Neuroscience 262 (2014) 92–106

CONCURRENT WORKING MEMORY TASK DECREASES THE STROOPINTERFERENCE EFFECT AS INDEXED BY THE DECREASEDTHETA OSCILLATIONS

Y. ZHAO, a D. TANG, a L. HU, a L. ZHANG, a�

G. HITCHMAN, a L. WANG a AND A. CHEN a,b*

aKey Laboratory of Cognition and Personality of Ministry of

Education, Faculty of Psychology, Southwest University,

Chongqing 400715, People’s Republic of China

bResearch Center of Psychological Development and Education,

Liaoning Normal University, Dalian, Liaoning 116029, People’s

Republic of China

Abstract—Working memory (WM) tasks may increase or

decrease the interference effect of concurrently performed

cognitive control tasks. However, the neural oscillatory

correlates of this modulation effect of WM on the Stroop task

are still largely unknown. In the present study, behavioral

and electroencephalographic (EEG) data were recorded from

32 healthy participants during their performance of the

single Stroop task and the same task with a concurrent WM

task. We observed that the Stroop interference effect repre-

sented in both response times (RTs) and theta-band event-

related spectral perturbation (ERSP) magnitude reduced

under the dual-task condition compared with the single-task

condition. The reduction of interference in theta-band ERSP

was further positively correlated with interference reduction

in RTs, and was mainly explained by the source in the left

middle frontal gyrus. In conclusion, the present study

suggests that the effect of concurrent WM tasks on the

reduction of the Stroop interference effect can be indexed

by EEG oscillations in theta-band rhythm in the centro-

frontal regions and this modulation was mediated by the

reduced cognitive control under the concurrent WM task.

� 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Key words: working memory load, Stroop interference effect,

EEG oscillations, theta-band rhythm.

0306-4522/13 $36.00 � 2014 IBRO. Published by Elsevier Ltd. All rights reservhttp://dx.doi.org/10.1016/j.neuroscience.2013.12.052

*Correspondence to: A. Chen, Faculty of Psychology, SouthwestUniversity, Chongqing 400715, People’s Republic of China. Tel:+86-23-68367642.

E-mail address: xscat@swu.edu.cn (A. Chen).† Present address: NeuroImaging Center, Department of Neurosci-

ence, University Medical Center Groningen, University of Groningen,The Netherlands.Abbreviations: ACC, anterior cingulate cortex; ANOVA, analysis ofvariance; CWT, continuous Morlet wavelets transform; DI, dual &incongruent; DLPFC, dorsolateral prefrontal cortex; DN, dual & neutral;EEG, electroencephalography; ERD, event-related desynchronization;ERP, event-related potential; ERS, event-related synchronization;ERSP, event-related spectral perturbation; FDR, false discovery rate;ICA, independent component analysis; LORETA, low resolution brainelectromagnetic tomography; MRI, magnetic resonance imaging; RT,response time; SI, single & incongruent; SN, single & neutral; S-ROI,spatial regions of interest; TF-ROI, time-frequency regions of interest;WM, working memory.

92

INTRODUCTION

Cognitive control is a key function for human beings to

adapt flexibly and dynamically to the complex and ever-

changing environment. It represents the ability to select

a weaker, but task-relevant stimulus or behavior while

ignoring or inhibiting the stronger, but task-irrelevant one

(Miller and Cohen, 2001). WM refers to the ability of

temporary information storage and manipulation in the

brain (D’Esposito et al., 1995; Durstewitz et al., 2000).

While different, a huge body of evidence has convinced

researchers that the two constructs are to some degree

overlapped (D’Esposito et al., 1995; Miller, 2000; Kane

and Engle, 2002; Heitz and Engle, 2007; Salminen

et al., 2012; Unsworth et al., 2012; Barbey et al., 2013;

Morey and Bieler, 2013; Weldon et al., 2013). In

accordance with this notion, manipulating the type and/

or amount of WM load may significantly affect one’s

behaviors during performance of cognitive control tasks,

such as the Stroop task (Stroop, 1935), where

participants are instructed to make a response

according to the printed color of the word while ignoring

its semantic meaning. For example, de Fockert et al.

(2001) found a concurrent verbal WM task impaired the

performance of a name-face Stroop task, leading them

to conclude the WM task and cognitive control task

recruited the same limited central resources. Kim et al.

(2005) used several transformed versions of Stroop

tasks and found that when a verbal short-term WM task

was concurrently performed along with a meaning-

comparison Stroop task, in which participants had to

compare the meaning of a written colored word with the

color of a simultaneously presented patch, the Stroop

interference effect increased significantly. However, in

the second task, which used a similar paradigm,

participants had to identify whether the color of a patch

was the same as the color of a concurrently displayed

colored word. The word had meanings of ‘‘same’’ or

‘‘different’’. In that condition, the concurrent verbal short-

term WM task significantly boosted participants’

performance on the color-comparison Stroop task. Kim

et al. (2005) interpreted these results as suggesting that

in the meaning-comparison task, participants had to

process the meaning of written colored words; therefore,

word processing (the task target) in that condition

became a necessity. Consequently, concurrent verbal

WM took up the resource needed for target processing,

resulting in the Stroop interference effect increasing.

However, in the color-comparison Stroop task, the

processing of word meaning may have interfered with

ed.

participants’ decisions, thus words processing served as

the task distractor. As a result, concurrently loaded

verbal WM drained away the resource for words

processing, causing the interference effect of that

Stroop task to decrease.

Several subsequent studies to some extent replicated

Kim et al.’s results, finding concurrent WM tasks may

decrease Stroop interference (Park et al., 2007; Gil-

Gomez de Liano et al., 2010). Their observations, along

with Kim et al.’s original report, were generally

interpreted in terms of multiple resources theory (Navon

and Gopher, 1979; Egner et al., 2007; Egner, 2008).

That is, as the processing of the target and distractors

in a cognitive control task sometimes deploy different

resources, the interference effect will increase when the

concurrent WM task takes up the same resources as

the target processing, but decrease when the

concurrent WM task takes up the same resources as

the distractor processing (Kim et al., 2005; Park et al.,

2007; Zhao et al., 2010).

Although many studies have investigated the

modulation of concurrent WM tasks on the performance

of cognitive control tasks, the neural correlates of this

kind of modulation are still unknown. The few studies

investigating the related neural activities are limited to

functional magnetic resonance imaging (fMRI) or event-

related potential (ERP) studies (de Fockert et al., 2001;

Rissman et al., 2009; Spronk and Jonkman, 2012). To

the best of our knowledge, there have been no studies

exploring the neural oscillatory correlates in a cognitive

control task with concurrent WM demands. Due to their

advantages over the traditional ERP methods, EEG

oscillations have received a lot of recognition in recent

years for their use in cognitive neuroscience research

(Gevins et al., 1997; Klimesch, 1999; Raghavachari

et al., 2001; Hanslmayr et al., 2008; Lisman, 2010;

Cohen, 2011; Cohen and Cavanagh, 2011; Nigbur

et al., 2012; Tang et al., 2013). It has been known that

traditional ERP analysis performs well to detect scalp

electrophysiological activities time- and phase-locked to

the onset of a stimulus (e.g., presenting a visual

stimulus) or a response (e.g., pressing a response

button) (Cohen, 2011), which are usually termed evoked

activities. However, in addition to the well-known evoked

activities, there are also induced activities which are

time- but not phase-locked to the onset of a stimulus or

a response. These activities are believed to reflect

some kind of stimulus-induced ongoing EEG activity,

which carry important information about cognitive

processing but are usually averaged out as background

noises in the traditional ERP analysis (Makeig et al.,

2004). Some researchers have further stated that

induced activities reflect more complex brain features

that require iterative interactions between different

processing levels and memory, in comparison with

evoked activities, which have an instantaneous and

reflective feature (Eckhorn et al., 1990; Muller et al.,

1996). Thus, it is appealing to investigate the neural

correlates of higher brain functions such as concurrent

WM tasks on cognitive control with EEG methods.

