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-Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm 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 Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.
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ISSN 0033-2941DOI 10.2466/18.PR0.115c31z8
© Psychological Reports 2014
Psychological Reports: Disability & Trauma
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
University, China
Brain and Creativity Institute, University of Southern California
Brain and Creativity Institute, University of
Southern California
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 specic 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 (trac 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 dier 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 benecial 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 specic applications on the
Internet (Griths, 1998; Young, 1998a; Griths, 1999; Young, 2004; Turel,
2014, 115, 3, 675-695.
1Address correspondence to Or Turel, 800 N. State College Blvd., Fullerton, CA 92834 or
e-mail ( or
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
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. Griths,
1995; Young, 1998b; Pratarelli, Browne, & Johnson, 1999; Chou, Condron,
& Belland, 2005; Block, 2008; Byun, Runi, Mills, Douglas, Niang, Step-
chenkova, et al., 2009; Young, 2010; Kuss, Griths, & 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%
(Greeneld, 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, Cauman, &
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 specic, 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-specic
“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 reects 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 (Griths,
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 dierences between the samples, and national dierences in
accessibility to technologies and the availability of alternative activities.
04_PR_Turel_140140.indd 676 16/12/14 10:58 AM
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 & Griths,
2011; Griths, 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-
nicant 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 denition of “signicant 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 dierences
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 reective-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 eects 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
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”-
like symptoms.
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 reects 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 reective-
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
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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 sucient 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 conict. It therefore presumably captures the “level of ‘addic-
tion’” and was valid and reliable (α = .92, Spearman-Brown Coecient for
split-half reliability = 0.91, Guttman split-half coecient = 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-signicant. 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 ( on an IBM-compatible PC. The partici-
pants' responses were collected online using an MRI-compatible button
The participants performed two Facebook-specic 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
were asked to press a button when they saw a trac 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 trac 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. Trac 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 eciency 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-
tivity index
dZ Z'
Hitsrate fals ealaramrate,
and decision bias
CZZ=− ×+
05.Hits rate falsealaramrate
Fig. 1. The illustration of the event-related Facebook-specic 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 (trac 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 trac 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
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-
hibitory control.
fMRI Protocol
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.
fMRI Analysis
Image preprocessing and statistical analyses were carried out using
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 ane transformations (Jenkinson & Smith, 2001).
Registration from MPRAGE structural image to standard space was fur-
ther rened 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
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-eect model. A 2 task (go vs no-go) × 2 stimuli (trac
sign vs Facebook pictures) within-subjects factor design was used. The
main eects and interaction were modeled as well as 4 single-condition
eects (Facebook go; Facebook no-go; trac sign go; trac sign no-go).
Higher-level random-eects models were tested for group analyses using
FMRIB's Local Analysis of Mixed Eect 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 signicant 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
Behavioral Results
There was no signicant correlation between “addiction” score and
age (r = −.20, p = .40), but the “addiction” score was signicantly correlated
with sex (r = .45, p = .05). This sex dierence 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
signicant 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
for inhibitory failures only). For every behavioral measure, a paired t test
was performed to test the dierence between tasks (SGo vs FGo task). In
all cases Levene's test statistics were non-signicant (all p > .10), indicating
that equal variance can be assumed. Analysis revealed that the average
reaction time for go trials was signicantly longer in the SGo task than in
the FGo task (p < .05 corrected). Analysis also revealed dierences in the
false alarm rate and reaction time for no-go trials between the two tasks,
but they were no longer signicant after multiple comparison correction
(Table 1).
fMRI Results
The fMRI analyses were used in a conrmatory 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 signicantly 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 eect, 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
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
occipital/parietal cortex. Furthermore, the prefrontal activation in Face-
book-no-go trials was compared with this of trac sign-no-go trials. No
signicant dierences 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 signicant correlations were observed
04_PR_Turel_140140.indd 684 16/12/14 10:58 AM
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 signicant 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
dierent 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
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-
tion symptoms.
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 scientic 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 &
Griths, 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 signicantly 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
the consequent hyperactivity in the bilateral ventral striatum (Everitt, et
al., 1999). In this respect, Facebook “addiction” is similar to substance and
gambling addictions.
