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NeuroImage 32 (2006) 978 – 987

EEG oscillations and recognition memory: Theta correlates ofmemory retrieval and decision making
Joshua Jacobs,a Grace Hwang,b Tim Curran,c and Michael J. Kahana b,*a

Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
Department of Psychology, University of Pennsylvania, 3401 Walnut Street, Room 303C, Philadelphia, PA 19104, USAc
Department of Psychology, University of Colorado at Boulder, Boulder, CO 80309, USAb

Received 29 December 2005; revised 7 February 2006; accepted 8 February 2006
Available online 12 July 2006

Studies of memory retrieval have identified electroencephalographic(EEG) correlates of a test item’s old – new status, reaction time, andmemory load. In the current study, we used a multivariate analysis todisentangle the effects of these correlated variables. During retrieval,power of left-parietal theta (4 – 8 Hz) oscillations increased inproportion to how well a test item was remembered, and theta incentral regions correlated with decision making. We also studied how
these oscillatory dynamics complemented event-related potentials.
These findings are the first to demonstrate that distinct patterns oftheta oscillations can simultaneously relate to different aspects of
behavior.
D 2006 Elsevier Inc. All rights reserved.
Keywords: Theta; EEG oscillations; Recognition memory; Decision
making; Working memory; Gamma

Introduction
Understanding the functional role of human electroencephalographic (EEG) oscillations is complicated by the fact that
oscillatory patterns have been observed to correlate with multiple
experimental manipulations. For example, in recent memory
studies, theta (4 – 8 Hz) oscillations correlated with memory load
(Jensen and Tesche, 2002), task difficulty (Gevins et al., 1997),
error processing (Luu et al., 2004), stimulus type (Hwang et al.,2005), and recognition of previously viewed stimuli (Klimesch etal., 2000; Klimesch et al., 2006). Oscillations at other frequencies,
in addition to theta, have also been observed to vary with task
variables: Alpha (9 – 12 Hz) power was observed to increase withmemory load in simple working-memory tasks (Jensen et al., 2002)
but to decrease as memory load increased during more demanding
tasks (Gevins et al., 1997). Gamma (30 – 60 Hz) power has been

* Corresponding author. Fax: +1 215 746 3848.
E-mail address: kahana@psych.upenn.edu (M.J. Kahana).
Available online on ScienceDirect (www.sciencedirect.com).
1053-8119/$ - see front matter D 2006 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2006.02.018

observed to correlate with memory load (Howard et al., 2003),
stimulus novelty (Gruber and Matthias, 2005), attention (TallonBaudry et al., 2005), and reaction time (Gonzalez Andino et al.,2005; Jokeit and Makeig, 1994). Similarly, studies of spatial
memory have reported increased theta power during learning oflong as compared to short mazes, movement as compared tostillness, retrieval as compared to encoding, and searching for
randomly placed targets as compared to searching for fixedlocation landmarks (Kahana et al., 1999; Caplan et al., 2001, 2003;
Ekstrom et al., 2005; de Araujo et al., 2002). One recent study(Bastiaansen et al., 2005) directly illustrates the complexity ofdetermining how EEG oscillations correlate with task variables:
during visual word presentation, left-posterior theta power simultaneously correlated with both word length and whether a word
served a primarily semantic (e.g., nouns and verbs) or a primarily
syntactic (e.g., prepositions and conjunctions) function. Consistent
with these reports, recent reviews (Buzsáki, 2002; Bastiaansen and
Hagoort, 2003; Kahana et al., 2001) describe a diverse array ofrelations between oscillations and behavior, with no single pattern
emerging clearly.
One difficulty in using memory tasks to study EEG oscillations
is that an experimental manipulation may cause a change in
response accuracy or reaction time (RT). In these cases, it can be
unclear if an observed EEG pattern is a direct correlate of themanipulation itself, or if it is more closely related to the accuracy –RT change. An example of this is the linear relation between study
list length and RT in working-memory tasks (Sternberg, 1966).
This list-length – RT relation makes it difficult to distinguishwhether an EEG pattern that correlates with list length is actuallya direct result of an increase in RT. In addition to list length, otherexperimental manipulations, such as the position of a memory
probe in a just-presented list (Forrin and Cunningham, 1973), also
influence RT, thus creating more potential confounds. This type ofissue has surfaced in recent EEG publications, where it has been
observed that theta power increased during experimental manipulations that raised task difficulty, such as increases in stimulus
complexity (Gevins et al., 1997) or memory load (Jensen andTesche, 2002). Although these EEG patterns could be interpreted

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

as direct correlates of stimulus changes, another interpretation isthat these patterns were related to a more general phenomenon:
increased RT.
To address these concerns, we considered the possibility that
EEG oscillations simultaneously relate to multiple aspects of
behavior. Thus, we designed a framework to analyze how power of
EEG oscillations simultaneously correlates with task variables(experimental manipulations and RT) in a working-memory task.
This approach has two main benefits: First, by simultaneously
analyzing how task variables relate to oscillatory power, it ispossible to distinguish which variable best predicts an oscillatory
pattern. Second, our framework is capable of identifying oscillatory patterns that correlate with multiple task variables.
Because working-memory studies have found EEG oscillations
to be correlated with a wide range of variables, and because thebehavioral data in such studies are well characterized (Sternberg,1975), we sought to simultaneously examine the relation of EEG
oscillatory power—across a broad range of frequencies, after-probe
latencies, and electrodes—with each of the major variables thatcharacterize working-memory performance. Subjects performed
the Sternberg working-memory task while EEG was recorded at
129 scalp electrodes. On each trial, subjects were asked to judge
whether a test probe was a member of a just-presented list of
consonants. We varied the number of list items, whether the probe
matched one of the studied items, and (if so) the position of theprobe in the list. We fit a multivariate linear model to predict EEG
oscillatory activity after probe using details of each presented list
and RT. An analysis of this model identified oscillatory correlates
of probe old – new status, RT, memory load, and study – test lag.