The goal of the present research is to investigate the

neural correlates of a concurrent visual short-term WM

task on cognitive control with EEG methods. To achieve

this, we loaded the Chinese Stroop task with some to-

be-remembered meaningless visual shapes constructed

by hand-drawn lines (see Fig. 1), which were hard to be

associated with any meanings and could only be kept in

short-term WM by visual representations. The

concurrent visual short-term WM task may share a

common resource (for visual shape information

processing) with the Chinese characters based on the

findings that Chinese character recognition tends to be

strictly a visual form-to-meaning process, which is quite

different from English word recognition (Van Orden

et al., 1988; Plaut et al., 1996; Zhou and Marslen-

Wilson, 1996, 2000). The processing of color, on the

other hand, is believed to rely on another independent

cognitive processing system separated from shape

processing, in which only color information can be

stored and manipulated (Wheeler and Treisman, 2002;

Kim et al., 2005; Olsson and Poom, 2005). Thus we

speculated that the Chinese Stroop interference effect

under this type of concurrent short-term WM task may

decrease compared with the single Stroop task condition.

Previous studies have suggested that neural

oscillations in the theta band (4–7 Hz in frequency) and

alpha band (8–12 Hz in frequency) are often related to

higher level brain functions (Gevins et al., 1997;

Klimesch, 1999; Raghavachari et al., 2001; Hanslmayr

Fig. 1. Trial schema for the dual-task condition. The figure illustrates

a representative trial in the dual-task condition and its detailed timing.

The four WM load stimuli were randomly chosen for each trial from

the eight hand-drawn formations as depicted at the bottom right

corner of the picture. The Stroop stimulus in this trial as depicted in

this picture is a Chinese character meaning red but written in green.

(For interpretation of the references to color in this figure legend, the

reader is referred to the web version of this article.)

Y. Zhao et al. / Neuroscience 262 (2014) 92–106 93

et al., 2008; Lisman, 2010; Cohen, 2011; Cohen and

Cavanagh, 2011; Nigbur et al., 2012; Tang et al., 2013).

For example, in early 2001, Jenson and Tesche found

theta power increased linearly with concurrent WM load

(Jensen and Tesche, 2002). Hanslmayr et al. (2008)

further used a Stroop task with EEG activities

simultaneously recorded, and found that compared with

congruent trials, incongruent trials were accompanied

with larger theta and frontal alpha activities, indicating

the activation of theta oscillations for the incongruent

condition reflected the activation of central executive

processes. On the other hand, frontal alpha activity is

also found to be related to WM and cognitive control

(Jensen et al., 2002; Hanslmayr et al., 2008;

Mathewson et al., 2011). Thus we speculated that

oscillations in the theta-band and/or alpha-band

frequency ranges may be the potential neural oscillatory

correlates for the modulation of concurrent WM task on

cognitive control.

EXPERIMENTAL PROCEDURES

Participants

Thirty-two right-handed participants (14 females) with

ages ranging from 18 to 26 (22.6 ± 2.1, mean ± SD)

took part in the present experiment. All participants

were free from neurological disease and psychoactive

medication use, had normal or corrected-to-normal

vision and normal color perception assessed by the

Ishihara Color Test. They all gave informed consent and

were paid for their participation. The Southwest

University’s local ethics committee approved the

procedure. In addition, the participants were unaware of

the purposes of the experiment.

Apparatus and stimuli

The experiment was conducted using an E-prime

software (Psychology Software Tools, Inc., Pittsburgh,

PA), running on a Dell computer. Stimuli were presented

on a 17-inch computer monitor with a refresh rate of

85 Hz. Participants were seated in a comfortable chair

in a dim, sound-attenuated chamber at a distance of

approximately 60 cm from the screen. They were

instructed to keep relaxed and to respond by pressing

one of the pre-specified keys on a standard computer

keyboard.

The Stroop task consisted of two different conditions:

neutral and incongruent. For the neutral condition, the

stimulus was a colored Chinese character randomly

selected from the set of three neutral Chinese

characters: ‘‘格,’’ ‘‘是,’’ or ‘‘法.’’ (‘‘grid’’, ‘‘yes’’ or ‘‘law’’)

which are familiar to all native speakers and whose

pronunciation, meaning and formation are all different

from the color characters. For the incongruent condition,

the stimulus was also a colored character randomly

chosen among four Chinese color names: ‘‘红,’’ ‘‘黄,’’

‘‘蓝,’’ or ‘‘绿’’ (‘‘red’’, ‘‘yellow’’, ‘‘blue’’, or ‘‘green’’). The

above characters were colored in red (RGB 255, 0, 0),

blue (RGB 0, 0, 255), yellow (RGB 255, 255, 0), or

green (RGB 0, 255, 0) accordingly. The printed color

and semantics of the character in the incongruent

condition were different (e.g. RED was colored in

green). The whole WM task stimulus repository

(including all the to-be remembered items used in our

experiment) consisted of eight different imitative

structures constructed by hand-drawn lines. These

structures are similar to the structures of Chinese

characters but are not associated with any meanings. In

each trial of the WM task, four different structures were

randomly chosen and ordered from the whole

repository. In addition, they were displayed along a

horizontal row at the center of the screen with equal

spacing between each structure. All stimuli were

displayed on a gray (RGB 128, 128, 128) background.

Experimental design

The experiment included two within-subjects factors: the

task and congruency. The task factor consisted of either

the single Stroop task condition or the dual-task

condition (maintaining a WM load while performing the

Stroop task). The congruency factor had two levels:

‘‘neutral’’ or ‘‘incongruent’’. Thus the two factors resulted

in four different conditions: single & neutral (SN), single

& incongruent (SI), dual & neutral (DN), and dual &

incongruent (DI), with each condition including 96 trials.

Procedure and task

In the dual-task condition (Fig. 1), each trial began with

four horizontally ordered structures displayed at the

center of the screen for 2000 ms. Participants were

instructed to remember and maintain all the four

structures during each trial by visual encoding and not to

try to associate the structures with any meaning. After

the structures disappeared, a fixation cross was

presented for 500 ms, followed by a blank screen for

300–500 ms (interval varied randomly). Then a color-

word Stroop stimulus appeared, for a maximum duration

of 2000 ms. Participants were instructed to respond to

the printed color of the character and ignore its meaning

by pressing the ‘‘D’’ key using the left middle finger if the

printed color of the character was red, the ‘‘F’’ key using

the left forefinger if the printed color of the character

was yellow, the ‘‘J’’ key using the right forefinger if the

printed color of the character was green, and the ‘‘K’’

key using the right middle finger if the printed color of

the character was blue. They were instructed to respond

as quickly and accurately as possible. After a response

was made, the Stroop stimulus screen immediately

disappeared and was followed by a 100-ms blank

screen. Then a fixation cross was presented for 500 ms,

which was followed by the memory test display. During

the memory test stage, one structure appeared at the

center of the screen, which was either taken from the

four to-be-memorized structures presented in the load

display at the beginning of this trial or from the

remaining unseen structures. Participants were asked to

press the ‘‘T’’ key using the left forefinger if the test

structure matched one of the structures in the to-be-

memorized set, or the ‘‘U’’ key using the right forefinger

if not. The memory test was displayed until a response

94 Y. Zhao et al. / Neuroscience 262 (2014) 92–106

was made. Lastly, a blank screen was presented for 500–

800 ms (interval varied randomly).