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 signicant 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, dier 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 dierences 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 reection, 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 dierences 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
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 dierences 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 inection 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 dierences 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 dierences
in societal view of such addictions and possible dierences 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
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 ecacious in
treating Facebook “addiction.” However, more research on the ecacy of
these therapeutic strategies to deal with such “addictions” is needed.
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... 2D:4D Ratio), das Risiko für pathologischen Alkoholkonsum (Kornhuber et al. 2011;Lenz et al. 2012) und IGD beeinflusst (Kornhuber et al. 2013;Lenz et al. 2012;Lenz et al. 2017;Lenz et al. 2018). Weitere Studien konnten andererseits zeigen, dass soziale Netzwerke auf Frauen im Vergleich zu Männern eine höhere Anziehung ausüben (Andreassen et al. 2017;Turel 2015) und Frauen mit einer höheren Wahrscheinlichkeit SND-Symptome entwickeln (Turel et al. 2014 (Griffin et al. 1989), einem stärker ausgeprägten Craving (Verlangen nach der Droge) während der Abstinenz (Robbins et al. 1999;Tonn Eisinger et al. 2018) und einem erschwerten Aufhören mit dem Drogenkonsum (Carpenter et al. 2006;Lynch et al. 2002;Tonn Eisinger et al. 2018) (Evans et al. 2002;Justice und de Wit 1999;Tonn Eisinger et al. 2018). Diese bei Frauen beobachteten Unterschiede in der Reaktion auf Kokain in Abhängigkeit des Estradiol-Levels konnten bei Ratten reproduziert werden (Anker und Carroll 2011;Becker und Hu 2008;Tonn Eisinger et al. 2018). ...
... Studien konnten zeigen, dass SNSs auf Frauen im Vergleich zu Männern eine höhere Anziehung ausüben(Andreassen et al. 2017;Turel 2015) und Frauen mit einer höheren Wahrscheinlichkeit SND-Symptome entwickeln(Turel et al. 2014) bzw. mehr SND-Kriterien erfüllen (Bouna-Pyrrou et al. 2018). ...
ZUSAMMENFASSUNG Hintergrund und Ziele: Internetspiele und Soziale Netzwerke (Social Network Sites = SNSs) bergen ein hohes Suchtpotential. Es ist wichtig die passionierte Nutzung der genannten Medien von der pathologischen Nutzung zu unterscheiden. Die Internet Gaming Disorder (IGD) und die Social Network Use Disorder (SND) gehen mit verschiedenen negativen Auswirkungen auf das Individuum und sein Umfeld einher, die denen in substanzgebundenen Süchten beobachteten Effekten gleichen. Jungen und Männer spielen mehr Internetspiele und haben ein größeres Risiko eine IGD zu entwickeln. Für Mädchen und Frauen scheint das Gleiche für SNSs und SND zu gelten. Es bestehen Geschlechtsunterschiede in verschiedenen Bereichen der Suchtentwicklung und -Aufrechterhaltung. Frauen haben beispielsweise eine höhere Tendenz den Substanzgebrauch oder das Glückspiel zu nutzen, um negative Emotionen zu modulieren, entwickeln nach im Vergleich zu Männern kürzeren Konsumzeiten ein süchtiges Verhalten und weisen höhere Rückfalltendenzen auf. Ein Ansatz die auch bei SND und IGD 2 beobachteten geschlechtsspezifischen Unterschiede zu erklären, sind die bereits in substanzbasierten Süchten und in der Glücksspielsucht (die bereits als Verhaltenssucht klassifiziert ist) beobachteten Effekte der weiblichen Sexualhormone Östrogen und Progesteron auf das Suchtverhalten und das mesolimbische Dopaminsystem. Östrogen hat bei Frauen und weiblichen Individuen eine das Suchtverhalten (Eskalation, Craving u.a.) fördernde Wirkung. Progesteron hingegen scheint in beiden Geschlechtern eine das Suchtverhalten hemmende Wirkung zu haben. Erklärt werden diese Effekte unter anderem über den Einfluss der Hormone auf das dopaminerge Belohnungssystem und GABA-erge Synapsen. Ziel dieser Arbeit ist es zu untersuchen, ob die von anderen Autoren beobachteten Geschlechterunterschiede bezüglich IGD und