Methods
Participants
Eighteen right-handed University of Colorado students (6 males
and 12 females) were paid to participate in two experimental
sessions. All subjects were between the ages of 19 and 29. Two
subjects participated in only a single session because of technicaldifficulties.
Procedure
In each of 576 trials, which were distributed across two sessions
on different days, subjects viewed a list of 2, 4, or 6 consonants
followed by a test probe. All interstimulus intervals (ISIs) wererandomly jittered to decorrelate evoked activity across stimulus
presentations. Trials began with presentation of a fixation cross for
1 s, followed by a 100 – 300-ms ISI. Each consonant was thenpresented for 750 ms, followed by a 200 – 300-ms ISI. The final list
item was followed by a 200 – 300-ms ISI, and then subjects were
presented with a memory probe, shown in red (all other items weredisplayed in white against a black background). In response to theprobe, subjects were asked to press a key on a keyboard indicating
as quickly as possible if the probe was a target (i.e., an item
presented on the study list) or a lure (i.e., an item that was not on
the study list). The specific keys used for indicating target and lureresponses were counterbalanced across subjects. List length, probetarget – lure status, and (for target trials) probe position were
determined randomly for each trial. Both response accuracy and
RT were recorded.

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Scalp voltages were measured with a 129-channel Geodesic
Sensor Net connected to an AC-coupled, high-input-impedance
amplifier (200 MV, Net Ampsi, Electrical Geodesics, Inc.,
Eugene, OR). Amplified analog voltages (0.1 – 100 Hz bandpass,3 dB) were sampled at 500 Hz. Individual sensors were adjusted
until impedances were less than 50 kV. EEG was measured withrespect to an average reference. The average reference wascomputed by subtracting the mean voltage across all electrodes
from the voltage measured at each individual electrode (Dien,
1998). Trials in which a blink was recorded within 1000 ms of theprobe were discarded (blinks were located by monitoring electrooculogram for voltages over 70 AV). Overall, 4% of trials werediscarded because of blinks.
Oscillatory analyses
A Morlet wavelet transform (6 cycles) was used to compute
EEG oscillatory power at 9 time points after each probe (0,100, . . . , 800 ms) for 21 logarithmically spaced frequencies
between 2 and 64 Hz (2x/4 Hz for x Z 4, . . . , 24). We thenconverted each subject’s log-transformed power P in each trial i
to a Z score at each frequency f, time point t, and electrode e:Zð f ;t;eÞ ðiÞ ¼

Pð f ;t;eÞ ðiÞ  lð f ;t;eÞ
rð f ;t;eÞ

ð1Þ

l ( f ,t ,e ) and r ( f ,t ,e ) are the mean and standard deviation,
respectively, of the subject’s log-transformed oscillatory powerat frequency f, time point t, and electrode e across all trials. Eq.
(1) was computed separately for each subject, and then all
subjects’ trials were combined into a single data set.
We next used a multivariate linear model to characterize therelation between oscillatory power and task variables at eachfrequency, time point, and electrode. Fitting data from only correct
trials (errors were rare), we used ordinary least squares (OLS) toestimate values for the bs in the following model:
Lag
RTQ
LL
Zð f ;t;eÞ ¼ bTL
ð f ;t;eÞ TL þ bð f ;t;eÞ RTQ þ bð f ;t;eÞ LL þ bð f ;t;eÞ Lag

þ bTLCð f ;t;eÞ TLC þ e

ð2Þ

TL is a dummy variable indicating whether each trial’s probe istarget (1) or lure (0). RTQ indicates each trial’s RT quartile (1 =fastest responses, 4 = slowest). To compute RTQ, RTs from eachtrial were binned by rank into quartiles individually for eachsubject. This procedure ensures that fast- and slow-responding
subjects contribute equally to each RTQ label, to allow us to
study relative RT changes. LL indicates each trial’s study list
length. Lag indicates how recently the probe was studied. In
target trials, Lag is the number of stimuli between the probe’s
study presentation and the end of the study list. In lure trials, Lag
is equal to LL.
Finally, TLC (target – lure confidence) is a variable introduced to represent the interaction between TL and RTQ.
Specifically, TLC combines TL and RTQ to quantify the match
between the test probe and memory of the just-presented list.
Given the well-documented correlation between RT and
response confidence (Murdock, 1974), we expected fast target
responses when subjects were confident that they had previously viewed the probe—an indication that the probe closely
matched their memory of a list stimulus. Inversely, we expected
fast lure responses when subjects were confident they had not980