In the single Stroop task condition, a 2000-ms blank

screen (replacing the WM load screen) was presented

at the beginning of each trial. Then the task

configuration was similar to that of dual-task condition

following the display of the WM load screen. After the

response to the Stroop stimulus was made, a 500–

800 ms blank screen was presented for a randomly

varied duration. Then the next trial began.

The task conditions were performed in separated

blocks and in a successive manner. The order of the

two task conditions was counterbalanced between

participants. Each task contained two successive

blocks, each of which contained 96 trials (trial

sequences were randomized), thus resulting in 384 trials

in total. The numbers of all possible color-word

combinations were the same in each block, so was the

proportion of incongruent and neutral trials. In the dual-

task condition, the proportion of the correct ‘‘T’’ and ‘‘U’’

responses in the memory test period was equal.

Between blocks there was a two-minute break allowing

participants to rest. Prior to four experimental blocks,

participants accomplished a training block which only

included the neutral Stroop stimuli from the dual-task

condition. Only when accuracy in the training block

exceeded 80% could participants go on to perform the

formal experiment.

Electrophysiological recording and analysis

EEG data were collected using a 64-channel Brain

Products system (Brain Products, GmbH, Germany;

pass band: 0.01–100 Hz, sampling rate: 500 Hz) that

was connected to a standard EEG cap based on the

extended 10–20 system. The left mastoid was used as

the reference channel, and all channel impedances

were kept lower than 5 kX. The electro-oculographic

signals were simultaneously recorded from four surface

electrodes, which were placed over the higher and lower

eyelid and 1 cm lateral to the outer corner of the left and

right orbit to monitor ocular movements, and eye blinks.

EEG data were preprocessed using routines taken

from EEGLAB, an open source toolbox running in the

MATLAB environment for electrophysiological signal

processing (Delorme and Makeig, 2004). After being

imported into MATLAB, the continuous data were re-

referenced to the average of both mastoids. Four

electro-oculographic channels and one right mastoid

channel were rejected. We then removed the linear

trend from the continuous data with the MATLAB

function ‘detrend()’. After that, data were digitally filtered

with a low cutoff value of 1 Hz and a high cutoff value of

30 Hz, respectively, using a finite impulse response

filter. Subsequently, the resulting data were segmented

into a time window from �800 to 2500 ms that was

time-locked to the Stroop stimulus onset and baseline

corrected using the pre-stimulus interval (�800 ms to

0 ms). Such relatively long epochs were taken for the

following reasons: (1) subsequent independent

component analysis (ICA) performs better with more

data points (Jung et al., 1998) and (2) edge effects in

the time–frequency analysis caused by sudden

transitions from signal values between trials would not

influence signals in the window of interest (Cohen and

Cavanagh, 2011). Epochs contaminated by spurious

gross-movement and other non-stereotyped artifacts

were identified by visual inspection and rejected.

Furthermore, any epochs corresponding to error or

missing trials (trials without responses) in the Stroop

task or WM task were further excluded from analysis.

We subsequently downsampled segmented data sets

from the original sampling rate of 500 Hz to 250 Hz,

after which the data sets were subjected to an ICA

algorithm provided in EEGLAB (Jung et al., 2000). In all

data sets, independent components representing eye

movements or blinks were identified by visual inspection

and removed. On average, 1.5 components were

removed (range, 0–2) per participant. After independent

component removal, additional baseline correction was

performed using the pre-stimulus time interval (�800 ms

to 0 ms). We then equalized the number of epochs

included in the four different conditions (DI, DN, SI, and

SN) of every participant by setting the number of

epochs in the condition with the least number of epochs

as a criterion and randomly selecting the same

corresponding number of epochs among all the epochs

in the other three conditions, respectively. This

procedure avoided potential confounding due to different

signal–noise ratios caused by different numbers of

epochs in each condition.

After all EEG data were preprocessed, an estimate of

the oscillatory power as a function of time and frequency

(time–frequency representation) was obtained from

single-trial EEG epochs using the continuous Morlet

wavelet transform (CWT) conducted by Letswave

software (http://amouraux.webnode.com) (Mouraux and

Iannetti, 2008). The parameters of central frequency (x)and restriction (r) in CWT were 5 and 0.15 respectively,

and time–frequency representations were explored from

1 to 30 Hz in steps of 0.58 Hz. Single-trial time–

frequency representations were then averaged to obtain

the averaged time–frequency representations of every

participant under each condition. The resulting averaged

time–frequency representations were exported from

Letswave and imported into MATLAB for further detailed

analysis.

To analyze the power modulation of ongoing EEG

rhythms after Stroop stimuli onset, ERSP was calculated

for every time–frequency pixel in the time–frequency

representations. For each estimated frequency, ERSP

was displayed as an increase or decrease of oscillatory

power relative to the baseline interval (�600 ms to

�200 ms) according to the formula: ERt,f%= [At,f � Rf]/

Rf, where At,f was the signal power at a given time t andat a given frequency f, and Rf was the averaged signal

power of frequency f within the baseline interval

(Pfurtscheller and Lopes da Silva, 1999). After

transforming the original power values to ERSP in the

time–frequency representations, we performed an

exploratory data-driven analysis routine to identify all the

time–frequency regions of interest (TF-ROIs) which were

most likely significantly modulated by factors of task and

Y. Zhao et al. / Neuroscience 262 (2014) 92–106 95

congruency or their interaction, and their corresponding

spatial regions of interest (S-ROIs). Before proceeding to

the specific details of such a routine, it is necessary to

point out that we only analyzed the time window of

1200 ms after stimuli onset, because participants tended

to finish their responses for both tasks within about

1000 ms after Stroop stimuli onset.

The exploratory data-driven analysis routine was

performed as follows:

(1) We first roughly identified several TF-ROIs with

maximal modulations related to the two experimen-

tal factors and their interaction, respectively. We

achieved this by calculating the time–frequency dif-

ference maps corresponding to the respective mod-

ulations of two factors and their interaction across

all the electrodes, and then the TF-ROIs, showing

the largest modulation of each effect from the differ-

ence maps, were identified.

(2) We calculated the mean of all the time–frequency

pixels included in a specific TF-ROI for each

electrode. For every TF-ROI, two-way repeated-

measures analysis of variances (ANOVAs) were

performed for each electrode and the resulting F

value for the specific effect corresponding to this

TF-ROI was extracted. Then all of the extracted Fvalues corresponding to the electrodes were plotted

as a scalp map. Based on the scalp regions show-

ing most pronounced F values, three S-ROIs which

were related to corresponding effects were identi-

fied: centro-frontal electrodes [(F1 + Fz + F2+

FC1 + FCz + FC2)/6, identified for both the main

effect of congruency and the interaction effect

between task and congruency]; right centro-

parietal electrodes [(FC4 + FC6 + C4+ C6+

CP4+ CP6)/6, identified for the main effect of task

type]; frontal electrodes [(F1 + Fz + F2 + AF3

+ AF4)/5, identified for the other main effect of task

type.

