ISSN 0033-2941DOI 10.2466/18.PR0.115c31z8
© Psychological Reports 2014
Psychological Reports: Disability & Trauma
EXAMINATION OF NEURAL SYSTEMS SUB-SERVING
FACEBOOK “ADDICTION”1, 2
California State University, Fullerton
Brain and Creativity Institute, University of Southern California
Faculty of Psychology, Southwest University
Beibei, Chongqing, China
National Key Laboratory of Cognitive
Neuroscience and Learning, Beijing Normal
Brain and Creativity Institute, University of Southern California
Brain and Creativity Institute, University of
Department of Psychology and Brain and
Creativity Institute, University of Southern
Summary.—Because addictive behaviors typically result from violated homeo-
stasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain
systems, this study examined whether these systems sub-serve a specic case of
technology-related addiction, namely Facebook “addiction.” Using a go/no-go par-
adigm in functional MRI settings, the study examined how these brain systems in
20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18–23) who completed a Face-
book addiction questionnaire, responded to Facebook and less potent (trac sign)
stimuli. The ndings indicated that at least at the examined levels of addiction-like
symptoms, technology-related “addictions” share some neural features with sub-
stance and gambling addictions, but more importantly they also dier from such
addictions in their brain etiology and possibly pathogenesis, as related to abnormal
functioning of the inhibitory-control brain system.
While the Internet is largely benecial to society, it can also bring
about negative consequences (D'Arcy, Gupta, Tarafdar, & Turel, 2014),
including behavioral and psychological signs that have been labeled by
some researchers as “addiction”3 to the use of specic applications on the
Internet (Griths, 1998; Young, 1998a; Griths, 1999; Young, 2004; Turel,
2014, 115, 3, 675-695.
1Address correspondence to Or Turel, 800 N. State College Blvd., Fullerton, CA 92834 or
e-mail (email@example.com or firstname.lastname@example.org).
2This research was supported by research grants from the National Institute on Drug Abuse
(NIDA) R01DA023051, National Cancer Institute (NCI) R01CA152062, and the National
Heart, Lung, and Blood Institute and the National Institute of Child Health and Human
Development (U01HL097839). The authors would also like to thank Alexandra Hollihan and
Stephanie Castillo, who helped with the data collection.
3The authors use quotation marks to indicate that the appropriateness of the term “addiction”
to the described cases is still being examined.
04_PR_Turel_140140.indd 675 16/12/14 10:58 AM
O. TUREL, ET AL.
Serenko, & Giles, 2011). Such “addictions” can result in academic failure,
sleep deprivation, social isolation, health issues, and many other impair-
ments for adolescents and young adults; they also result in reduced work
performance and marital discord and separation for adults (cf. Griths,
1995; Young, 1998b; Pratarelli, Browne, & Johnson, 1999; Chou, Condron,
& Belland, 2005; Block, 2008; Byun, Runi, Mills, Douglas, Niang, Step-
chenkova, et al., 2009; Young, 2010; Kuss, Griths, & Binder, 2013). It is
therefore worthwhile to examine the possible neural basis of such “addic-
Research across multiple countries, including the United States, es-
timates the prevalence of such “addictions” to be between 0.7% and 11%
(Greeneld, 1999; Johansson & Götestam, 2004; Kim, Ryu, Chon, Yeun,
Choi, Seo, et al., 2006; Cao & Su, 2007; Rendi, Szabo, & Szabó, 2007; Ghas-
semzadeh, Shahraray, & Moradi, 2008; Park, Kim, & Cho, 2008; Shaw &
Black, 2008; Siomos, Dafouli, Braimiotis, Mouzas, & Angelopoulos, 2008;
Bakken, Wenzel, Götestam, Johansson, & Oeren, 2009),4 and that it is more
prevalent among youth and young adults (Kuss, et al., 2013), presumably
because the inhibitory system of such individuals develops more slowly
than their impulsive system (Casey, Giedd, & Thomas, 2000; Casey, Tot-
tenham, Liston, & Durston, 2005; Steinberg, 2005; Casey, Getz, & Galvan,
2008; Steinberg, 2008; Steinberg, Graham, O'Brien, Woolard, Cauman, &
Banich, 2009). Given the symptoms and prevalence of this phenomenon,
calls have been issued to study its possible neurological roots (Block, 2008)
and to focus on “addiction” to specic, intrinsically rewarding applica-
tions on the Internet (e.g., Facebook, videogames) (Yellowlees & Marks,
2007). Consequently, the concept of “Internet Gaming Disorder” was in-
cluded in the Appendix (section 3, potential disorders requiring further
research) of the DSM–V, and it is possible that more application-specic
“addictions” will be considered for inclusion in future versions of the
DSM. Moreover, several scales for measuring such “addictions” have been
developed (van Rooij, Schoenmakers, Vermulst, van den Eijnden, & van
de Mheen, 2011; Andreassen, Torsheim, Brunborg, & Pallesen, 2012). Nev-
ertheless, the DSM is not conclusive on the existence of this possible dis-
order, and many researchers also still question whether the observed phe-
nomenon reects a pathological “addictive” state or merely a “bad habit,”
especially when applied to the vast general population of users who show
addiction-like symptoms in relation to Internet application use (Griths,
1998, 1999; LaRose, 2010; Bergmark, Bergmark, & Findahl, 2011).
4This is a wide range and it can be assumed that it is a consequence of multiple factors,
including the type of Internet application examined (e.g., Facebook, videogames), demo-
graphics and socio-economic dierences between the samples, and national dierences in
accessibility to technologies and the availability of alternative activities.
04_PR_Turel_140140.indd 676 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 677
This study attempts to address one aspect of this issue by focusing
on a possibly “addictive” Internet technology, namely Facebook. Several
studies have demonstrated that Facebook “addiction” is a plausible phe-
nomenon and that addiction-like symptoms in relation to Facebook use
may be prevalent in the general population (Echeburua & de Corral, 2010;
Karaiskos, Tzavellas, Balta, & Paparrigopoulos, 2010; Kuss & Griths,
2011; Griths, 2012). These behaviors are usually labeled as “addictive”
based on DSM criteria for dependence on substances, including tolerance,
withdrawal, and loss of control to the point that the behavior causes a sig-
nicant impairment to the individual (World Health Organization, 1992;
American Psychiatric Association, 2000). Perhaps many Facebook users
may be labeled as “addicts” simply because they easily meet several of
these criteria, especially when the denition of “signicant impairment” is
subjective and variable. The following research question is therefore posed:
Research question 1. Does Facebook “addiction” constitute a path-
ological problem similar to those observed in the case of other
substance and behavioral addictions, in the general user pop-
One objective way to identify similarities or fundamental dierences
between Facebook (Internet) and other addictions is to look at the neu-
ral systems sub-serving these possible disorders. Thus, one goal of this
study was to examine neural activities in two key brain systems impli-
cated in substance addiction, the impulsive, amygdala-striatal system
and the reective-inhibitory prefrontal brain system (e.g., Jentsch & Tay-
lor, 1999; Volkow & Fowler, 2000; Arnsten & Li, 2005; Bickel, Miller, Yi,
Kowal, Lindquist, & Pitcock, 2007) when Facebook users are exposed to
Facebook cues. The amygdala-striatal (mesolimbic dopamine-dependent)
neural system is critical for the incentive motivational eects of a variety
of rewards (Stewart, Dewit, & Eikelboom, 1984; Robbins, Cador, Taylor, &
Everitt, 1989; Wise & Rompre, 1989; Robinson & Berridge, 1993; Di Chiara,
Tanda, Bassareo, Pontieri, Acquas, Fenu, et al., 1999; Everitt, Parkinson,
Olmstead, Arroyo, Robledo, & Robbins, 1999; Balleine & Dickinson, 2000;
Koob & Le Moal, 2001). It becomes hyperactive and begins to intensify the
incentive value of rewards in individuals with substance abuse problems
(Bechara, 2005). As cue-behavior-reward associations are strengthened,
they begin to drive behavior without the necessary involvement of con-
scious processes (Everitt, et al., 1999; Robinson & Berridge, 2003; Everitt &
Robbins, 2005). Because Facebook use can provide strong rewards (Turel
& Serenko, 2012; Meshi, Morawetz, & Heekeren, 2013),5 it is expected that
5The terms “rewards” and “incentives” are used interchangeably. However, please note that
rewards are one form of incentives, and other incentives can include avoiding negative con-