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

viewed the probe, indicating that the probe was very different
from their memory of list stimuli. We expected slower
responses when there was a moderate degree of match between
the probe memory of the list. TLC has values between 1 and 4
for the four values of RTQ for lure responses, ordered fromfastest to slowest, and between 5 and 8 for the four values of
RTQ for target responses, ordered from slowest to fastest. Thus,an EEG pattern that correlates with TLC indicates the degree ofmatch between the probe and the memory of the just-presented
list.
We Z-transformed the computed values of the task variables,
obviating the need for a constant term in Eq. (2). Positive values ofb TL, b RTQ, b LL, b Lag, and b TLC indicate increased oscillatory
power in trials with target probes, slow responses, long lists, probes
for recently viewed stimuli, and high memory confidence,
respectively. e indicates the residual from the linear model.
Topographic plots were created with the topoplot function from
EEGLAB (Delorme and Makeig, 2004).
Correcting for multiple comparisons
Since Eq. (2) is applied at a number of frequencies, time
points, and electrodes, it was necessary to adjust our significancethreshold to control the number of times we falsely rejected the
hypothesis that b was 0. Each fit of the model in Eq. (2) resulted
in five P values (one for each b); the model was fitted at each of
129 electrodes, 8 after-probe time points, and 21 frequencies,
resulting in (5  129  8  21) 108,360 statistical comparisons.
We analyzed the resulting distribution of P values with the False
Discovery Rate (FDR) procedure (Genovese et al., 2002) tocompute a P threshold of 0.0039 that sets the rate of falsely
rejected null hypotheses to 5%. This procedure ensures a lessthan 5% rate (on average) of false positives among any null
hypotheses we reject at P < 0.0039. In the accompanying figures,
we plot b as 0 (green) when the corresponding P  0.0039,
meaning that colored (e.g., red or blue) points on plots indicatethat P < 0.0039. Thus, the FDR-determined P threshold indicatesthat a colored data point has a 95% chance of being a correct
rejection of the null hypothesis and a <5% chance of being a
false positive.

Results
RT increased approximately linearly as list length increased
from 2 to 6 items (627 T 26 ms, 678 T 27 ms, and 736 T 31 ms
for list lengths 2, 4, and 6, respectively). Error rates showed asimilar increase with list length (2.7% T 0.5%, 5.2% T 1%, and10.3% T 1% for list lengths 2, 4, and 6, respectively). Thispattern is in agreement with previous observations (Sternberg,1966). The overall high level of accuracy led us to discard trials
containing incorrect responses. The mean RTs of trials with each
RTQ label were 498 T 16 ms, 589 T 22 ms, 682 T 28 ms, and805 T 42 ms, ordered from fastest to slowest. Behavioral statistics
were calculated across subjects and reported as mean T standard
error.
Fig. 1 summarizes the results of our regression model (Eq. (2))
at various electrodes. In subsequent sections, we focus on detailingthe patterns that were robust at a range of electrodes, frequencies,
and time points. These included b TL and/or b TLC, which was
positive at ¨4 Hz, ¨300 – 800 ms after probe in the left-parietal

region (see Figs. 1B – D). b RTQ was positive at central electrodes at¨7 Hz, ¨500 – 800 ms after probe (see Fig. 1H), and at widespreadelectrodes at ¨32 Hz, 0 – 400 ms after probe (see Figs. 1A – D,
G – J). b LL was negative at ¨3 – 7 Hz, 0 – 400 ms after probe atwidespread electrodes (see Figs. 1A, B, D – J). Finally, b Lag was
negative at 2 Hz, 0 – 400 ms after probe at left-parietal electrodes(see Figs. 1C – E).
Oscillatory correlates of memory recognition (TL and TLC)
We observed increased left-parietal theta power following
target probes (indicated by positive values of b TL in Figs. 1B –D). To further illustrate the theta target – lure phenomenon, Fig.
2A plots left-parietal theta (4 Hz) power for the fastest andslowest quartiles of target and lure trials. At 300 ms after probe,
theta power was greater following target probes than followinglures. Furthermore, this pattern was magnified in trials whensubjects responded quickly—a finding that inspired us to create
the TLC variable to directly study the interaction between TL andRTQ.
At a left-parietal electrode (Fig. 1D), we observed that b TLC but
not b TL was positive at 4 Hz, 300 ms after probe, indicating thattheta power at this time point is best predicted by the degree ofmatch between the probe and memory contents. However, at 4 Hz,
400 – 800 ms after probe, b TL but not b TLC was positive, indicatingthat theta power is best predicted by whether the probe is a target orlure (TL) rather than by TLC. Fig. 2B topographically plots b TLC
at 4 Hz, 300 ms after probe, illustrating that the effect localizes tothe left-parietal region. Likewise, Fig. 2C topographically plots
b TL at 4 Hz, 500 ms after probe.
Oscillatory correlates of reaction time (RTQ) in decision making
Fig. 1H indicates that b RTQ at a central electrode is positive at¨4 – 8 Hz, ¨400 – 900 ms after probe, indicating increased theta
power in trials in which subjects responded slowly. To further
illustrate this relation, Figs. 3A and B plot the time courses oftheta (7 Hz) power at this electrode, aligned to the probe andresponse, respectively. Fig. 3A shows that theta power consistently increased after the probe’s onset, and remained elevatedfor a duration proportional to RTQ. Fig. 3B shows that theta
power began to decrease shortly before the response. Fig. 3C
topographically plots b RTQ at 7 Hz, 600 ms after probe,
indicating that the theta – RTQ relation localized to central
electrodes. Beta and gamma oscillations also correlated with
RTQ (see Fig. 1H): 16 – 32 Hz power was elevated at ¨0 – 400
ms after probe in trials with slow RTs, as indicated by positive
values of b RTQ. Fig. 3D plots b RTQ at 32 Hz, 100 ms afterprobe, indicating that this phenomenon is present at widespreadelectrodes.
Oscillatory correlates of memory load (LL)
Figs. 1D – J illustrate negative values of b LL at 4 – 8 Hz, 100 –500 ms after probe, indicating increased theta power for shortcompared to long lists. This relation is detailed in Fig. 4A, where
4-Hz power at a right-parietal electrode, 100 ms after probe, isplotted as a function of list length and lag. Fig. 4B plots the
topography of b LL at 4 Hz, 100 ms after probe, illustrating negative
relations between theta power and LL at widespread electrodes.
This pattern occurs so early after the probe’s onset (0 – 100 ms after