(3) Based on the defined S-ROIs, we firstly calculated

the magnitude difference between incongruent

and neutral conditions [expressed in ER% of

(DI + SI � DN � SN)] in the centro-frontal S-ROI

[(F1 + Fz + F2 + FC1+ FCz + FC2)/6] to evalu-

ate the potential congruency main effect; we secondly

calculated the magnitude difference between dual-

task and single-task conditions [expressed in ER%

of (DI + DN � SI � SN)] in the right centro-parietal

S-ROI [(FC4+ FC6+C4+C6+CP4+ CP6)/6]

and the frontal S-ROI [(F1 + Fz + F2+ AF3

+ AF4)/5] to evaluate the potential main effect of

task type; we lastly calculated the magnitude differ-

ence according to the formula (SI � SN) �(DI � DN) [expressed in ER% in the centro-frontal

S-ROI (F1 + Fz + F2+ FC1 + FCz + FC2)/6] to

evaluate the potential interaction effect.

(4) For each obtained time–frequency representation of

magnitude difference (for the main effects of con-

gruency and task type, and the interaction effect

respectively), we tested whether and when the

resulting magnitudes within the post-stimulus inter-

val were significantly different from those within

the pre-stimulus interval using a bootstrapping

method (Delorme and Makeig, 2004; Durka et al.,

2004). At each time–frequency point in the post-

stimulus interval, investigated populations and ref-

erence populations were collected from 32 partici-

pants. The null hypothesis was that there was no

mean difference between these two populations.

Then a pseudo-t statistic between the two popula-

tions was calculated, and we estimated the proba-

bility distribution of the pseudo-t statistic from the

reference population by drawing with replacement

two populations of the same size. The permutation

was executed 5000 times. The distribution of the

pseudo-t statistics from the reference population

and the bootstrap p value for the null hypothesis

were then generated.

(5) This procedure yielded time–frequency distribu-

tions, in which the brain responses within the post-

stimulus interval were significantly different from

the reference interval (Hu et al., 2012; Peng et al.,

2012). To address the problem of multiple compar-

isons, the significance level (p value) was corrected

using a false discovery rate (FDR) procedure

(Durka et al., 2004). In addition, to control for

false-positive observations (Maris and Oostenveld,

2007), significant TF-ROIs were defined based on

the criteria that (1) the included time–frequency pix-

els were significantly different from the pre-stimulus

intervals at p< .01; (2) they had to be composed of

more than 75 consecutive significant time points

(300 ms) (Hu et al., 2013); (3) Frequencies below

4 Hz (Delta-band) were not considered for oscilla-

tions at such an extremely low frequency band is

often subject to artifacts due to sweating, move-

ment and electrode drift (He and Raichle, 2009).

After TF-ROIs and S-ROIs were identified, we

calculated the mean magnitude within the TF-ROIs at

corresponding S-ROIs for each condition. The resulting

values were entered into a two-way repeated-measures

ANOVA with the factors of task and congruency. Only

TF-ROIs showing significant modulations of corresponding

effects are reported.

Source localization analysis

We identified a TF-ROI (4–7 Hz, 400–1000 ms) in which

the modulation of the Stroop effect by Task type

occurred (see ‘Results’). We applied low-resolution

electromagnetic tomography analysis (LORETA) to

determine the cortical sources of the TF-ROI (Pascual-

Marqui et al., 1994, 1999; Pascual-Marqui, 1999; Frei

et al., 2001). LORETA solves the ‘‘inverse problem’’ by

finding the smoothest of all solutions with no

assumptions about the number, location, or orientation

of the generators. It uses a three-shell spherical head

model registered to the digitized Montreal Neurological

Institute (MNI305) MRI template. The solution space

corresponds to cortical gray matter of 2394 voxels

sampled at 7-mm resolution.

96 Y. Zhao et al. / Neuroscience 262 (2014) 92–106

We considered LORETA solutions in two time

windows (�600 to �200 ms and 400–1000 ms) for

theta-band (4–7 Hz) and for the four respective

conditions. The resulting LORETA values were

averaged across the respective time window and the

specific frequency window (4–7 Hz). To correspond to

the event-related changes of EEG (ERSP), we

subtracted the averaged baseline LORETA values

(�600 to �200 ms) from the averaged LORETA values

of 400–1000 ms after stimulus. As our major focus was

the interaction effect of Stroop interference by Task

type, we applied the formula [(SI � SN) � (DI � DN)] to

LORETA values to obtain the final LORETA image. The

maximum voxels in the image were reported based on

their MNI coordinates and corresponding Brodmann

areas (BAs).

RESULTS

Behavioral data

Descriptive statistics for both RTs and error rates in the

Stroop task as a function of experimental factors are

shown in Table 1. Only trials on which the subjects were

correct on the WM load task were included in the

analysis of results for the Stroop task, and only trials on

which the subjects were correct on both the WM task

and the Stroop task were included in the analyses of the

Stroop task RTs. Trials were further excluded from

analyses when the Stroop task RTs deviated by 3 or

more SDs from an individual subject’s overall mean RT.

A two-way within-subjects repeated-measures ANOVA

was performed on error rates in the Stroop task as a

function of task type (dual, single) and congruency

(incongruent, neutral). The results revealed a marginally

significant main effect of congruency [F(1, 31) = 3.51,

p= .07, g2 = .10], with more errors in the incongruent

condition. However, the main effect of task type was not

significant [F(1, 31) = 1.01, p= .32, g2 = .03], and

there was no significant interaction between congruency

and task-type factors [F(1, 31) < 1].

For the Stroop task RTs, a two-way within-subjects

repeated-measures ANOVA was performed. Results

revealed a significant main effect of congruency,

[F(1, 31) = 68.95, p< .0001, g2 = .69], showing a classic

Stroop interference effect. The main effect of task was

also significant [F(1, 31) = 58.19, p< .0001, g2 = .65].

However, there was no significant interaction between

the congruency and task-type factors [F(1,31) < 1].

Although the interaction between the congruency and

task-type factors was not significant for RTs, the

interference effect was smaller in the dual-task condition

than that in the single-task condition (see Table 1). On

the considerations that previous studies had shown a

significant positive correlation between the interference

effect and general RTs (Yeung et al., 2004, 2011;

Yeung and Nieuwenhuis, 2009) and that general

slowing accounted for a large part of the enlarged

interference effect observed in adult aging (Salthouse,

1996), we speculated that the Stroop interference effect

may be confounded by a general RT-slowing in the

dual-task condition. If that was the case, when general

RTs were equalized between the two task types, the

estimation of the interaction effect between congruency

and task type should be more accurate than when not

equalized. Based on this, Stroop interference effects

were corrected as proportions of baseline RTs, that is,

(incongruent RT - neutral RT)/neutral RT, separately on

dual- and single-task conditions for each subject (Maylor

and Lavie, 1998). After this, a paired-samples t test

(2-tailed) was carried out for the general RT weighted

Stroop interference effects in the dual- and single-task

conditions. Results revealed the effect of task type on

Stroop interference was marginally significant [t(31)= 2.32, p= 0.06], with a tendency of a smaller

interference effect in the dual-task condition compared

with that in the single-task condition.