SND reproduziert werden können und ob ein Zusammenhang zwischen den Östrogen- und Progesteronkonzentrationen im Serum und der IGD und SND besteht. Methoden: Über einen Zeitraum von drei Jahren wurden insgesamt 192 Probanden rekrutiert (99 Frauen und 93 Männer). Die Rekrutierung erfolgte in drei Schritten. Im Onlinescreening (1), Telefonscreening (2) und der Testung vor Ort in der Psychiatrischen und Psychotherapeutischen Klinik (3). Es erfolgte jeweils die Erhebung des Internetnutzungsverhaltens mittels DSM-5 Kriterien für IGD und von uns adaptiert für SND, der Compulsive Internet Use Scale (CIUS), der Internetnutzungszeiten und ein vor Ort durchgeführtes Craving Experiment. Die Ausschlusskriterien waren u.a. Substanzsüchte, Hinweise auf eine Schizophrenie oder schwere körperliche Erkrankungen. Die Testung vor Ort und die Blutentnahme fanden immer zur gleichen Zeit zwischen 09:00 und 12:00 Uhr am Vormittag statt. Die Bestimmung der Hormone im Serum erfolgte nach Abschluss der Rekrutierung für alle Proben zum gleichen Zeitpunkt. Für die verschiedenen Formen des Estradiols Gesamtestradiol (E2), freies Estradiol (fE2) und bioverfügbares Estradiol (bE2) wurde eine ELISA und für Progesteron die Massenspektrometrie verwendet. Ergebnisse und Beobachtungen: Für die Auswertung der Hormonkonzentrationen im Serum erfolgte die Einteilung der Kohorte in drei Gruppen: Frauen mit hormoneller Kontrazeption (FmH), Frauen ohne hormonelle Kontrazeption (FoH) und Männer. Frauen beider Gruppen erfüllten mehr SND-Kriterien (Männer vs. FmH p = 0,016; Männer vs. FoH p = 0,001), wählten signifikant häufiger SNSs für das Craving-Experiment (Männer vs. FmH p < 0,001; Männer vs. FoH p < 0,001) und FoH verbrachten signifikant mehr Zeit in SNSs (Männer vs. FoH p = 0,004) als Männer. Diese hingegen erfüllten mehr IGD-Kriterien (Männer vs. FmH p = 0,001; Männer vs. FoH p = 0,002) wählten häufiger das Internetspielen für das Craving-Experiment (Männer vs. FmH p < 0,001; Männer vs. FoH p < 0,001) und verbrachten signifikant mehr Zeit mit dem Spielen im Internet als Frauen (Männer vs. FmH p < 0,001; Männer vs. FoH p < 0,001). Insgesamt erfüllten wenige Proband:innen fünf oder mehr IGD-/ bzw. SND-Kriterien. Die 3 Werte für Östrogen und Progesteron waren bei FoH am höchsten und bei FmH am niedrigsten, die Männer lagen dazwischen. Die Estradiolkonzentrationen bei den Männern lagen über den angenommenen Referenzwerten. Bei Männern korrelierte die Estradiolkonzentration signifikant negativ mit den erfüllten SND-Kriterien (bE2) (p = -0,046) und dem maximalen Craving für das Spielen im Internet in den letzten 7 Tagen (E2, fE2, bE2) (p = -0,013; -0,008; -0,007). In der Gruppe FmH zeigte sich eine signifikant positive Korrelation der Estradiolkonzentrationen und der maximalen mit dem Spielen im Internet verbrachten Zeit (p = 0,049), sowie eine signifikant positive Korrelation der Progesteronkonzentration und den erreichten Punkten in der CIUS (p = 0,027). FoH wiesen keinerlei signifikante Korrelationen der untersuchten Parameter auf. Praktische Schlussfolgerungen: Die bereits durch andere Autoren beschriebenen Geschlechterunterschiede bezüglich IGD und SND konnten reproduziert werden. Die Männer erfüllten mehr IGD-Kriterien und verbrachten mehr Zeit mit dem Internetspielen, während die Frauen mehr SND-Kriterien erfüllten und mehr Zeit mit SNSs verbrachten. Höhere Estradiolkonzentrationen scheinen bei Männern im Hinblick auf SND und Craving für Internetspielen protektiv zu sein, während sie bei FmH mit längeren maximalen Internetspielzeiten korrelieren. FoH zeigten in unserer Kohorte keinen Zusammenhang zwischen den Serumkonzentrationen von Östrogen und Progesteron und der IGD bzw. SND. Ursächlich hierfür können unter anderem die geringe Stichprobengröße und die Schwankungen der Hormonkonzentrationen während des Zyklus in dieser Gruppe sein. Insgesamt werden größere Kohorten mit ≥ 5 Kriterien für IGD und SND unter gleichzeitiger Berücksichtigung des hormonellen Status bei weiblichen Probandinnen benötigt. Aufgrund der geringen Stichprobengröße und der bei multipler Testung bestehenden Gefahr falsch positiver Ergebnisse bedürfen unsere Ergebnisse der Replikation.