04_PR_Turel_140140.indd 677 16/12/14 10:58 AM
O. TUREL, ET AL.
similar learning mechanisms take place with Facebook use, which can
lead to “addiction”-like symptoms.
Hypothesis 1. If Facebook “addiction” is sub-served by similar
pathological issues underlying other addictions, the impul-
sive amygdala-striatal system activity in response to Face-
book stimuli will be positively correlated with “addiction”-
While the amygdala-striatal system provides the drive for impul-
sive behaviors, diagnosis of addictions typically also requires poor con-
trol abilities that fail to inhibit impulsive behaviors and the consideration
of long-term goals (Noel, Brevers, & Bechara, 2013). This inhibitory sys-
tem depends primarily on the functions of the prefrontal cortex (Fellows,
2004; Wheeler & Fellows, 2008). A critical neural region in this system is
the ventromedial prefrontal cortex (which is considered inclusive of the
medial orbitofrontal cortex; Bechara, Tranel, & Damasio, 2000). Other im-
portant regions in the inhibitory system include the lateral orbitofrontal
and inferior frontal gyrus regions, as well as the anterior cingulate cortex,
which are involved in a variety of simple inhibitory processes (Glascher,
Adolphs, Damasio, Bechara, Rudrauf, Calamia, et al., 2012). Good inhibi-
tory functioning reects the ability to actively stop a pre-potent behav-
ioral response after it has been triggered (Logan, Schachar, & Tannock,
1997; Braver & Ruge, 2006). Inhibitory processes are activated primarily
by antecedent cues (e.g., Wood & Neal, 2007), and inhibition is therefore
especially relevant in the face of these cues. Individuals with hypoactivity
of these systems have a tendency to act more impulsively (Bechara, 2005).
Hypothesis 2. If Facebook “addiction” is sub-served by similar
pathological issues underlying other addictions, the reective-
inhibitory prefrontal system activity in response to Facebook
stimuli will be negatively correlated with “addiction”-like
The sample was recruited in two phases. The rst involved an on-
line questionnaire which captured demographic, exclusion criteria (self-
reported neurological or psychiatric history as well as uncorrected vision),
and “addiction” variables. It was administered to 45 Facebook users who
were recruited using an announcement on a bulletin board at a North
American university, and who were given a small gift card in exchange
for their time. None of the users who completed this questionnaire met
any of the exclusion criteria. In the second phase, twenty participants who
04_PR_Turel_140140.indd 678 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 679
completed the screening survey were invited and agreed to participate in
the fMRI scan. The selection was made such that the sexes are balanced,
and that there is sucient variability in addiction scores. The sample was
equally distributed between men and women, and the average age of the
participants was 20.3 yr. (SD = 1.30, range = 18–23). The participants had
normal or corrected-to-normal vision, and were free of neurological or
psychiatric history (self-reported). All participants gave informed consent
to the experimental procedure, which was approved by the University of
Southern California Institutional Review Board.
Participants were asked to complete an online version of the Facebook
“addiction” scale (adapted from van Rooij, et al., 2011). This scale asked
them to report the frequency (1: Never, 5: Very Often) of typical Facebook
“addiction” symptoms such as withdrawal, salience, relapse, loss of con-
trol, and conict. It therefore presumably captures the “level of ‘addic-
tion’” and was valid and reliable (α = .92, Spearman-Brown Coecient for
split-half reliability = 0.91, Guttman split-half coecient = 0.91, Average
Variance Extracted (AVE) = 0.53, and composite reliability = 0.91). Given
its validity and reliability, the mean of all items was calculated, which
represents the average severity of the addiction symptoms per individ-
ual. The mean score was 2.20 (SD = 0.72, range = 1.07–3.64), and the scores
seemed to be reasonably normally distributed (skewness = 0.32, SE = 0.51;
kurtosis = –0.59, SE = 0.99); the Kolmogorov-Smirnov test (statistic = 0.097,
df = 20, p = .20) and the Shapiro-Wilks test (statistic = 0.97, df = 20, p = .81)
were non-signicant. Hence, no transformations to normality were ap-
plied. The behavioral questionnaire also captured age, sex, mental history,
and exclusion criteria (e.g., non-corrected bad vision and any peripheral
neuropathies). No mental issues, including drug and alcohol abuse were
reported by the sample; no participant met any of the exclusion criteria.
fMRI Procedures and Tasks
fMRI scans were performed one week after the completion of the be-
havioral questionnaire. In these scans, participants rested in the supine
position on the fMRI scanner bed to view the task back-projected onto a
screen through a mirror attached to the head coil. Foam pads were used
to minimize head motion. Stimulus presentation and timing of all stimuli
and response events were achieved using Matlab (Mathworks) and Psych-
toolbox (www.psychtoolbox.org) on an IBM-compatible PC. The partici-
pants' responses were collected online using an MRI-compatible button
The participants performed two Facebook-specic go/no-go tasks while
in the scanner: (1) a sign go/Facebook no-go task (SGo task) in which they
04_PR_Turel_140140.indd 679 16/12/14 10:58 AM
O. TUREL, ET AL.
were asked to press a button when they saw a trac sign image, and refrain
from pressing the button when they saw a Facebook-related image; and (2) a
Facebook go/sign no-go task (FGo task) in which they were asked to press a
button when they saw a Facebook-related image, and refrain from pressing
the button when they saw a trac sign image. This go/no-go paradigm al-
lows examination of both the brain responses to Facebook stimuli and the in-
hibition of pre-potent responses to Facebook stimuli. Examples of stimuli are
shown in Fig. 1. Trac signs included common (excluding red) signs.
Each task consisted of 120 go trials (75%) and 40 no-go trials (25%).
No-go trials were presented in pseudo-randomized order, designed so
that no-go trials appeared with equal probability after 1–5 consecutive go
trials, and no two no-go trials appeared consecutively. Each stimulus was
presented for 500 msec., followed by a xation cross for 1.5–4 sec. with a
mean of 2.5 sec. The sequence was optimized for design eciency using
an in-house program. Each task ran for 8 min. The order of the two ver-
sions of go/no-go tasks was counterbalanced across participants.