EEG data during the Stroop task

The main effect of task type. The modulation of the

main effect of task type (dual, single task) happened

mainly in both the right centro-parietal electrode regions

and frontal electrode regions. The grand-averaged time–

frequency representations of the four different conditions

(DI, DN, SI, and SN) and the difference time–frequency

representation between the dual- and single-task

conditions [(DI + DN) � (SI + SN)] in the right centro-

parietal and frontal electrode regions are illustrated in

Fig. 2A, B, respectively. For the right centro-parietal

electrode regions, a TF-ROI including theta- and alpha-

bands (4–13 Hz in frequency, 800–1100 ms in latency)

that showed the most pronounced task-related effect

was defined (in rectangles in Fig. 2B, p< .05, FDR

corrected). The scalp topographies of ERSP magnitudes

for the DI, DN, SI, and SN conditions and the difference

between the dual-task and single-task conditions

[(DI + DN) � (SI + SN)] within the defined TF-ROI

(4–13 Hz, 800–1100 ms) are illustrated in Fig. 2C. The

mean ERSP magnitudes within the defined TF-ROI for

the four conditions (DI, DN, SI, and SN) were entered

into a two-way within-subjects repeated-measures

ANOVA. The results revealed a significant main effect of

task type, [F(1, 31) = 29.74, p< .001, g2 = .49]. For

the frontal electrode regions, a TF-ROI in the beta band

(13–23 Hz, 400–700 ms) that showed the most

Table 1. Behavioral measures in the single task and dual taska,b

Task Stroop congruency

Incongruent Neutral Incongruent-neutral

Type M SD M SD M SD

Single task

RTs 869 156 805 140 64 51

%E 3 3 2 3 1 3

Dual task

RTs 988 129 928 113 60 50

%E 3 4 3 4 1 4

a Mean correct reaction times (ms) and error rates on the Stroop task as a

function of working memory load and Stroop congruency.b M= mean; E%= error rate, calculated as a percentage; SD= standard

deviation.

Y. Zhao et al. / Neuroscience 262 (2014) 92–106 97

pronounced task-related effect was defined (in rectangles

in Fig. 2B, p< .05, FDR corrected). The scalp

topographies of ERSP magnitudes for the four

conditions (DI, DN, SI, and SN) and the difference

between the dual-task and single-task conditions

[(DI + DN) � (SI + SN)] within the defined TF-ROI

(13–23 Hz, 400–700ms) are illustrated in Fig. 2C. The mean

ERSP magnitudes within the defined TF-ROI of the DI,

DN, SI, and SN conditions were entered into a two-way

within-subject repeated-measures ANOVA. The results

also revealed a significant main effect [F(1, 31) = 16.56,

p< .001, g2 = .35].

The main effect of congruency. The modulation of the

main effect of congruency happened mainly in the centro-

frontal electrode regions. The grand-averaged time–

frequency representations of the four conditions (DI,

DN, SI, and SN) and the difference time–frequency

Fig. 2. The grand-averaged time–frequency representations of the four conditions within the respective defined S-ROIs and corresponding scalp

topographies of magnitudes of the four conditions within the respective defined TF-ROIs. Panel A depicts the grand-averaged time–frequency

representations of the four different conditions (DI, DN, SI, and SN) within the respective defined S-ROIs. Each row corresponds to one S-ROI

corresponding to the largest modulation of the specific effects. From top to bottom: the right centro-parietal electrodes [(FC4 + FC6+ C4+ C6

+ CP4+ CP6)/6] corresponding to the task effect’s modulation; frontal electrodes [(F1 + Fz + F2 + AF3 + AF4)/5] corresponding to the task

effect’s other modulation; centro-frontal electrodes [(F1 + Fz+ F2 + FC1+ FCz + FC2)/6] corresponding to the congruency effect’s modulation

and centro-frontal electrodes [(F1 + Fz + F2 + FC1+ FCz + FC2)/6] corresponding to the interaction effect’s modulation. X-axis, time (ms); Y-axis, frequency (Hz). Panel B depicts the grand-averaged time–frequency representations for the corresponding magnitude difference reflecting

different kinds of effects at different S-ROIs and the results of corresponding bootstrapping statistical analyses at the significant level of p< .01

(FDR corrected). The time–frequency pixels displaying a significant difference from the baseline are colored in black. White rectangles outline the

TF-ROIs corresponding to each specific effect. Panel C depicts the scalp topographies reflecting the ERSP magnitude distributions within the

corresponding TF-ROIs (expressed in ER%) and the difference scalp topographies reflecting the corresponding different kinds of experimental

effects. The time interval for each row of maps changes with the time interval defined by the respective time–frequency ROIs. That is to say, for Task

modulation 1 (the uppermost row), the time interval is 800–1100 ms; for Task modulation 2 (the second row), the time interval is 400–700 ms; for

congruency modulation (the third row), the time interval is 400–1100 ms; for interaction modulation (the last row), the time interval is 400–1000 ms.

White rectangles outline the S-ROIs which show the largest modulation of corresponding effects on the scalp.

98 Y. Zhao et al. / Neuroscience 262 (2014) 92–106

representation between the incongruent and neutral

conditions [(DI + SI) � (DN + SN)] in the centro-frontal

electrode regions are illustrated in Fig. 2A, B,

respectively. A TF-ROI covering theta- and alpha-bands

(5–12 Hz in frequency, 400–1100 ms in latency) that

showed the most pronounced Stroop interference effect

was defined (in rectangles in Fig. 2B, p< .05, FDR

corrected). The scalp topographies of ERSP magnitudes

for the four conditions (DI, DN, SI, and SN) and the

difference between the incongruent and neutral

conditions [(DI + SI) � (DN + SN)] within the defined

TF-ROI (5–12 Hz, 400–1100 ms) are illustrated in

Fig. 2C. Note that a significantly stronger theta- and

alpha-band ERS was observed for the incongruent type

than for neutral type, which resulted in a clear positive

ERSP magnitude for the difference between the

incongruent and the neutral types in the centro-frontal

electrode regions. The mean ERSP magnitudes

(expressed in ER%) within the defined TF-ROI for the

DI, DN, SI, and SN conditions were entered into a two-

way within-subjects repeated-measured ANOVA. The

results revealed a significant main effect of congruency

[F(1, 31) = 39.15, p< .001, g2 = .56].

The interaction effect of congruency by task type. The

modulation of the interaction effect of congruency by task

type happened mainly in the centro-frontal electrode

regions. The difference time–frequency representation

for the interaction effect [(SI � SN � (DI � DN)] in the

centro-frontal electrode regions are illustrated in Fig. 2A,

B. A TF-ROI in the theta band (4–7 Hz in frequency,

400–1000 ms in latency) that showed the most

pronounced interaction effect was defined (in rectangles

in Fig. 2B, p< .05, FDR corrected). The scalp

topographies of ERSP magnitudes for the DI, DN, SI,

and SN conditions and the difference scalp

topographies for the interaction effect [(DI + SI) � (DN

+ SN)] within the defined TF-ROI (4–7 Hz, 400–

1000 ms) are illustrated in Fig. 2C. Note that there was

a stronger theta-band ERS within the centro-frontal

electrode regions for the incongruent condition

compared with the neutral condition in the single-task

condition. However, the theta-band ERS difference

between the incongruent and neutral conditions

disappeared in the dual task. The mean ERSP

magnitudes within the defined TF-ROI for the DI, DN,

SI, and SN conditions were entered into a two-way

within-subject repeated-measures ANOVA. The results

revealed a significant interaction between congruency

and task type, [F(1, 31) = 9.33, p= .005, g2 = .23].