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

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Fig. 1. Regression analysis of oscillatory correlates of behavior. At each electrode, positive values for b TL, b RTQ, b LL, b Lag, and b TLC indicate increased
oscillatory power in trials with target probes, slow responses, long lists, probes for recently viewed items, and high memory confidence, respectively.
Electrode positions are indicated by position on electrode map (nose is up). bs are plotted as a function of frequency and after-probe time point. bs withan associated P  0.0039 are plotted as 0 (green). (A) Electrode 25 (F3). (B) Electrode 42 (C5). (C) Electrode 51 (TP7). (D) Electrode 53 (P3). (E)Electrode 66 (PO7). (F) Electrode 124 (F4). (G) Electrode 104 (C6). (H) Electrode 107 (immediately right-anterior to CZ). (I) Electrode 87 (P4). (J)Electrode 85 (PO8).

probe) that it probably relates to maintaining the studied list inmemory rather than to probe processing.
Oscillatory correlates of probe position (Lag)
Figs. 1C – E describe negative values for b Lag at 2 Hz,
indicating increased delta power following probes for recently
viewed items. Fig. 4C details this relation by plotting 2-Hz power
as a function of lag at PO7. Fig. 4D plots the topography of b Lag at
2 Hz, 300 ms after probe, indicating that this pattern localized to
the left-occipital region.
Distinguishing oscillatory and event-related potential (ERP)
correlates of behavior
Event-related potential (ERP) memory studies have reported aleft-parietal voltage increase following presentation of target
probes (Friedman and Johnson, 2000). Since the time course andtopography of this phenomenon are similar to the b TL patterns weobserved (Fig. 2), we were interested in determining if these
phenomena were directly related. Thus, we designed a framework
to distinguish oscillatory and ERP correlates of task variables.
First, we performed dual univariate regressions to study how
oscillatory power and probe-evoked voltage related independently
to a task variable:
Y ¼ bpow
ð f ;t;eÞ powð f ;t;eÞ þ e

ð3Þ

Y ¼ bðERP
t;eÞ ERPðt;eÞ þ e :ð4Þ

Y is a placeholder for one of the task variables described above.
Indices f, t, and e represent the frequency, after-probe time point,
and electrode of interest. ERP(t,e) indicates the baseline-corrected,
Z-transformed voltage at after-probe time point t. Voltage isbaseline corrected to the average voltage 100 – 0 ms relative tothe probe. Each subject’s data were individually Z-transformed and982

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

Fig. 2. Relation between probe target – lure status (TL) and left-parietal theta power. (A) Normalized after-probe theta (4 Hz) power at a left-parietal electrode(P3) for the fastest and slowest quartiles of responses, for targets and lures. Power at each time point for each quartile is normalized (Z-transformation) relative
to the distribution of power values at stimulus onset (0 ms). (B) Topographic plot of b TLC at 4 Hz, 300 ms after probe. (C) Topographic plot of b TL at 4 Hz, 500
ms after probe. In panels B and C, bs with an associated P  0.0039 are plotted as 0 (green).

then combined into a single dataset. Thus, the regressiondetermined b ERP and b pow indicate Y’s independent relations withoscillatory power and evoked voltage, respectively.
To quantify the nature of any correlations between therespective relations of evoked voltage and oscillatory power totask variables, we performed a bivariate regression to use thesevariables simultaneously to predict task variable Y:
pow bibi
Y ¼ bERPð f ;t;eÞ ERPðt;eÞ þ bð f ;t;eÞ powð f ;t;eÞ þ e :ð5Þ

Fig. 3. EEG oscillations and reaction time (RTQ). (A) Theta (7 Hz) power at a central electrode (107, immediately right-anterior to CZ), aligned to the probe’s
onset, for trials with different RTQ labels. (B) Theta (7 Hz) power at this same electrode aligned to subject’s response. In panels A and B, power is Ztransformed relative to 0 ms. (C) Topographic plot of b RTQ at 7 Hz, 600 ms after probe. (D) Topographic plot of b RTQ at 32 Hz, 100 ms after probe. In C and D,
b RTQ with an associated P  0.0039 is plotted as 0 (green).