Following signal detection theory, the hit rate, false alarm rate, sensi-
Hitsrate fals ealaramrate,
and decision bias
05.Hits rate falsealaramrate
Fig. 1. The illustration of the event-related Facebook-specic go/no-go tasks: (1) sign
go/Facebook no-go task (SGo task), and (2) Facebook go/sign no-go task (FGo task). Partici-
pants were asked to press a button as soon as possible in the go trials (trac sign pictures in
SGo task and Facebook-related pictures in FGo task) and withhold the response in the no-go
trials (Facebook-related pictures in SGo task and trac sign pictures in FGo task). The order
of tasks was counterbalanced across subjects and across sessions.
04_PR_Turel_140140.indd 680 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 681
were calculated for each task (Macmillan & Creelman, 1996). The mean
reaction time for go trials and no-go trials (false alarm trials only) for
each task were also calculated. The reaction time for go trials served as an
index for habitual-impulsive responding to the stimuli, with longer reac-
tion times indicating less habitual response, while decision bias C served
as an index of response inhibition, with higher values indicating better in-
Functional MRI (fMRI) imaging was conducted in a 3T Siemens
MAGNETOM Tim/Trio scanner. Functional scanning used a z-shim gra-
dient echo EPI sequence with PACE. This sequence was aimed at reduc-
ing signal loss in the prefrontal and orbitofrontal areas. The parameters
were: TR/TE = 2000/25 msec.; ip angle = 90°; 64 × 64 matrix size with res-
olution 3 × 3 mm2. Thirty-one 3.5-mm axial slices were used to cover the
whole cerebral cortex and most of the cerebellum with no gap. The slices
were tilted about 30° clockwise along the AC-PC plane to obtain better
signals in the orbitofrontal cortex. An anatomical T1-weighted structural
scan was also done (TR/TE = 1950/2.26 msec.; ip angle = 7°; 176 sagittal
slices; spatial resolution = 1 × 1 × 1.95 mm) for registration purposes.
Image preprocessing and statistical analyses were carried out using
FSL (www.fmrib.ox.ac.uk/fsl). Images were realigned to compensate for
small residual head movements (Jenkinson & Smith, 2001). Translational
movement parameters never exceeded one voxel in any direction for any
participant. Data were spatially smoothed using a 5-mm full-width-half-
maximum (FWHM) Gaussian kernel and were ltered using a nonlinear
high pass lter with a 100-second cuto.
A two-step registration procedure was used whereby EPI images
were rst registered to the MPRAGE structural image, and then into stan-
dard MNI space, using ane transformations (Jenkinson & Smith, 2001).
Registration from MPRAGE structural image to standard space was fur-
ther rened using FNIRT nonlinear registration (Andersson, Jenkinson,
& Smith, 2007a, 2007b). Statistical analyses were performed in the native
image space, with the statistical maps normalized to the standard space
prior to higher-level analyses. The data were modeled at the rst-level
using a general linear model within FSL's FILM module. Brain activation
in every trial was modeled for go and no-go trials, respectively, at the sin-
gle-participant level. Error-related trials (misses and false alarms) were
modeled together as a nuisance variable. The event onsets were convolved
with canonical hemodynamic response function (HRF, double-gamma) to
generate regressors. Temporal derivatives were included as covariates of
04_PR_Turel_140140.indd 681 16/12/14 10:58 AM
O. TUREL, ET AL.
no interest to improve statistical sensitivity. Null events were not explic-
itly modeled, and therefore constituted an implicit baseline. The six move-
ment parameters were also included as covariates in the model.
A higher-level analysis created cross-run contrasts for each partici-
pant, using a xed-eect model. A 2 task (go vs no-go) × 2 stimuli (trac
sign vs Facebook pictures) within-subjects factor design was used. The
main eects and interaction were modeled as well as 4 single-condition
eects (Facebook go; Facebook no-go; trac sign go; trac sign no-go).
Higher-level random-eects models were tested for group analyses using
FMRIB's Local Analysis of Mixed Eect stage 1 only (Beckmann, Jenkin-
son, & Smith, 2003; Woolrich, Behrens, Beckmann, Jenkinson, & Smith,
2004) with automatic outlier detection (Woolrich, 2008). The brain activa-
tion associated with each contrast was rst tested in all participants using
one-sample t tests. Then, the brain activation was correlated with the ad-
diction score. Group images were thresholded with a height threshold of
Z > 2.3 and a cluster probability of p < .05, corrected for whole-brain multi-
ple comparisons based on a Gaussian random eld theory. The sex of par-
ticipants was included as a covariate for all fMRI analyses.
Regions of interest (ROI) were used to show the direction of the ac-
tivation and in correlation analyses and scatterplots. ROIs were created
from clusters of voxels with signicant activation in the voxel-wise anal-
ysis. Analyses were performed by extracting parameter estimates (betas)
for each event type from the tted model, and averaging them across
all voxels in the cluster for each participant and session. Percent signal
changes were calculated using a method suggested by Mumford.6
There was no signicant correlation between “addiction” score and
age (r = −.20, p = .40), but the “addiction” score was signicantly correlated
with sex (r = .45, p = .05). This sex dierence was also supported by a t test
(t18 = −2.12, p < .05, Cohen's d = 0.96), and variance was reasonably equal (Lev-
ene's test F = 1.03, p < .32). This implies that at least in the sample, women
(coded as 1) presented stronger addiction-like symptoms with regards to
Facebook use (female M = 2.52) than did men (male M = 1.89). There was no
signicant correlation between “addiction” score and other behavioral mea-
sures of the go/no-go tasks (all with p > .05).
Table 1 summarizes the major behavioral measures for both fMRI
tasks, including hit rates, false alarm rates, sensitivity index d', decision
bias C, and reaction times for go trials and no-go trials (false alarm rates
04_PR_Turel_140140.indd 682 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 683
for inhibitory failures only). For every behavioral measure, a paired t test
was performed to test the dierence between tasks (SGo vs FGo task). In
all cases Levene's test statistics were non-signicant (all p > .10), indicating
that equal variance can be assumed. Analysis revealed that the average
reaction time for go trials was signicantly longer in the SGo task than in
the FGo task (p < .05 corrected). Analysis also revealed dierences in the
false alarm rate and reaction time for no-go trials between the two tasks,
but they were no longer signicant after multiple comparison correction
The fMRI analyses were used in a conrmatory manner. First, it was ex-
amined whether the amygdala-striatal system was engaged in the Facebook-
go trials. As showed in Table 2 and Fig. 2, this pattern was supported because
the Facebook-go trials activated a large sector of the amygdala-striatum sys-
tem along with other brain regions, including the occipital cortex, parietal
cortex, and precentral gyrus/insula. Next, in order to test for possible hy-
peractivity of the amygdala-striatal system as a function of one's “addiction”
score (Hypothesis 1), the “addiction” scores were correlated with the brain ac-
tivity in Facebook-go trials. The results revealed that bilateral ventral striatum
activity in Facebook-go trials was correlated positively with the “addiction”
score (Fig. 3). Hypothesis 1 was further supported by the results in Table 1,
which demonstrate that on average there are signicantly more false alarms
in the case of sign-go (i.e., Facebook-no-go) tasks, and that response times
were shorter in Facebook-go trials.