Post hoc tests revealed that for the single-task

condition, theta-band ERS was significantly higher in the

incongruent type than that in the neutral type

(p< .001). However, for the dual-task condition, there

was not a significant difference between the incongruent

and neutral types (p= .29, Fig. 3A).

The validity of the reduced interference in theta bandERSP in the dual-task condition. To validate that the

reduced interference in theta rhythm observed in the

dual-task condition was robust, we have listed theta band

ERSP (4–7 Hz) under each condition for all participants

in Table 2 (Michels et al., 2008). As can be seen from

Table 2, for theta rhythm ERSP, the Stroop interference

effect for the single-task condition was robust, with 27

out of 32 participants showing larger or equal ERSP

values in the incongruent condition. The interference

effect in the dual-task condition was mixed across

participants, with nearly half of the participants (14 out of

32) showing the opposite modulation effect compared

with rest of the participants. The interaction effect

between Stroop congruency and Task was robust, with

23 out of 32 participants showing that the interference

effect in the dual-task condition was smaller than in the

single-task condition. As a contrast, we have also listed

alpha band ERSP (8–12 Hz) under each condition for all

participants in Table 3. As can be seen from Table 3, for

alpha rhythm ERSP, the Stroop interference effect for

Fig. 3. The effect of task type and congruency on theta-band ERSP (expressed in ER%) and the correlation between the interaction effect of

congruency by WM load expressed in theta-band ERSP and behavioral RTs. Panel A depicts the mean theta-band ERSP magnitudes at the specific

TF-ROI (4–7 Hz, 400–1000 ms) and S-ROI (frontal-central electrodes) in the four conditions (DI, DN, SI and SN). The congruency effect in the dual-

task condition was significantly smaller than that in the single-task condition. Panel B displays a scatter plot of the effect of WM on the interference

effect represented in RTs (X-axis) versus that represented in theta-band ERSP (Y-axis). A positive, linear relationship was evident and the

corresponding fitting line is shown (slope: .0015, p< .005).

Y. Zhao et al. / Neuroscience 262 (2014) 92–106 99

each task condition (dual/single) was robust, with 27 out of

32 participants showing larger ERSP values in the

incongruent condition of the dual task, and 26 out of 32

participants showing larger ERSP values in the

incongruent condition of the single task. However, for the

interaction effect between Stroop congruency and Task,

exactly half of the participants showed the opposite

modulation effect to the rest of the participants. So it can

be concluded that in contrast to the theta band ERSP,

the alpha-related ERSP effects were simply mixed

(negative and positive) across participants. This resulted

in the overall diminished interaction effect between

Congruency and Task for the alpha rhythm.

Correlational analysis. Based on the results that

oscillations in the theta-band were related to the

interaction effect of congruency by task type, to further

validate the behavioral implications of theta-band

oscillations, we calculated the Stroop interference effect

(incongruent minus neutral) for every participant both on

RTs and on ERSP magnitude in the dual-task condition

and the single-task condition, respectively. By

subtracting the congruency effect in the dual-task

condition from that in the single-task condition

[(SI � SN) � (DI � DN)], we further calculated the

congruency effect difference between the single-task

and dual-task conditions. With these values, the

Spearman correlation coefficient between the behavioral

indices of congruency effect difference and the

corresponding ERS/ERD indices was calculated, and

the resulting correlation coefficient was tested against a

null hypothesis of no correlation at p< .05 (two tailed).

The result revealed that RTs[(SI�SN)�(DI�DN)] in the

behavior level was significantly positively correlated with

(SI � SN) � (DI � DN) in the EEG level as expressed in

theta-band ERSP magnitude (r= .36, p< .05). When

three outliers (maximum/minimum RT values and the

point with the largest residual error) were removed

(Glasauer et al., 2003), the correlation coefficient

increased and became extremely significant (r= .57,

p< .002, Fig. 3B. Fitting line: slope: .0015, p< .005).

Source localization analysis. As shown in Fig. 5, the

interaction effect of Stroop interference by Task type

was mainly located at the left middle frontal gyrus (BA

6; MNI coordinates: �24, �11, 64, Fig. 5).

Table 2. Theta rhythm (4–7 Hz) ERSP valuesa,b

Participant Dual (I � N)Dual Single (I � N)Single Interaction

I N I N

1 0.29 0.12 0.17 0.28 0.12 0.16 0.01

2 0.13 0.17 �0.05 0.11 0.04 0.06 �0.11

3 0.10 0.07 0.02 0.11 0.02 0.09 �0.07

4 0.21 0.31 �0.10 0.27 0.20 0.07 �0.17

5 0.11 0.08 0.03 0.15 0.10 0.06 �0.02

6 0.11 0.16 �0.05 0.30 0.19 0.11 �0.16

7 0.00 0.03 �0.03 �0.01 �0.04 0.04 �0.07

8 0.43 0.34 0.10 0.23 0.03 0.20 �0.10

9 0.30 0.14 0.16 0.27 0.16 0.11 0.06

10 0.22 0.29 �0.07 0.24 0.09 0.16 �0.22

11 0.06 0.12 �0.07 0.03 0.03 0.00 �0.07

12 0.42 0.31 0.11 0.34 0.13 0.22 �0.10

13 0.12 0.13 �0.01 0.18 0.09 0.09 �0.09

14 0.23 0.13 0.10 0.25 0.17 0.08 0.02

15 0.09 0.14 �0.05 0.37 0.16 0.21 �0.26

16 0.22 0.02 0.19 0.12 0.09 0.02 0.17

17 0.07 0.19 �0.12 0.07 0.06 0.01 �0.13

18 0.18 0.14 0.04 0.11 �0.17 0.28 �0.25

19 0.19 0.13 0.06 0.28 0.08 0.19 �0.13

20 0.17 0.28 �0.11 0.23 0.14 0.09 �0.20

21 0.10 0.14 �0.05 0.19 0.03 0.15 �0.20

22 0.22 0.25 �0.04 0.22 0.11 0.11 �0.15

23 0.07 0.05 0.02 0.23 0.28 �0.05 0.07

24 0.17 0.25 �0.08 0.07 �0.13 0.21 �0.28

25 0.05 �0.03 0.08 0.03 0.14 �0.11 0.19

26 0.14 0.12 0.02 0.06 0.13 �0.07 0.08

27 0.24 0.19 0.06 0.15 0.06 0.09 �0.04

28 0.17 0.05 0.12 0.19 0.19 0.00 0.12

29 0.29 0.22 0.07 0.26 0.36 �0.10 0.17

30 0.04 0.16 �0.11 0.09 0.09 �0.01 �0.11

31 0.17 0.08 0.08 0.31 0.14 0.18 �0.10

32 0.21 0.19 0.02 0.45 0.36 0.09 �0.07

Mean 0.17 0.16 0.02 0.19 0.11 0.09 �0.07

a Individual theta-band ERSP values for incongruent and neutral trials in the single/dual-task conditions.b I = incongruent; N = neutral.

