. Petsche, Synchronization between temporal and parietal cortex during multimodal
object processing in man, Cereb. Cortex 9 (1999) 137–150.
[69] C.L. Wiggs, A. Martin, Properties and mechanisms of perceptual
priming, Curr. Opin. Neurobiol. 8 (1998) 227–233.
[70] S. Zeki, in: A Vision of the Brain, Blackwell, Oxford, 1993.
[71] X.L. Zhang, H. Begleiter, B. Porjesz, W. Wang, A. Litke, Event
related potentials during object recognition tasks, Brain Res. Bull.
38 (1995) 531–538.