To test Hypothesis 2, it was rst examined whether the inhibitory con-
trol system (especially the ACC and dorsolateral prefrontal cortex) was
engaged in Facebook-no-go trials. Table 2 and Fig. 2 lend support to such
an eect, with activation of the ACC, right DLPFC/insula, and bilateral
BehavioRal MeasuRes FRoM the FaceBook-speciFic go/No-go task
Variable SGo Task FGo Task t p Cohen's
M SD M SD
Hits rate 0.90 0.11 0.90 0.12 −0.02 .98 −.009
False alarm rate 0.13 0.13 0.06 0.05 2.42 .03 1.08
Go trial response time (msec.) 522.3 86.7 487.3 78.2 44.2 .002 1.66
No-go trial response time (msec.) 433.2 50.7 389.0 44.2 2.53 .03 1.13
d' 2.85 0.54 3.04 0.61 −1.10 .29 −0.49
C−0.31 0.46 −0.07 0.27 −1.70 .12 −0.76
Note.—SGo = a sign go/Facebook no-go task; FGo = a Facebook go/sign no-go task. *p < .05
corrected for multiple comparison with a Bonferroni correction.
04_PR_Turel_140140.indd 683 16/12/14 10:58 AM
O. TUREL, ET AL.
occipital/parietal cortex. Furthermore, the prefrontal activation in Face-
book-no-go trials was compared with this of trac sign-no-go trials. No
signicant dierences were found. This suggests that in both conditions
participants engaged in similar levels of inhibition.
Fig. 2. Activation of Facebook-go (in red) and Facebook-no-go (in green) trials. Yellow
areas indicate common activation for go and no-go trials.
suMMaRy oF FMRi Results
Brain Region Voxels MNI Coordinates Z
x y z
Brain activity of Facebook-go trials
B Occipital cortex/Amygdala/Striatum 31,860 30 −62 −18 6.41
L Insula 1,596 −50 4 2 3.60
L Postcentral cortex 968 −62 −16 46 4.28
Brain activity of Facebook-no-go trials
B Occipital/ Parietal/ Temporal cortices 31,386 −40 −80 −14 5.94
R DLPFC 3,239 50 10 42 4.03
Cingulate cortex 781 4 12 48 3.16
Whole brain correlation between brain activity in Facebook-go trials and addiction score
Bilateral ventral striatum 1,021 −20 14 −8 3.67
Whole brain correlation between brain activity in Facebook-no-go trials and addiction
No signicant correlations were observed
04_PR_Turel_140140.indd 684 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 685
Next, the authors tested whether the activity of the inhibitory control
neural systems, as a manifestation of inhibition attempts in response to
Facebook cues, was associated negatively with “addiction” scores. That is,
the “addiction” scores of the participants were correlated with the brain
activity in Facebook-no-go trials. The results indicated no signicant as-
sociation between any component of the inhibition system (ventromedial
prefrontal cortex, lateral orbitofrontal, and inferior frontal gyrus regions,
and anterior cingulate cortex) and “addiction” scores (all with p > .05).
While the activation of the amygdala-striatal (impulsive) brain system
was positively associated with one's Facebook “addiction” score (i.e., the level
of addiction-like symptoms presented), there was no association between this
score and activation of the prefrontal cortex (inhibition) brain system. The
ndings, therefore, suggested that at least individuals with low to medium
levels of addiction-like symptoms have a hyperactive amygdala-striatal sys-
tem, which makes this “addiction” similar to many other addictions, but they
do not have a hypoactive prefrontal lobe inhibition system, which makes it
dierent from many other addictions, such as to illicit substances. Hence,
technology “addictions” may not present the exact same brain etiology and
possibly pathogenesis that drives substance and gambling addictions. The
detected hyperactivity of the impulsive brain system supplements and con-
rms ndings of other studies which discovered similarities between brain
systems sub-serving technology-related addictions and other addictions (Ko,
Liu, Hsiao, Yen, Yang, Lin, et al., 2009; Han, Hwang, & Renshaw, 2010; Han,
Kim, Lee, Min, & Renshaw, 2010; Han, Bolo, Daniels, Arenella, Lyoo, & Ren-
shaw, 2011; Han, Kim, Lee, & Renshaw, 2012; Han, Lyoo, & Renshaw, 2012;
Fig. 3. The ventral striatum signal showed positive correlation with the addiction score
in Facebook go trials. (A) Coronal image shows the ventral striatum signal. (B) Scatter plot
shows the correlation pattern.
04_PR_Turel_140140.indd 685 16/12/14 10:58 AM
O. TUREL, ET AL.
Ko, Liu, Yen, Yen, Chen, & Lin, 2013; Ko, Liu, Yen, Chen, Yen, & Chen, 2013).
However, the ndings regarding the frontal lobe inhibition system also point
to possible dissimilarities between substance and gambling addictions, and
Facebook “addiction,” which exist at least at the examined levels of addic-
Several implications of these ndings should be noted. First, studies
on technology-related “addictions” indicate that many individuals present
at least some (and in some cases many) addiction-like symptoms with low-
medium frequency and intensity (and in some cases high) in relation to the
use of presumably addictive technologies (La Barbera, La Paglia, & Valsavoia,
2009; Turel & Serenko, 2012). This has raised public and scientic awareness
of this potential problem (Block, 2008; Byun, et al., 2009), and has resulted in
the inclusion of Internet Gaming Disorder as a “condition for further study”
in DSM–V (American Psychiatric Association, 2013).
Without discounting the existence and importance of this problem
and its possible adverse consequences, one may question (1) whether the
term “addiction” is the most appropriate one for this problem (LaRose,
Lin, & Eastin, 2003; Yellowlees & Marks, 2007; Turel, et al., 2011; Kuss &
Griths, 2011); and (2) whether, at least when applied to the general pop-
ulation of users, commonly used addiction scales, which include relatively
easy-to-meet criteria and benign symptoms (LaRose, et al., 2003; LaRose,
2010), actually capture “addiction” or merely capture symptoms emerg-
ing from a strong bad habit of implicit and automatic high engagement
with a technology (Charlton & Danforth, 2007; Yellowlees & Marks, 2007;
Turel & Serenko, 2012). The ndings of this study partially addressed
these questions and implied that at least at low-medium levels of addic-
tion-like symptoms, the observed symptoms are associated with some
brain changes (sensitization of the amygdala-striatal system), but not with
changes in all key brain systems associated with substance addictions (es-
pecially the prefrontal cortex). In this sense, it adheres to calls by research-
ers (Block, 2008) and the DSM–V (American Psychiatric Association, 2013)
to further examine the pathology of technology-related addictions.