100 Y. Zhao et al. / Neuroscience 262 (2014) 92–106

DISCUSSION

Previous studies found that concurrent WM tasks can

increase or decrease interference effects as evidenced

in behavioral RT data in various cognitive control tasks

(de Fockert et al., 2001; Kim et al., 2005; Park et al.,

2007; Gil-Gomez de Liano et al., 2010; Zhao et al.,

2010; Ahmed and de Fockert, 2012). In the present

study, we validated that a concurrent visual short-term

WM task could decrease the interference effect by using

the Chinese Stroop task with hand-drawn lines to be

maintained in visual WM. Behaviorally, we observed the

Stroop interference effect tended to decrease with the

concurrent WM task. Time–frequency analysis revealed

that after Chinese Stroop stimuli onset, the concurrent

WM task specifically modulated the congruency effect in

the theta-band ERSP magnitude, while having no effect

on the congruency effect in the alpha-band ERSP

magnitude. Further, the modulation of the Stroop

interference effect by concurrent WM expressed in

theta-band ERSP was correlated with behavioral RTs,

and source localization identified the modulation can be

mainly explained with the source located in the middle

frontal gyrus.

Dovetailing with general assumptions of multiple

resource theory (Navon and Gopher, 1979; Egner et al.,

2007; Egner, 2008), the interference effect on RTs of

the Chinese Stroop task tended to decrease when

performed concurrently with a visual short-term WM

task. The Chinese Stroop task differs from the English

Stroop task because Chinese characters rely heavily on

a specific visual system dedicated to form/shape

processing (Van Orden et al., 1988; Plaut et al., 1996;

Zhou and Marslen-Wilson, 1996, 2000), whereas color

processing relies on another comparatively independent

system in which only color information can be stored

and manipulated (Wheeler and Treisman, 2002; Kim

et al., 2005; Olsson and Poom, 2005). Thus the

concurrent visual short-term WM maintenance demands

(imitative structures as Chinese characters) occupied

the resources for Chinese characters processing, which

in their existing configuration facilitated the performance

of the Stroop task. Although a few studies have

observed the same kinds of WM facilitation effect

Table 3. Alpha rhythm (8–12 Hz) ERSP valuesa,b

Participant Dual (I � N)Dual Single (I � N)Single Interaction

I N I N

1 0.32 0.08 0.25 0.46 0.25 0.21 0.04

2 0.20 0.26 �0.06 0.14 0.02 0.12 �0.18

3 0.06 0.01 0.05 �0.09 �0.11 0.02 0.02

4 0.21 0.18 0.03 0.15 0.17 �0.02 0.06

5 0.14 0.12 0.03 0.14 0.13 0.01 0.02

6 0.22 0.10 0.12 0.22 0.08 0.13 �0.02

7 0.02 �0.06 0.08 �0.01 �0.11 0.10 �0.02

8 0.22 0.12 0.10 0.07 0.01 0.07 0.04

9 0.19 0.03 0.17 0.16 0.11 0.05 0.12

10 0.00 0.03 �0.03 �0.06 �0.08 0.02 �0.05

11 0.07 �0.05 0.12 �0.03 �0.07 0.04 0.08

12 0.30 0.22 0.07 0.19 0.10 0.09 �0.01

13 0.30 0.23 0.06 0.16 0.14 0.01 0.05

14 0.26 0.07 0.18 0.25 0.12 0.13 0.06

15 0.16 0.05 0.10 0.26 0.01 0.24 �0.14

16 0.27 0.08 0.19 0.06 �0.01 0.07 0.12

17 0.21 0.36 �0.15 �0.06 �0.05 �0.01 �0.15

18 0.01 0.00 0.02 0.02 �0.10 0.11 �0.10

19 0.43 0.15 0.28 0.21 0.10 0.11 0.17

20 0.04 �0.06 0.10 �0.12 �0.08 �0.04 0.14

21 0.06 0.05 0.01 0.12 0.06 0.06 �0.05

22 0.13 0.15 �0.02 0.15 0.08 0.08 �0.10

23 �0.01 0.03 �0.04 0.05 0.08 �0.02 �0.01

24 0.43 0.33 0.10 0.31 0.06 0.26 �0.15

25 �0.05 �0.12 0.07 �0.03 �0.08 0.05 0.03

26 0.10 0.05 0.05 0.08 �0.02 0.11 �0.06

27 0.40 0.07 0.34 0.26 0.15 0.11 0.23

28 0.13 0.01 0.02 0.26 0.07 0.19 �0.16

29 0.11 0.06 0.05 �0.09 �0.04 �0.05 0.10

30 �0.06 �0.09 0.03 0.11 0.03 0.08 �0.05

31 0.19 0.00 0.19 0.05 0.13 �0.07 0.26

32 0.13 0.06 0.06 0.30 0.19 0.11 �0.05

Mean 0.16 0.08 0.08 0.12 0.04 0.07 0.01

a Individual alpha-band ERSP values for incongruent and neutral trials in the single/dual-task conditions.b I = incongruent; N = neutral.

Y. Zhao et al. / Neuroscience 262 (2014) 92–106 101

(e.g., Kim et al., 2005; Park et al., 2007; Zhao et al.,

2010), our research is at present the first report to have

directly observed this effect with typical Stroop tasks.

However, the modulation effect in RTs was still rather

small. Only after being corrected for the interference

effect as proportions of baseline RTs following Maylar

and Lavie’s routine did the modulation effect approach

significance (p= .06) (Maylor and Lavie, 1998). This

suggests that the effect size of modulation on the typical

Stroop interference effect by WM may be genuinely

small. This may be why previous publications have

never reported such an effect.

In relation to EEG results concerning the Stroop task,

paralleling the Hanslmayr et al. (2008) study, both theta-

& alpha-band ERSP increased significantly in the

incongruent condition compared with the neutral

condition in the single Stroop task. Stronger theta and

alpha-band ERSP magnitude emerged in a relatively late

period, extending from 400 to 1100 ms, implying their

potential involvement in a late response interference

resolution period. In accordance with this, theta-band

activity has been linked to multiple cognitive processes

including memory, attention, learning, performance

monitoring, and action selection (Bas�ar-Eroglu et al.,

1992; Wu et al., 2007; Cavanagh et al., 2012; Tang et al.,

2013). It was thus reasonable to conclude that theta

dynamics in the centro-frontal electrode regions reflected

a non-specific brain process such as the activation of

central executive processes which was important for WM,

attention, and cognitive control (Hanslmayr et al., 2008;

Cavanagh et al., 2012). On the other hand, recent studies

suggest that alpha rhythm is more likely to reflect cortical

inhibition in situations where participants have to inhibit

task-irrelevant information intake such as in mental

calculations, WM tasks or in situations where a response

must be withheld (Klimesch et al., 2007; Palva and

Palva, 2007). The increased fronto-central alpha-band

oscillations in the incongruent condition in our study were

consistent with the alpha rhythm’s inhibition hypothesis,

no matter whether explained in terms of more focused

attention (less task-irrelevant information intake) or

response inhibition.

However, when participants performed the Stroop

task with concurrent WM demands, the difference

between the incongruent and neutral condition in theta-

band ERSP magnitude reduced and this reduction was

robust, as shown in Fig. 4 and Table 2. Furthermore,

such a reduction was in accordance with the reduced

Stroop interference effect in the dual-task condition. The

congruency effect difference [(SI � SN) � (DI � DN)]

represented in theta-band ERSP magnitude and its

counterpart in behavioral RTs were positively correlated,

suggesting that theta-band ERSP magnitude was

modulated in the same way as behavioral RTs by

concurrent WM demands. Thus the reduced theta-band

ERSP difference may be the neural correlate of the

reduced Stroop interference. As visual WM demands

drained away the resources for Chinese character

(distractor) processing, the interference effect reduced

and there was no need for participants to actively

maintain the Stroop target and ignore the distractor in

the incongruent condition, thus theta-band oscillations,

which reflected the central process of active control,

reduced compared with the single Stroop task condition.