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

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Fig. 4. Oscillatory power as a function of list length (LL) and lag since first probe presentation (Lag). (A) Theta (4 Hz) power 100 ms after probe at electrode 79(immediately medial to P4). Power is plotted as a function of lag since first probe presentation, grouped by list-length. (B) b Lag at 4 Hz, 100 ms after probe,
plotted topographically. (C) Delta (2 Hz) power 300 ms after probe at PO7. (D) b Lag at 2 Hz, 300 ms after probe, plotted topographically. In panels A and C,
error bars denote 95% confidence intervals. In panels B and D, bs with an associated P  0.0039 are plotted as 0 (green).

To study how ERPs and oscillatory power relate to TL, we
compared the regression-determined bs from Eqs. (3) and (4)to those from Eq. (5), substituting TL for Y. To the extent that–bi m b ERP, or b pow–bi m b pow , then oscillatory power andb (ERPf,t,e)(t,e)( f,t,e)( f,t,e)ERPs are correlated in their relation to Y. For example, ifERP–bi|b (ERPf,t,e) | < |b (t,e) |, it indicates that part of the relation of probeevoked voltage to Y is best captured by means of oscillatory power.
ERP–bi
Inversely, if b (ERP
f,t,e) = b (t,e) , it indicates that ERPs and oscillatory
power independently correlate with Y.
Fig. 5A plots b pow at electrode P3, indicating that from 250 to
500 ms after probe, target probes elicit greater 4-Hz power than
do lures. Fig. 5B plots b ERP, indicating that target probes elicit
greater voltage than do lures from 200 to 300 ms after probe, andlures elicit greater voltage than do targets from 400 to 600 ms
after probe. To determine if the oscillatory and ERP target – lureeffects correlate at each time point, Figs. 5C and D plot b pow–bi
and b ERP–bi. b pow–bi and b ERP–bi show the same trends as b pow
and b ERP, indicating that b’s value did not vary according towhether it was computed in the univariate (Eqs. (3) and (4)) or
bivariate (Eq. (5)) regressions. From this finding, we inferred thattheta power changes do not fully account for the ERP target – lureeffect.
Since ERP lag effects have been characterized previously(Golob and Starr, 2004), we were interested in determining if theoscillatory lag effects that we observed (e.g., Fig. 4C) werecorrelated with ERP effects. Fig. 6A compares ERP and oscillatory
correlates of Lag, indicating that from 0 to 500 ms after probe,
increased 2 – 4-Hz power occurred following recently viewed
probes (small values of Lag). Fig. 6B indicates that, at 200 ms
after probe, increased voltage is associated with larger values ofLag, as indicated by positive values of b ERP. At this time point,

however, b ERP–bi at 2 – 4 Hz is not significantly positive (Fig. 6D),
indicating a correlation between the 2 – 4-Hz and ERP Lag effects.
ERP correlates of response confidence
Previous studies found that negative voltage deflections in
response-locked ERPs were greatest in magnitude when subjects
responded incorrectly (Luu et al., 2004; Pailing et al., 2000).
Since our data indicated that RTQ can measure response
confidence, we studied how response negativity related toconfidence by studying response-locked ERPs for different valuesof RTQ. Fig. 7 plots the response-locked ERP for correct
responses at a central electrode grouped by RTQ. ERPs are mostnegative when responses are slow, consistent with previous
findings (Pailing et al., 2000).

Discussion
EEG working-memory studies have identified numerous
relations between EEG oscillations and task manipulations. The
present work replicates and extends several of these findings, byillustrating oscillatory patterns at different electrodes that simultaneously correlated with different task variables. These patterns
were especially prevalent in the theta band, where we observed
correlates of memory recognition, RT, and memory load atdifferent electrodes and after-probe latencies. These observations
indicate that although theta oscillations are present in widespreadcortical regions, their functional role varies throughout thecortex—consistent with observations of multiple local corticaltheta generators (Raghavachari et al., 2006).

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J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

Fig. 5. Distinguishing oscillatory and ERP correlates of TL (bs from Eqs. (3), (4), and (5) at P3, substituting TL for Y). b plotted as 0 (green) whencorresponding P  0.01. (A) b pow (Eq. (3)). (B) b ERP (Eq. (4)). (C) b pow–bi (Eq. (5)). (D) b ERP–bi (Eq. (5)).

Our data indicate that left-parietal theta relates to memory
recognition (Fig. 2). Left-parietal theta power initially (300 ms
after probe) correlated with the degree of match between the probe
and memory contents (TLC); later (500 ms after probe), it was best
predicted by whether the probe was a target or lure (TL). We
analyzed whether this theta target – lure effect was similar to the

ERP target – lure effect, and found that they were not directly
related, consistent with previous reports (Klimesch et al., 2000).
ERPs result from a complex pattern of power and phase dynamics
at a range of frequencies, but our analysis was only designed to
distinguish power changes at single frequencies that affect ERP
amplitude. Thus, we cannot rule out the possibility that theta

Fig. 6. Distinguishing oscillatory and ERP correlates of Lag (bs from Eqs. (3), (4), and (5) at PO7, substituting Lag for Y). b plotted as 0 (green) whencorresponding P  0.01. (A) b pow (Eq. (3)). (B) b ERP (Eq. (4)). (C) b pow–bi (Eq. (5)). (D) b ERP–bi (Eq. (5)).