... Once sufficiently accustomed to them, avoidance can even induce withdrawal responses (Schultz 1998), further pushing individuals to repeat them. Due to its effects on the dopamine system, and the amount of time people spend using it, social media has been linked to addictive behaviors similar to those of substance and gambling addictions (He et al. 2017;Turel et al. 2014). Via photo-based social media's integration with fitness, unlimited supplies of these pleasures of novelty have become accessible. ...
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Though the rise of social media has provided countless advantages and possibilities, both within and without the domain of sports, recent years have also seen some more detrimental aspects of these technologies come to light. In particular, the widespread social media culture surrounding fitness – ‘fitspiration’ – warrants attention for the way it encourages self-sexualization and -objectification, thereby epitomizing a wider issue with photo-based social media in general. Though the negative impact of fitspiration has been well documented, what is less understood are the ways it potentially impacts and molds moral psychology, and how these same aspects may come to influence digital sports subcultures more broadly. In this theoretical paper, I rely on the insights of Friedrich Nietzsche to analyze the moral significance of a culture like fitspiration becoming normalized and influential in structuring and informing self-understanding, notions of value, and how to flourish in life. Using two doctrines central to Nietzsche’s philosophy—The Last Man and his conception of the ’higher self’ – I argue that fitspiration involves a form of hedonism that is potentially harmful to the pursuit and achievement of human flourishing. Through fitspiration, desire is elevated to a central moral principle, underlying the way users both consume and produce its content, catering simultaneously to their desires for external validation and instant gratification. It thereby creates conditions which foster a culture in adherence to the ethos of The Last Man. In doing so, I argue it impedes the cultivation of the virtues and higher values which define the higher individual, regarded by Nietzsche as essential for human flourishing. However, drawing on the ethical framework of the higher individual provides the philosophical and psychological resources with which resisting and overcoming the more harmful temptations of these trends may be possible.
... Prior studies of addiction have mentioned that boredom proneness can result in some adverse outcomes, such as social media overload. Social media become a kind of pathological pursuit for users to relieve their boredom proneness, thus resulting in the issue of overload (Turel et al., 2014(Turel et al., , 2019. Many prior studies have explained why people overuse social media, and boredom proneness is a significant influencing factor recognized by scholars; seeking out entertainment and killing time are powerful predictive factors for the use of social media platforms (Quan-Haase and Young, 2010; Ku et al., 2013). ...
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Features and relevant services of online social media have been attracting users during the COVID-19 pandemic. Previous studies have shown that college students tend to use social media more frequently than other groups. However, in being affected by social media overload, the social media use behaviors of many college students have been out of their control in terms of their capabilities or cognition. Based on the stressor–strain–outcome (SSO) model and the theory of compensatory internet use (TCIU), we developed a research model to study the causes of social media overload and its impact on college students’ academic performance during the COVID-19 pandemic. A total of 441 valid responses from college students through questionnaires in China are collected via purposive sampling and used in the data analysis. This study conducts PLS-SEM to analyze collected data, finding that boredom proneness is associated with overload (stress), which has a bearing on social media overload (strain) and the reduction in final performance (outcome). Through illustrating the psychological and behavioral conditions that hinder the academic performance of students, this study provides deeper insights into students’ uncontrollable use of social media. Moreover, with respect to the identified antecedents, this study aims to find solutions to mitigate the impact of social media overload resulting from boredom proneness on the academic performance of college students.