Source localization results using the LORETA method

suggest that current density activity in the dorsolateral

PFC (the left middle frontal gyrus) can explain the

reduced theta-band ERSP difference in the dual-task

condition. In line with the aforementioned theta-band’s

function in the central process of active control, the

middle frontal gyrus has been documented to be

involved in the cognitive control processes (Milham

et al., 2001; Kerns et al., 2004) and many researchers

suggest it is further involved in WM processes

(McCarthy et al., 1994; Cohen et al., 1997; Courtney

et al., 1997; Markov et al., 2010), which, in turn,

validated the overlap of cognitive control and WM.

In contrast to the reduced incongruent-neutral

difference in theta-band ERSP under the dual-task

condition, the alpha-band ERSP effect, on the other

hand, remained stable under the concurrent WM

demands. The alpha-band’s dissociation with theta-band

rhythm suggests that although both frequency bands’

ERSP increased in the incongruent condition of the

Fig. 4. Box plots of theta-band ERSP magnitude results plus original data. This figure shows the box plots of theta-band ERSP magnitudes at the

specific TF-ROI (4–7 Hz, 400–1000 ms) and S-ROI (frontal-central electrodes) in the four conditions (DI, DN, SI and SN). Box plots show the

median (central horizontal line), interquartile range (the 25th to the 75th percentile [box]), and ‘‘whiskers’’ (whiskers represent the smallest and

largest points inside 1.5 times the interquartile range, which respectively spread out from the first and third quartiles). Original data points are shown

beside the box plots. Note the data points in a specific condition are randomly jittered to make it easier to see individual values.

102 Y. Zhao et al. / Neuroscience 262 (2014) 92–106

single Stroop task, their functional relevance may be

different. Theta-band rhythm, as stated above, is

generally considered to be involved in central executive

processes. The role of alpha rhythm, on the other hand,

is far from conclusive. While many researchers in recent

years have realized occipital alpha rhythm plays an

important role in sensory suppression in selective

attention (for a review, see Foxe and Snyder, 2011), a

few researchers have also reported the involvement of

frontal alpha in cognitive/attentional control (Hanslmayr

et al., 2008; Mathewson et al., 2011; Tang et al., 2013).

Thus the function of alpha rhythm may be different with

respect to the scalp regions and specific tasks at hand.

Referring to the current research, the Stroop interference

and theta-band incongruent/neutral difference reduction

in the dual-task condition indicates the Stroop

interference between target colors and distractive

characters had reduced, due to the concurrent WM

demands draining away the specific resources for

character processing. So the only difference between

incongruent and neutral trials in the dual condition was

the residual interference, as can be seen in behavioral

RTs, where RTs for incongruent trials were still longer

than those for neutral trials. Thus the larger frontal

alpha ERSP amplitude in the incongruent trials cannot

be interpreted with other factors, except in terms of

cognitive control. In the same line of thought, the

dissociation between alpha and theta rhythms may not

necessarily imply the two rhythms’ functional separation,

but in fact implies that while both rhythms play a role in

cognitive control, their sensitivity to the perturbation of

cognitive control may be different. It is still quite possible

that the functional roles of frontal alpha and frontal theta

activities would be different in the microscale. Future

research making use of electrocorticograms or single-

cell recording would be needed to illustrate this further

(Jacobs and Kahana, 2010).

Despite its possible implications, this research has

three limitations that serve as foundations for further

work. First, the current experimental design may not be

so optimal studying the effect of concurrent WM on

cognitive control. A more traditional and popular way to

manipulate the WM factor is to set a low-load control

condition in which participants just need to remember

one object (de Fockert et al., 2001). Thus any difference

between the low-load and high-load condition can be

more confidently attributed to WM per se. Our current

design cannot exclude the potential explanation that the

possible visual encoding period before the Stroop stimuli

in the dual-task condition caused all of the effect that we

observed, although the paradigms we adopted here

have also been previously used for the study of WM

effects (Kim et al., 2005; Park et al., 2007; Gil-Gomez

de Liano et al., 2010; Zhao et al., 2010; Spronk and

Jonkman, 2012). To identify whether we successfully

manipulated the WM factor, we conducted another

supplementary behavioral experiment which was nearly

the same as the original design except that an

additional ‘‘simultaneous presentation condition’’ was

added, in which four structures were presented

simultaneously (similar to a flanker task) with the Stroop

task-related stimulus. We believed that if the visual

encoding period plays a role, we would not observe any

difference between the ‘‘simultaneous presentation

condition’’ and the original ‘‘dual-task condition’’. The

results showed the opposite. Briefly, with four structures

presented simultaneously with the Stroop stimulus, the

interference effect of the Stroop task tended to increase,

rather than decrease, compared with the single Stroop

task (Fig. 6). Thus, we believe our design, although not

perfect, was sufficient to successfully manipulate WM

per se. Future researchers could use a high/low load

contrast to perfect this study.

Second, the selection of S-ROI and TF-ROI, although

following some objective criteria (see ‘Experimental

procedures’) which had been used in previous

publications (Hu et al., 2013; Tang et al., 2013), was still

to some extent arbitrary. Future researchers could use

more objective cluster-based TF-ROI defining methods

(Zhang et al., 2012).

Third, we used LORETA to localize the sources of

observed scalp EEG activity. Although source

localization has proved to be as accurate in some

conditions as MRI or positron emission tomography

(Brodbeck et al., 2011), it is still necessary to exercise a

degree of caution when trying to interpret the sources

Fig. 5. The difference LORETA map for the Stroop congruency and Task type interaction effect. This figure shows the difference LORETA map for

the Stroop congruency and Task type interaction effect [(Single & Incongruent – Single & Neutral) – (Dual & Incongruent – Dual & Neutral)]. The

maximum activation was located at the left middle frontal gyrus (BA 6, MNI coordinates: �24, �11, 64).

Y. Zhao et al. / Neuroscience 262 (2014) 92–106 103

obtained, as the inverse problem is an inherent hard nut in

source localization. Future researchers could use high-

density electrode scalps (>100) and a realistic head

model to better their source localization results

(Brodbeck et al., 2011).

CONCLUSION

In sum, with a typical Chinese version of the Stroop task,

we observed that concurrent visual short-term WM

demands decrease the Stroop interference effect in RTs

and theta-band ERSP, supporting the multiple resources

theory of WM. The different modulation effects of WM

on theta and frontal alpha rhythm further suggests that

although frontal alpha rhythm is also involved in the

Stroop task, it functions in a way more robust to

perturbations of cognitive control.

Acknowledgments—This work was supported by the National

Natural Science Foundation of China (31170980, 81271477),

the Foundation for the Author of National Excellent Doctoral Dis-

sertation of PR China (201107), the New Century Excellent Tal-

ents in University (NCET-11-0698), and the Fundamental

Research Funds for the Central Universities (SWU1009001).

We thank Wei-Wei Peng for her generous suggestions in data

analysis and Yi-fan Li for his patient help in instructing the elec-

tronic artwork.

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(Accepted 24 December 2013)(Available online 7 January 2014)

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