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

Fig. 7. RTQ-grouped response-locked ERPs. Response-locked ERPs forcorrect responses grouped by RTQ at electrode 107 (immediately rightanterior to CZ). Recordings are baseline corrected to voltage 500 ms before
response.

oscillations relate to the ERP target – lure effect in a manner that isbeyond the scope of our analysis. Nonetheless, both the ERP andtheta target – lure effects are present at left-parietal electrodes 300
ms after probe, further implicating electrical activity recorded inthis region in memory recognition.
Our observations relating left-parietal theta to memory recognition should not be interpreted as evidence that left-parietal theta
is exclusive to working memory. In fact, recent work has describeda similar left-parietal theta-power increase 300 – 500 ms after
viewing open-class words (nouns, verbs, or adjectives) comparedto viewing closed-class words (determiners, conjunctions, orprepositions) (Bastiaansen et al., 2005). Since these patterns have
similar spatial and temporal characteristics, it seems reasonable tohypothesize that both patterns result from a more general
phenomenon relating left-parietal theta to memory retrieval. Other
studies observed that, in addition to theta, gamma power increased
following target stimuli, compared with lures, in long-term
recognition tasks (Gruber et al., 2004; Düzel et al., 2003). Since
our analyses did not reveal this pattern, we hypothesize that
gamma’s relation to stimulus recognition is specific to tasks withlong retention intervals.
Dual-process models of human memory generally assume theexistence of neural correlates of familiarity and recollection (Norman
and O’Reilly, 2003; Yonelinas, 2002). The neural-familiarity signal isa graded measure of the match between a stimulus and memory
contents, whereas recollection is an all-or-none signal reflecting
retrieval of the details of a stimulus. One interpretation of ourfindings suggests that left-parietal theta ¨300 ms after probe could
be a familiarity signal, because it correlated with TLC. Likewise, the¨500-ms left-parietal TL effect could be a correlate of recollection.
Consistent with this interpretation, the oscillatory correlates of TLC
and TL have time courses similar to observed ERP correlates offamiliarity and recollection (Curran, 2000).
Previous studies demonstrated that central or frontal theta
power related both to task difficulty (Gevins et al., 1997; Jensen
and Tesche, 2002) and to response-related error processing (Luu etal., 2004). These observations, and the present findings, areconsistent with central theta power relating to decision making.
Figs. 3A and B illustrate that central theta (¨7 Hz) power increased
after probe onset and decreased before the response. This patterncorrelated best with RTQ (negative values of b RTQ in Fig. 1H),
indicating that central theta related best to decision making rather

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than to stimuli manipulation. Consistent with this pattern, theanterior cingulate cortex, which underlies central electrodes, hasbeen implicated in decision making via functional imaging studies(Botvinick et al., 2004).
Recent work found that increased theta power during incorrect
responses related to the ERP error-related negativity (ERN) pattern(Luu et al., 2004). This pattern is consistent with theta relating todecision difficulty, because incorrect responses are associated withdecreased confidence (and increased RTs). Since we observed that
central theta power correlated with response confidence, it is also
possible that the ERN related most directly to response confidence(rather than to error processing). We directly studied this byexamining response-locked ERPs in correct trials (Fig. 7), andfound that voltage was most negative for slow responses,
consistent with previous findings (Pailing et al., 2000).
When we examined the relation between oscillatory power andmemory load (e.g., Fig. 4A), we found that theta power decreased
with increasing memory load: Fig. 1 shows that, at widespreadelectrodes, b LL is negative at 4 Hz. This finding is opposite to thetrend suggested by a previous study that analyzed the retention
interval (Jensen and Tesche, 2002). This difference indicates that
EEG oscillations can relate differently to behavior during distinct
portions of the task. Moreover, this behavior-related power
decrease exemplifies how oscillations can relate to behavior via
both power increases, as is commonly seen in the theta band (Fig.
2), and power decreases, as is seen in the inverse relation between
alpha power and attention (Pfurtscheller et al., 1996).
One version of a recent theoretical working-memory model
suggested that the positive correlation between RT and memory
load (Sternberg, 1966) is caused by an increase in theta’s
wavelength in proportion to the number of items stored in memory(Jensen and Lisman, 1998). Thus, this model predicts that theta’s
frequency would decrease with increasing memory load (LL). In
our analyses, this pattern would result in negative values of b LL at
the upper part of the theta frequency range (¨8 Hz) and positive
values of b LL at the lower end (¨4 Hz). Since our analyses foundnegative values of b LL at 4 Hz, they do not support this model.
Although our data do not demonstrate behavior-related EEG
frequency shifts at individual electrodes, we did observe thatdifferent frequencies within the 48-Hz theta band best correlatedwith behavior across brain regions. Fig. 1H indicates that b RTQ was
greatest at central electrodes at ¨7 Hz; Fig. 1D shows that b TL was
greatest in left-parietal regions at ¨4 Hz. These theta-frequency
differences could be explained by regional differences in neural
structure (Buzsáki and Draguhn, 2004).
We also observed correlates of behavior in oscillations at
frequencies outside the theta band: the left-parietal Lag effect (seeFig. 4C) is best characterized as a 2-Hz oscillatory effect but is also
visible in ERPs. This finding indicates that previous observationsof ERP serial-position effects (Golob and Starr, 2004) may also
relate to phase-locked delta activity. In the alpha band, we foundthat oscillatory power ¨700 ms after probe was greatest for fastRTs (Fig. 1), indicating that alpha power increased after subjects
had responded and were not cognitively engaged. This is consistent
with reports of alpha as a correlate of cortical idling (Pfurtscheller
et al., 1996).
Finally, we found that increased gamma power after probe wascorrelated with slow RT (Fig. 3D). This relation is present
immediately after the probe’s onset, so it likely is related to attention
or task vigilance rather than to probe processing. Although recent
studies have also observed relations between gamma power and RT,