Second, the ndings lend support to past research pointing to the im-
portance of the amygdala-striatal system in addiction pathology (Everitt,
et al., 1999; Jentsch & Taylor, 1999; Volkow & Fowler, 2000; Everitt & Rob-
bins, 2005; Koob & Volkow, 2010). In this study, this system responded to
Facebook cues, produced more false alarms in sign-go (Facebook inhibi-
tion) tasks, and resulted in shorter response times in Facebook-go trials
(compared to the response times with regards to neutral signs). Further-
more, the activation of this system was positively and signicantly corre-
lated with the “addiction” scores. Thus, these symptoms, at least in part,
manifest from automatically and easily retrieved implicit associations and
04_PR_Turel_140140.indd 686 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 687
the consequent hyperactivity in the bilateral ventral striatum (Everitt, et
al., 1999). In this respect, Facebook “addiction” is similar to substance and
It was also hypothesized that the level of addiction-like symptoms in re-
lation to Facebook use would be negatively associated with activation of pre-
frontal inhibition brain structures, including the ventromedial prefrontal cor-
tex, lateral orbitofrontal, and inferior frontal gyrus regions, and the anterior
cingulate cortex (Di Chiara, 2000; Goldstein & Volkow, 2002; Volkow, Fowler,
Wang, & Swanson, 2004). These results were consistent with previous reports
that no-go trials with various stimuli activate the inhibitory control system
(Menon, Adleman, White, Glover, & Reiss, 2001; Garavan, Ross, Murphy,
Roche, & Stein, 2002), but also suggested that participants, regardless of their
level of addiction-like symptoms, presented normal functioning of the inhi-
bition system (i.e., no signicant hypo-activity was detected). That is, no im-
pairment of the inhibition system was observed.
These ndings imply that technology-related “addictions,” at least at
lower-to-medium levels of addiction-like symptoms, dier from other ad-
dictions, e.g., to illicit substances, on at least one dimension. While gam-
bling and substance addictions often involve the impairment of both the
impulse and inhibition brain systems (Noel, et al., 2013), technology-re-
lated “addictions,” at least at the examined levels of addiction-like symp-
toms, involve only changes to the amygdala-striatal system. Perhaps this
is a result of dierences between the adverse consequences in the case
of technology-related “addictions” (e.g., missing school) and those in the
case of other addictions (e.g., major health risks and troubles with the
law), the latter of which are likely to be more severe. While substance ad-
dicts often respond to substance-related cues without reection, and have
weak abilities to inhibit or exert cognitive control over their behaviors,
it seems that technology-related ”addicts” respond to Facebook cues in
a similar way, but have the capacity to inhibit such behaviors. Given the
abovementioned possible dierences between the adverse consequences
of substance and technology use, users of applications such as Facebook
perhaps lack the motivation to engage the prefrontal brain system (Shus-
ter & Toplak, 2009). This proposition, however, warrants further research.
Lastly, the ndings point to potential practical implications. They imply
that individuals who present low-medium levels of addiction-like symptoms
in relation to Facebook have an imbalance between their amygdala-striatal
and prefrontal cortex systems. Thus, their problematic use of Facebook (i.e.,
use that results in at least some addiction-like symptoms) can be overcome by
restoring the homeostasis between these two systems. This could be achieved
by cognitive behavioral therapy (CBT). Indeed, several attempts to apply
CBT in technology-related addiction cases have been reported to be success-
04_PR_Turel_140140.indd 687 16/12/14 10:58 AM
O. TUREL, ET AL.
ful (Young, 2007; van Rooij, Zinn, Schoenmakers, & van de Mheen, 2012), and
can perhaps also help with Facebook “addiction.”
Limitations and Future Research
Several limitations of this study that point to future research should
be acknowledged. First, the sample included educated young adults re-
siding in one country. Given possible age-, socio-economic-, and nation-
based dierences in technology “addictions,” future research may extend
the generalizability of the ndings to other populations, and in line with
the ecological model include a range of ecological risk factors, beyond
the individual, in the model (Catala-Minana, Lila, & Oliver, 2013; Raynor,
2013; Sterk, Elifson, & DePadilla, 2014). It can perhaps also focus on ad-
ditional possible consequences of such “addictions,” e.g., obesity. Second,
the sample was limited in the range of addiction-like symptoms it pre-
sented. Because the sample included participants with low-medium lev-
els of addiction-like symptoms, future research could examine those few
extreme users with very high addiction scores to nd if there is a point
of inection after which prefrontal cortex impairments might be observ-
able. This study was correlational in nature, and hence caution regarding
causality arguments should be exercised. Addiction co-morbidity did not
exist in the current sample. Nevertheless, longitudinal studies can be ex-
ecuted to see if prefrontal cortex weaknesses in these few extreme users
progress into not only Internet “addiction,” but also into other addictive
behaviors. Since Internet use starts at a very early age, typically before any
exposure to addictive substances, this would help address an important
theoretical question in addiction research on whether brain abnormalities
precede substance abuse, or whether these abnormalities are actually the
consequences of substance abuse (Ersche, Jones, Williams, Turton, Rob-
bins, & Bullmore, 2012).
While the authors have acknowledged the yet-unknown appropriate-
ness of the term “addiction” when applied to Facebook by using quotation
marks, more research on similarities and dierences between Facebook
“addiction” and substance and gambling addictions should be conducted.
Noteworthy is the fact that this study focused on “addiction” to an activ-
ity that is legal and has socially acceptable symptoms, or at least symptoms
not judged harshly by society. In contrast, addictions to substances or gam-
bling can have much more severe consequences. Perhaps these dierences
in societal view of such addictions and possible dierences in the sever-
ity of the consequences act as inhibition (de)motivators, and consequently
people may lack the motivation to inhibit their Facebook use, rather than
having impaired inhibition systems. This proposition, however, merits fur-
ther research. Similarly, Facebook “addiction” may not be a cause but a me-
diator or moderator of some other facet of experience. It could also serve
04_PR_Turel_140140.indd 688 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 689
as a gateway for developing other addictions (i.e., when prefrontal cortex
changes are observed), at which point early interventions could help avert
more serious addictions. This idea, too, merits further research. Lastly,
this study points to the possibility that some interventions, such as CBT
or trans-cranial magnetic stimulation (Hallett, 2000), may be ecacious in
treating Facebook “addiction.” However, more research on the ecacy of
these therapeutic strategies to deal with such “addictions” is needed.
aMeRicaN psychiatRic associatioN. (2000) Diagnostic and statistical manual of mental disor-
ders–text revision. (4th ed.) Washington, DC: Author.
aMeRicaN psychiatRic associatioN. (2013) Internet gaming disorder diagnostic and statistical
manual of mental disorders. (5th ed.) Arlington, VA: American Psychiatric Publish-
ing. Pp. 795-798.
aNdeRssoN, J. L. R., JeNkiNsoN, M., & sMith, S. (2007a) Non-linear optimisation. (FMRIB
Technical Report TR07JA1) Retrieved from www.fmrib.ox.ac.uk/analysis/techrep.
aNdeRssoN, J. L. R., JeNkiNsoN, M., & sMith, S. (2007b) Non-linear registration, aka spatial
normalisation. (FMRIB Technical Report TR07JA2) Retrieved from www.fmrib.
aNdReasseN, C. S., toRsheiM, T., BRuNBoRg, G. S., & palleseN, S. (2012) Development of a
Facebook addiction scale. Psychological Reports, 110(2), 501-517. DOI: 10.2466/02.09.
aRNsteN, A. F. T., & li, B. M. (2005) Neurobiology of executive functions: catecholamine
inuences on prefrontal cortical functions. Biological Psychiatry, 57(11), 1377-1384.
BakkeN, I. J., WeNzel, H. G. R. O., götestaM, K. G., JohaNssoN, A., & oeReN, A. (2009)
Internet addiction among Norwegian adults: a stratied probability sample study.