... For example, Cheng and Liu found that internet addiction subjects had decreased negative functional connectivity (FC) between the dorsolateral prefrontal cortex (DLPFC) and amygdala [83], which is responsible for emotion-cognition interactions [84]. Turel et al. showed that Facebook users with addiction-like symptoms have a hyperactive amygdalastriatal system [85]. He et al. found that social network site addiction is associated with a more impulsive brain 22:1537 system, manifested through reduced gray matter volumes in the amygdala bilaterally [86]. ...
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Both the rate of mobile phone addiction and suicidality among adolescents have increased during the pandemic lockdown. However, the relationship between mobile phone addiction and suicide risk and the underlying psychological mechanisms remains unknown. This study examined the associations between mobile phone addiction in adolescents during the first month of lockdown and the suicide risk in the subsequent five months. A two-wave short-term longitudinal web-based survey was conducted on 1609 senior high school students (mean age = 16.53 years, SD = 0.97 years; 63.5% female). At Time 1 (T1), the severity of mobile phone addiction and basic demographic information was collected from Feb 24 to 28, 2020 in Sichuan Province, China (at the pandemic’s peak). Five months later, between July 11 and July 23 (Time 2, T2), mobile phone addiction, daytime sleepiness, depression, and suicidality were measured within the past five months. The regression analysis revealed that mobile phone addiction during quarantine directly predicted suicidality within the next five months, even after controlling for the effect of depression and daytime sleepiness. Meanwhile, mobile phone addiction at T1 also indirectly predicted suicidality at T2, with depression and daytime sleepiness mediating this association. Programs targeting improvement of daytime sleepiness and depressive symptoms may be particularly effective in reducing suicide risk among adolescents with mobile phone addiction.
... While LaRose et al. (2003) agree that the term "addiction" is less suited to describe problematic media consumption and "dependence" might be a more fitting notation, they argue that the term "addiction" may be abused to generate a sense of urgency about psychological problems (LaRose, Eastin, 2003). In contrast to that, using the term "addiction" in such a manner may reduce serious addictions in relative importance while falsely labeling aspects of everyday life as pathological (Billieux et al. 2015;Gerlach and Cenfetelli 2020;Turel et al. 2014). ...
Conference Paper
Excessive use of IT can lead to severe negative consequences for the individuals involved. Many studies addressing this important topic have, however, used imprecise umbrella terms, such as excessive use, compulsive use, and addiction, leading to wrong conclusions. We build on the theory of IT-mediated state-tracking to examine smartphone habits, which are unaligned with users' goals (unwanted habits), enabling a discussion void of normative labeling. We address the role of self-regulation, including goal-setting strategies, in preventing the execution of these unwanted habits. To do so, we use a mixed-method approach building on longitudinal real-world smartphone use data combined with information about users' self-regulation abilities and goal-alignment of their state-tracking habits. We find that, while self-regulation does not affect whether a goal to reduce an unwanted habit is achieved, the number of set goals is lower for users high in self-regulation, and these users combine multiple strategies to achieve goals.
... Pero hay una prueba definitiva de que la premisa según la cual la adicción se explica por el efecto directo de las sustancias sobre el sistema nervioso es falsa: las denominadas adicciones comportamentales comparten un patrón común de 'alteraciones' con las adicciones a sustancias Horvath et al., 2020;Qin et al., 2020;Seok y Sohn, 2015;Schmitgen et al., 2020;Turel et al., 2014;Yao et al., 2017). ¿Qué 'sustancia' explica tales similitudes? ...