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J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987

the reported direction of this relation varied depending on whether
subjects could precisely predict stimuli onset: in studies in which
subjects could predict when the cue would appear, increased gamma
power was associated with fast responses (Gonzalez Andino et al.,2005); if subjects could not precisely predict the probe’s onset,
increased gamma power was present in slow trials (Jokeit andMakeig, 1994). We observed increased gamma power in trials inwhich subjects responded slowly, consistent with the fact that ourprobe onset was temporally jittered, preventing subjects fromanticipating its exact onset.

Conclusions
We used a multivariate statistical framework to study how EEG
oscillations related to working memory. Power of theta oscillations
simultaneously correlated with different task variables: theta at leftparietal electrodes correlated with memory recognition, theta at
central electrodes correlated with decision making, and theta at
widespread electrodes correlated with memory load. In addition totheta, we also observed correlates of task variables in the delta,alpha, and gamma frequency bands. These patterns indicate that
EEG oscillations can be used to map the functional roles ofwidespread brain regions. Our findings are the first to describe
distinct patterns of theta oscillations that simultaneously relate tounique aspects of behavior. The range of patterns we observed
indicates the functional diversity of human brain oscillations, andbrings a new perspective to the debate over theta’s role.

Acknowledgments
We would like to acknowledge Polly Johnsen for her editorialassistance, and Per Sederberg, Marieke van Vugt, and Sean Polynfor their helpful discussions on data analyses. For help with subject
testing, we thank Daniel Blum, Sophie Boddington, Dan Collins,
Tatsuko GoHollo, Megan Lipsett, Gabriel Matthews, Brion
Woroch, and Brent Young. M.J.K. acknowledges support from
National Institutes of Health research grants MH61975 andMH062196, NSF grant SBE0354378, and the Swartz Foundation.
T.C. acknowledges support from National Institutes of Healthresearch grant MH64812.

References
Bastiaansen, M., Hagoort, P., 2003. Event-induced theta responses as awindow on the dynamics of memory. Cortex 39, 967 – 992.
Bastiaansen, M., van der Linden, M., ter Keurs, M., Dijkstra, T., Hagoort, P.,
2005. Theta responses are involved in lexical-semantic retrieval during
language processing. J. Cogn. Neurosci. 17, 530 – 541.
Botvinick, M., Cohen, J., Carter, C., 2004. Conflict monitoring and anterior
cingulate cortex: an update. Trends Cogn. Sci. 8, 539 – 546.
Buzsáki, G., 2002. Theta oscillations in the hippocampus. Neuron 33 (3),325 – 340.
Buzsáki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks.
Science 304, 1926 – 1929.
Caplan, J.B., Madsen, J.R., Raghavachari, S., Kahana, M.J., 2001. Distinct
patterns of brain oscillations underlie two basic parameters of humanmaze learning. J. Neurophysiol. 86, 368 – 380.
Caplan, J.B., Madsen, J.R., Schulze-Bonhage, A., Aschenbrenner-Scheibe,R., Newman, E.L., Kahana, M.J., 2003. Human theta oscillations