Scandinavian Journal of Psychology, 50(2), 121-127.
BalleiNe, B. W., & dickiNsoN, A. (2000) The eect of lesions of the insular cortex on
instrumental conditioning: evidence for a role in incentive memory. Journal of Neu-
roscience, 20, 8954-8964.
BechaRa, A. (2005) Decision-making, impulse control, and loss of willpower to resist
drugs: a neurocognitive perspective. Nature Neuroscience, 8(11), 1458-1463.
BechaRa, A., tRaNel, D., & daMasio, H. (2000) Characterization of the decision-making
decit of patients with ventromedial prefrontal cortex lesions. Brain, 123(11), 2189-
BeckMaNN, C. F., JeNkiNsoN, M., & sMith, S. M. (2003) General multilevel linear modeling
for group analysis in fMRI. Neuroimage, 20(2), 1052-1063.
BeRgMaRk, K. H., BeRgMaRk, A., & FiNdahl, O. (2011) Extensive Internet involvement–
addiction or emerging lifestyle? International Journal of Environmental Research and
Public Health, 8(12), 4488-4501. DOI: 10.3390/ijerph8124488
Bickel, W. K., MilleR, M. L., yi, R., koWal, B. P., liNdquist, D. M., & pitcock, J. A. (2007)
Behavioral and neuroeconomics of drug addiction: competing neural systems and
temporal discounting processes. Drug and Alcohol Dependence, 90, S85-S91. DOI:
04_PR_Turel_140140.indd 689 16/12/14 10:58 AM
O. TUREL, ET AL.
Block, J. J. (2008) Issues for DSM–V: Internet addiction. American Journal of Psychiatry,
165(3), 306-307. DOI: 10.1176/appi.ajp.2007.07101556
BRaveR, T. S., & Ruge, H. (2006) Functional neuroimaging of executive functions. In R.
Cabeza & A. Kingstone (Eds.), Handbook of functional neuroimaging of cognition. (2nd
ed.) Cambridge, MA: Massachusetts Institute of Technology Press. Pp. 307-348.
ByuN, S., RuFFiNi, C., Mills, J. E., douglas, A. C., NiaNg, M., stepcheNkova, S., lee, S. K.,
loutFi, J., lee, J. K., atallah, M., & BlaNtoN, M. (2009) Internet addiction: meta-
synthesis of 1996–2006 quantitative research. Cyberpsychology & Behavior, 12(2),
203-207. DOI: 10.1089/cpb.2008.0102
cao, F., & su, L. (2007) Internet addiction among Chinese adolescents: prevalence and
psychological features. Child: Care, Health and Development, 33(3), 275-281.
casey, B. J., getz, S., & galvaN, A. (2008) The adolescent brain. Developmental Review,
28(1), 62-77. DOI: 10.1016/j.dr.2007.08.003
casey, B. J., giedd, J. N., & thoMas, K. M. (2000) Structural and functional brain devel-
opment and its relation to cognitive development. Biological Psychology, 54(1-3),
241-257. DOI: 10.1016/s0301-0511(00)00058-2
casey, B. J., totteNhaM, N., listoN, C., & duRstoN, S. (2005) Imaging the developing
brain: what have we learned about cognitive development? Trends in Cognitive
Sciences, 9(3), 104-110. DOI: 10.1016/j.tics.2005.01.011
catala-MiNaNa, A., lila, M., & oliveR, A. (2013) Alcohol consumption in men punished
for intimate partner violence: individual and contextual factors. Adicciones, 25(1),
chaRltoN, J. P., & daNFoRth, I. D. W. (2007) Distinguishing addiction and high engage-
ment in the context of online game playing. Computers in Human Behavior, 23(3),
chou, C., coNdRoN, L., & BellaNd, J. C. (2005) A review of the research on Internet
addiction. Educational Psychology Review, 17(4), 363-388. DOI: 10.1007/s10648-005-
d'aRcy, J., gupta, A., taRaFdaR, M., & tuRel, O. (2014) Reecting on the “Dark Side”
of information technology use. Communications of the Association for Information
Systems, 35(1), 109-118.
di chiaRa, G. (2000) Role of dopamine in the behavioural actions of nicotine related
to addiction. European Journal of Pharmacology, 393(1-3), 295-314. DOI: 10.1016/
di chiaRa, G., taNda, G., BassaReo, V., poNtieRi, F., acquas, E., FeNu, S., cadoNi, C., &
caRBoNi, E. (1999) Drug addiction as a disorder of associative learning: role of
nucleus accumbens shell/extended amygdala dopamine. Annals of the New York
Academy of Sciences (Advancing from the Ventral Striatum to the Extended Amygdala),
echeBuRua, E., & de coRRal, P. (2010) Addiction to new technologies and to online social
networking in young people: a new challenge. Adicciones, 22(21), 91-95.
eRsche, K. D., JoNes, P. S., WilliaMs, G. B., tuRtoN, A. J., RoBBiNs, T. W., & BullMoRe, E. T.
(2012) Abnormal brain structure implicated in stimulant drug addiction. Science,
335(6068), 601-604. DOI: 10.1126/science.1214463
eveRitt, B. J., paRkiNsoN, J. A., olMstead, M. C., aRRoyo, M., RoBledo, P., & RoBBiNs, T.
W. (1999) Associative processes in addiction and reward: the role of amygdala
and ventral striatal subsystems. In J. F. McGinty (Ed.), Advancing from the ventral
striatum to the extended amygdala. Vol. 877. New York: Annals of the New York
Academy of Sciences. Pp. 412-438.
04_PR_Turel_140140.indd 690 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 691
eveRitt, B. J., & RoBBiNs, T. W. (2005) Neural systems of reinforcement for drug addic-
tion: from actions to habits to compulsion. Nature Neuroscience, 8(11), 1481-1489.
FelloWs, L. K. (2004) The cognitive neuroscience of human decision making: a review
and conceptual framework. Behavioral and Cognitive Neuroscience Reviews, 3(3),
gaRavaN, H., Ross, T. J., MuRphy, K., Roche, R. A. P., & steiN, E. A. (2002) Dissociable
executive functions in the dynamic control of behavior: inhibition, error detection,
and correction. NeuroImage, 17(4), 1820-1829. DOI: 10.1006/nimg.2002.1326
ghasseMzadeh, L., shahRaRay, M., & MoRadi, A. (2008) Prevalence of Internet addic-
tion and comparison of Internet addicts and non-addicts in Iranian high schools.
Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality
on Behavior and Society, 11(6), 731.
glascheR, J., adolphs, R., daMasio, H., BechaRa, A., RudRauF, D., calaMia, M., paul, L.
K., & tRaNel, D. (2012) Lesion mapping of cognitive control and value-based deci-
sion making in the prefrontal cortex. Proceedings of the National Academy of Sciences
of the United States of America, 109(36), 14681-14686. DOI: 10.1073/pnas.1206608109
goldsteiN, R. Z., & volkoW, N. D. (2002) Drug addiction and its underlying neurobiolog-
ical basis: neuroimaging evidence for the involvement of the frontal cortex. Ameri-
can Journal of Psychiatry, 159(10), 1642-1652. DOI: 10.1176/appi.ajp.159.10.1642
gReeNField, D. (1999) Virtual addiction: help for netheads, cyberfreaks, and those who love
them. Oakland, CA: New Harbinger.
gRiFFiths, M. D. (1995) Technological addictions. Clinical Psychology Forum, 76(1), 14-19.
gRiFFiths, M. D. (1998) Internet addiction: does it really exist? In J. Gackenbach (Ed.),
Psychology and the Internet: intrapersonal, interpersonal and transpersonal implications.