related to sensorimotor integration and spatial learning. J. Neurosci. 23,4726 – 4736.
Curran, T., 2000. Brain potentials of recollection and familiarity. Mem.
Cogn. 28, 923 – 938.
de Araujo, D.B., Baffa, O., Wakai, R.T., 2002. Theta oscillations andhuman navigation: a magnetoencephalography study. J. Cogn. Neurosci. 14 (1), 70 – 78.
Delorme, A., Makeig, S., 2004. EEGLAB: an open source toolbox foranalysis of single-trial EEG dynamics. J. Neurosci. Methods 134,
9 – 21.
Dien, J., 1998. Issues in the application of the average reference: review,
critiques, and recommendations. Behav. Res. Meth. Instrum. Comput.
30, 34 – 43.
Düzel, E., Habib, R., Scott, B., Schoenfeld, A., Lobaugh, N., McIntosh,
A.R., et al., 2003. A multivariate spatiotemporal analysis of electromagnetic time-frequency data of recognition memory. NeuroImage 18 (2),185 – 197.
Ekstrom, A., Caplan, J., Ho, E., Shattuck, K., Fried, I., Kahana, M.J., 2005.
Human hippocampal theta activity during virtual navigation. Hippocampus 15, 881 – 889.
Forrin, B., Cunningham, K., 1973. Recognition time and serial position ofprobed-item in short-term memory. J. Exp. Psychol. 99 (2), 272 – 279.
Friedman, D., Johnson, R., 2000. Event-related potential (ERP) studies ofmemory encoding and retrieval: a selective review. Microsc. Res. Tech.
51, 6 – 28.
Genovese, C.R., Lazar, N.A., Nichols, T.E., 2002. Thresholding ofstatistical maps in functional neuroimaging using the false discovery
rate. NeuroImage 15, 870 – 878.
Gevins, A., Smith, M.E., McEvoy, L., Yu, D., 1997. High-resolution EEG
mapping of cortical activation related to working memory: effects oftask difficulty, type of processing, and practice. Cereb. Cortex 7 (4),374 – 385.
Golob, E., Starr, A., 2004. Serial position effects in auditory event-related
potentials during working memory retrieval. J. Cogn. Neurosci. 16, 40 –52.
Gonzalez Andino, S., Michel, C., Thut, G., Landis, T., Peralto, de R.G.,
2005. Prediction of response speed by anticipatory high-frequency(gamma band) oscillations in the human brain. Hum. Brain. Mapp. 24,
50 – 58.
Gruber, T., Matthias, M., 2005. Oscillatory brain activity dissociates
between associative stimulus content in a repetition priming task in
the human EEG. Cereb. Cortex 15, 109 – 116.
Gruber, T., Tsivilis, D., Montaldi, D., Müller, M., 2004. Induced gamma
band responses: an early marker of memory encoding and retrieval.
NeuroReport 15, 1837 – 1841.
Howard, M.W., Rizzuto, D.S., Caplan, J.C., Madsen, J.R., Lisman, J.,
Aschenbrenner-Scheibe, R., et al., 2003. Gamma oscillations correlatewith working memory load in humans. Cereb. Cortex 13, 1369 – 1374.
Hwang, G., Jacobs, J., Geller, A., Danker, J., Sekuler, R., Kahana, M.J.,
2005. EEG correlates of subvocal rehearsal in working memory.
Behav. Brain Funct. 1, 20.
Jensen, O., Lisman, J.E., 1998. An oscillatory short-term memory buffer
model can account for data on the Sternberg task. J. Neurosci. 18,10688 – 10699.
Jensen, O., Tesche, C.D., 2002. Frontal theta activity in humans increases
with memory load in a working memory task. Eur. J. Neurosci. 15,1395 – 1399.
Jensen, O., Gelfand, J., Kounios, J., Lisman, J.E., 2002. Oscillations in thealpha band (9 – 12 Hz) increase with memory load during retention in ashort-term memory task. Cereb. Cortex 12, 877 – 882.
Jokeit, H., Makeig, S., 1994. Different event-related patterns of gammaband power in brain waves of fast- and slow-reacting subjects. Proc.
Natl. Acad. Sci. U. S. A. 91 (14), 6339 – 6343.
Kahana, M.J., Sekuler, R., Caplan, J.B., Kirschen, M., Madsen, J.R., 1999.
Human theta oscillations exhibit task dependence during virtual mazenavigation. Nature 399, 781 – 784.
Kahana, M.J., Seelig, D., Madsen, J.R., 2001. Theta returns. Curr. Opin.
Neurobiol. 11, 739 – 744.

J. Jacobs et al. / NeuroImage 32 (2006) 978 – 987
Klimesch, W., Doppelmayr, M., Schwaiger, J., Winkler, T., Gruber, W.,
2000. Theta oscillations and the ERP old/new effect: independent
phenomena? Clin. Neurophysiol. 111, 781 – 793.
Klimesch, W., Hanslmayr, S., Sauseng, P., Gruber, W., Brozinsky, C.J.,
Kroll, N.E.A., et al., 2006. Oscillatory EEG correlates of episodic trace
decay. Cereb. Cortex 16 (2), 280 – 290.
Luu, P., Tucker, D., Makeig, S., 2004. Frontal midline theta and the errorrelated negativity: neurophysiological mechanisms of action regulation.
Clin. Neurophysiol. 115, 1821 – 1836.
Murdock, B.B., 1974. Human Memory: Theory and Data. Lawrence
Erlbaum and Associates, Potomac, MD.
Norman, K.A., O’Reilly, R.C., 2003. Modeling hippocampal and neocortical contributions to recognition memory: a complementary learning
systems approach. Psychol. Rev. 110, 611 – 646.
Pailing, P., Segalowitz, S., Davies, P., 2000. Speed of responding and thelikelihood of error-like activity in correct trial ERPs. Psychophysiology,
37.

987

Pfurtscheller, G., Stancâk, A.J., Neuper, C., 1996. Event-related synchronization (ERS) in the alpha band—An electrophysiological correlate ofcortical idling: a review. Int. J. Psychophysiol. 24, 39 – 46.
Raghavachari, S., Lisman, J.E., Tully, M., Madsen, J.R., Bromfield, E.B.,
Kahana, M.J., 2006. Theta oscillations in human cortex during aworking memory task: evidence for local generators. J. Neurophysiol.
95 (3), 1630 – 1638.
Sternberg, S., 1966. High-speed scanning in human memory. Science 153,
652 – 654.
Sternberg, S., 1975. Memory scanning: new findings and current
controversies. Q. J. Exp. Psychol. 27, 1 – 32.
Tallon-Baudry, C., Bertrand, O., Henaff, M.-A., Isnard, J., Fischer, C.,2005. Attention modulates gamma-band oscillations differently in thehuman lateral occipital cortex and fusiform gyrus. Cereb. Cortex 15 (5),654 – 662.
Yonelinas, A., 2002. The nature of recollection and familiarity: a review of
30 years of research. J. Mem. Lang. 46, 441 – 517.