New York: Academic Press. Pp. 61-75.
gRiFFiths, M. D. (1999) Internet addiction: fact or ction? The Psychologist, 12(5), 246-251.
gRiFFiths, M. D. (2012) Facebook addiction: concerns, critics, and recommendations:
a response to Andreassen and colleagues. Psychological Reports, 110(2), 518-520.
hallett, M. (2000) Transcranial magnetic stimulation and the human brain. Nature,
406(6792), 147-150. DOI: 10.1038/35018000
haN, D. H., Bolo, N., daNiels, M. A., aReNella, L., lyoo, I. K., & ReNshaW, P. F. (2011)
Brain activity and desire for Internet video game play. Comprehensive Psychiatry,
52(1), 88-95. DOI: 10.1016/j.comppsych.2010.04.004
haN, D. H., hWaNg, J. W., & ReNshaW, P. F. (2010) Bupropion sustained release treatment
decreases craving for video games and cue-induced brain activity in patients with
Internet video game addiction. Experimental and Clinical Psychopharmacology, 18(4),
297-304. DOI: 10.1037/a0020023
haN, D. H., kiM, Y. S., lee, Y. S., MiN, K. J., & ReNshaW, P. F. (2010) Changes in cue-
induced, prefrontal cortex activity with video-game play. Cyberpsychology, Behav-
ior, and Social Networking, 13(6). DOI: 10.1089/cyber.2009.0327
haN, D. H., kiM, S. M., lee, Y. S., & ReNshaW, P. F. (2012) The eect of family therapy on
the changes in the severity of on-line game play and brain activity in adolescents
with on-line game addiction. Psychiatry Research: Neuroimaging, 202(2), 126-131.
04_PR_Turel_140140.indd 691 16/12/14 10:58 AM
O. TUREL, ET AL.
haN, D. H., lyoo, I. K., & ReNshaW, P. F. (2012) Dierential regional gray matter vol-
umes in patients with on-line game addiction and professional gamers. Journal of
Psychiatric Research, 46(4), 507-515. DOI: 10.1016/j.jpsychires.2012.01.004
JeNkiNsoN, M., & sMith, S. (2001) A global optimisation method for robust ane registra-
tion of brain images. Medical Image Analysis, 5(2), 143-156.
JeNtsch, J. D., & tayloR, J. R. (1999) Impulsivity resulting from frontostriatal dysfunc-
tion in drug abuse: implications for the control of behavior by reward-related
stimuli. Psychopharmacology, 146(4), 373-390. DOI: 10.1007/pl00005483
JohaNssoN, A., & götestaM, K. G. (2004) Internet addiction: characteristics of a question-
naire and prevalence in Norwegian youth (12–18 years). Scandinavian Journal of
Psychology, 45(3), 223-229.
kaRaiskos, D., tzavellas, E., Balta, G., & papaRRigopoulos, T. (2010) P02-232 - Social
network addiction: a new clinical disorder? European Psychiatry, 25(Suppl. 1), 855.
kiM, K., Ryu, E., choN, M-Y., yeuN, E-J., choi, S-Y., seo, J-S., & NaM, B-W. (2006) Inter-
net addiction in Korean adolescents and its relation to depression and suicidal
ideation: a questionnaire survey. International Journal of Nursing Studies, 43(2), 185-
ko, C-H., liu, G-C., hsiao, S. M., yeN, J-Y., yaNg, M-J., liN, W-C., yeN, C-F., & cheN, C-S.
(2009) Brain activities associated with gaming urge of online gaming addiction.
Journal of Psychiatric Research, 43(7), 739-747. DOI: 10.1016/j.jpsychires.2008.09.012
ko, C-H., liu, G-C., yeN, J-Y., cheN, C-Y., yeN, C-F., & cheN, C-S. (2013) Brain corre-
lates of craving for online gaming under cue exposure in subjects with Internet
gaming addiction and in remitted subjects. Addiction Biology, 18(3), 559-569. DOI:
ko, C-H., liu, G-C., yeN, J-Y., yeN, C-F., cheN, C-S., & liN, W-C. (2013) The brain acti-
vations for both cue-induced gaming urge and smoking craving among subjects
comorbid with Internet gaming addiction and nicotine dependence. Journal of Psy-
chiatric Research, 47(4), 486-493. DOI: 10.1016/j.jpsychires.2012.11.008
kooB, G. F., & le Moal, M. (2001) Drug addiction, dysregulation of reward, and allosta-
sis. Neuropsychopharmacology, 24(278), 97-129.
kooB, G. F., & volkoW, N. D. (2010) Neurocircuitry of addiction. Neuropsychopharmacol-
ogy, 35(1), 217-238. DOI: 10.1038/npp.2009.110
kuss, D. J., & gRiFFiths, M. D. (2011) Online social networking and addiction—a review
of the psychological literature. International Journal of Environmental Research and
Public Health, 8(9), 3528-3552. DOI: 10.3390/ijerph8093528
kuss, D. J., gRiFFiths, M. D., & BiNdeR, J. F. (2013) Internet addiction in students: preva-
lence and risk factors. Computers in Human Behavior, 29(3), 959-966. DOI: 10.1016/j.
la BaRBeRa, D., la paglia, F., & valsavoia, R. (2009) Social network and addiction.
Cyberpsychology & Behavior, 12(5), 628-629.
laRose, R. (2010) The problem of media habits. Communication Theory, 20(2), 194-222.
laRose, R., liN, C. A., & eastiN, M. S. (2003) Unregulated Internet usage: addiction,
habit, or decient self-regulation? Media Psychology, 5(3), 225-253.
logaN, G. D., schachaR, R. J., & taNNock, R. (1997) Impulsivity and inhibitory control.
Psychological Science, 8(1), 60-64.
04_PR_Turel_140140.indd 692 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 693
MacMillaN, N. A., & cReelMaN, C. D. (1996) Triangles in ROC space: history and theory
of “nonparametric” measures of sensitivity and response bias. Psychonomic Bul-
letin & Review, 3(2), 164-170. DOI: 10.3758/bf03212415
MeNoN, V., adleMaN, N. E., White, C. D., gloveR, G. H., & Reiss, A. L. (2001) Error-
related brain activation during a Go/NoGo response inhibition task. Human Brain
Mapping, 12(3), 131-143. DOI: 10.1002/1097-0193(200103)12:3<131::aid-hbm1010>
Meshi, D., MoRaWetz, C., & heekeReN, H. R. (2013) Nucleus accumbens response to
gains in reputation for the self relative to gains for others predicts social media
use. Frontiers in Human Neuroscience, 7. DOI: 10.3389/fnhum.2013.00439
Noel, X., BReveRs, D., & BechaRa, A. (2013) A neurocognitive approach to understand-
ing the neurobiology of addiction. Current Opinion in Neurobiology, 23(4), 632-638.
paRk, S. K., kiM, J. Y., & cho, C. B. (2008) Prevalence of Internet addiction and cor-
relations with family factors among South Korean adolescents. Adolescence (San
Diego): An International Quarterly Devoted to the Physiological, Psychological, Psychi-
atric, Sociological, and Educational Aspects of the Second Decade of Human Life, 43(172),
pRataRelli, M. E., BRoWNe, B. L., & JohNsoN, K. (1999) The bits and bytes of computer/
Internet addiction: a factor analytic approach. Behavior Research Methods, Instru-
ments, & Computers, 31(2), 305-314.
RayNoR, P. A. (2013) An exploration of the factors inuencing parental self-ecacy
for parents recovering from substance use disorders using the social ecological
framework. Journal of Addictions Nursing, 24(2), 91-99. DOI: 10.1097/JAN.0b013
ReNdi, M., szaBo, A., & szaBó, T. (2007) Exercise and Internet addiction: communalities
and dierences between two problematic behaviours. International Journal of Men-
tal Health and Addiction, 5(3), 219-232.
RoBBiNs, T. W., cadoR, M., tayloR, J. R., & eveRitt, B. J. (1989) Limbic-striatal interac-
tions in reward-related processes. Neuroscience and Biobehavioral Reviews, 13(2-3),
RoBiNsoN, T. E., & BeRRidge, K. C. (1993) The neural basis of drug craving: an incentive-
sensitization theory of addiction. Brain Research Reviews, 18, 247-291.
RoBiNsoN, T. E., & BeRRidge, K. C. (2003) Addiction. Annual Review of Psychology, 54(1),
shaW, M., & Black, D. W. (2008) Internet addiction: denition, assessment, epidemiol-
ogy and clinical management. CNS Drugs, 22(5), 353-365.
shusteR, J., & toplak, M. E. (2009) Executive and motivational inhibition: associations
with self-report measures related to inhibition. Consciousness and Cognition, 18(2),
471-480. DOI: 10.1016/j.concog.2009.01.004
sioMos, K. E., daFouli, E. D., BRaiMiotis, D. A., Mouzas, O. D., & aNgelopoulos, N. V.
(2008) Internet addiction among Greek adolescent students. CyberPsychology &
Behavior, 11(6), 653-657.
steiNBeRg, L. (2005) Cognitive and aective development in adolescence. Trends in Cog-
nitive Sciences, 9(2), 69-74. DOI: 10.1016/j.tics.2004.12.005
steiNBeRg, L. (2008) A social neuroscience perspective on adolescent risk-taking. Devel-
opmental Review, 28(1), 78-106. DOI: 10.1016/j.dr.2007.08.002
04_PR_Turel_140140.indd 693 16/12/14 10:58 AM
O. TUREL, ET AL.
steiNBeRg, L., gRahaM, S., o'BRieN, L., WoolaRd, J., cauFFMaN, E., & BaNich, M. (2009)
Age dierences in future orientation and delay discounting. Child Development,
80(1), 28-44. DOI: 10.1111/j.1467-8624.2008.01244.x
steRk, C. E., eliFsoN, K. W., & depadilla, L. (2014) Neighbourhood structural charac-
teristics and crack cocaine use: exploring the impact of perceived neighbourhood
disorder on use among African Americans. International Journal of Drug Policy,
25(3), 616-623. DOI: 10.1016/j.drugpo.2013.12.007
steWaRt, J., deWit, H., & eikelBooM, R. (1984) Role of unconditioned and conditioned
drug eects in the self-administration of opiates and stimulants. Psychological
Review, 91(2), 251-268.
tuRel, O., & seReNko, A. (2012) The benets and dangers of enjoyment with social net-
working websites. European Journal of Information Systems, 21(5), 512-528. DOI:
tuRel, O., seReNko, A., & giles, P. (2011) Integrating technology addiction and use: an
empirical investigation of online auction users. MIS Quarterly, 35(4), 1043-1061.
vaN RooiJ, A. J., schoeNMakeRs, T. M., veRMulst, A. A., vaN deN eiJNdeN, R., & vaN de
MheeN, D. (2011) Online video game addiction: identication of addicted ado-
lescent gamers. Addiction, 106(1), 205-212. DOI: 10.1111/j.1360-0443.2010.03104.x
vaN RooiJ, A. J., ziNN, M. F., schoeNMakeRs, T. M., & vaN de MheeN, D. (2012) Treating
Internet addiction with cognitive-behavioral therapy: a thematic analysis of the
experiences of therapists. International Journal of Mental Health and Addiction, 10(1),
69-82. DOI: 10.1007/s11469-010-9295-0
volkoW, N. D., & FoWleR, J. S. (2000) Addiction, a disease of compulsion and drive:
involvement of the orbitofrontal cortex. Cerebral Cortex, 10(3), 318-325. DOI:
volkoW, N. D., FoWleR, J. S., WaNg, G. J., & sWaNsoN, J. M. (2004) Dopamine in drug
abuse and addiction: results from imaging studies and treatment implications.
Molecular Psychiatry, 9(6), 557-569. DOI: 10.1038/sj.mp.4001507
WheeleR, E. Z., & FelloWs, L. K. (2008) The human ventromedial frontal lobe is critical
for learning from negative feedback. Brain, 131(5), 1323-1331.
Wise, R. A., & RoMpRe, P. P. (1989) Brain dopamine and reward. Annual Reviews of Psy-
chology, 40, 191-225.
Wood, W., & Neal, D. T. (2007) A new look at habits and the habit-goal interface. Psy-
chological Review, 114(4), 843-863.
WoolRich, M. (2008) Robust group analysis using outlier inference. NeuroImage, 41(2),
WoolRich, M. W., BehReNs, T. E. J., BeckMaNN, C. F., JeNkiNsoN, M., & sMith, S. M. (2004)
Multilevel linear modelling for fMRI group analysis using Bayesian inference.
NeuroImage, 21(4), 1732-1747.
WoRld health oRgaNizatioN. (1992) The ICD-10 classication of mental and behavioral dis-
orders: clinical descriptions and diagnostic guidelines. Geneva, Switzerland: Author.
yelloWlees, P. M., & MaRks, S. (2007) Problematic Internet use or Internet addiction?
Computers in Human Behavior, 23(3), 1447-1453. DOI: 10.1016/j.chb.2005.05.004
youNg, K. S. (1998a) Caught in the net: how to recognize the signs of Internet addiction—and
a winning strategy for recovery. New York: Wiley.
youNg, K. S. (1998b) Internet addiction: the emergence of a new clinical disorder. Cyber-
psychology & Behavior, 3(2), 237-244.
04_PR_Turel_140140.indd 694 16/12/14 10:58 AM
FACEBOOK AND NEURAL SYSTEMS 695
youNg, K. S. (2004) Internet addiction: a new clinical phenomenon and its consequences.
American Behavioral Scientist, 48(4), 402-415. DOI: 10.1177/0002764204270278
youNg, K. S. (2007) Cognitive behavior therapy with Internet addicts: treatment out-
comes and implications. Cyberpsychology & Behavior, 10, 671-679. DOI: 10.1089/
youNg, K. S. (2010) Internet addiction over the decade: a personal look back. World
Psychiatry, 9(2), 91.
Accepted November 13, 2014.
04_PR_Turel_140140.indd 695 16/12/14 10:58 AM