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El Modelo de Enfermedad Cerebral de la Adicción (BDMA, por sus siglas en inglés) es el paradigma dominante desde su proclamación oficial por el National Institute of Drug Addiction (NIDA) hace ahora un cuarto de siglo. Sin embargo, todos sus principios han sido falsados en reiteradas ocasiones y ninguno de los beneficios propuestos por sus autores ha sido alcanzado. Su vigencia se sustenta en el apoyo incondicional de la industria farmacéutica y en el manejo de fondos que el NIDA destina prioritariamente a estudios que verifican sus hipótesis. Siguiendo a Popper, el procedimiento correcto no es el verificacionismo, sino el principio de falsación, que obliga a desechar las hipótesis refutadas. Y, siguiendo a Kuhn, cuando un paradigma científico no cumple los requerimientos debe ser sustituido por otro que supere al desechado. Este artículo repasa las inconsistencias del BDMA y las falacias en las que se ha sustentado su hegemonía, ahora firmemente cuestionada. Paradigma, Verificacionismo, Falsacionismo, Modelo de enfermedad cerebral de la adicción, Modelo biopsicosocial, Medicalización, Conductas adictivas. Palabras clave 47 (1) 90-117. 2022 Resumen Cómo citar este artículo/citation: Pedrero Pérez, E. J. (2022). El necesario cambio de paradigma en el estudio de la adicción: inconsistencias y falacias del modelo de enfermedad cerebral de la adicción. Revista Española de Drogodependencias, 47(1), 90-117. https://doi.
Purpose This study aims to investigate the antecedents and outcomes of excessive use of personal social media at work. The prevalence of personal social media in the work environment can easily lead to excessive use and negative consequences. Understanding the predictive factors and negative consequences of employees' excessive use of personal social media at work is important to develop their appropriate use of social media and improve their job performance. Design/methodology/approach Based on dual-system theory and the person-environment fit model, this study develops a research model to examine the effect of habit and self-regulation on excessive use of personal social media at work and that of the outcomes of excessive use on employee job performance through strain. This study conducts a questionnaire survey on 408 employees to test the research model and hypotheses empirically. Findings Results suggest that the imbalance between habit and self-regulation drives excessive personal social media use of employees at work. Furthermore, excessive use of personal social media has a strong impact on employee strain, which can significantly decrease job performance. Originality/value First, this study considers excessive use of personal social media at work as a result of two different cognitive systems, that is, an automatic system and a controlled system, thereby extending the dual-system theory to explain excessive use of personal social media in the work context. Second, unlike previous studies that focused on the outcomes or explored the antecedents of excessive social media use at work respectively, the study employs the person-environment fit model and examines the systematic influence of excessive social media use at work from a broad perspective by linking its antecedents and outcomes.
The immense popularity of social networks such as Facebook has led to concerns about their potentially addictive nature and the ways in which they may be negatively affecting users, especially adolescents. However, despite the fact that “Facebook addiction” and “social media addiction” have become common terms in the media and social dialogue, the empirical evidence at this time does not support the existence of such a psychological affliction for several reasons: (1) The majority of studies on social media addiction are correlational and use self-report questionnaires which are not suitable for diagnosis; (2) Most studies employ non-standardized measures, cut-off scores, and criteria, and (3) There is an absence of case studies, experimental studies, longitudinal studies, and clinical studies in the field. Social interaction is a fundamental human need which social networks facilitate. Therefore, their widespread appeal is understandable. However, although an addiction to social media might not exist, there are still various problems that have been associated with social media use, including lower self-esteem, Fear of Missing Out (FOMO), bullying, anxiety, and depression, among others. In this chapter, we review the research on social media addiction, analyze how it fulfills the psychological criteria that define a true addiction, discuss the various problems associated with social media use outside of the addiction framework, and explore how these problems develop as well as look at potential treatments and prevention strategies for them.KeywordsSocial media sitesFacebookAddictionFOMO
The main objective of this chapter is to gain an in-depth understanding of the social media addiction construct. For this purpose, prior studies on social media addiction are reviewed. Based on this review the influence of several personal, social, and situational factors on social media addiction are examined. Firstly, personal factors such as demographic characteristics, personality traits, self-esteem, well-being, loneliness, anxiety, and depression are studied for their impact on social media addiction. Next, the social correlates and consequents of social media addiction are identified, namely need for affiliation, subjective norms, personal, professional, and academic life. Lastly, situational factors like amount of social media use and motives of use are inspected. Following the review of literature an empirical study is made to analyze factors that discriminate addicted social media users from non-addicted social media users on the basis of these different factors.
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The rise of social media raises important ethical issues regarding social media user behaviors. This study seeks to investigate the determinants of social media addiction by focusing on social capital and privacy self-efficacy. We argue that social capital has a mixed association with social media addiction by highlighting the difference between social capital bonding and social capital bridging. Notably, social media users differ in their usage purposes; as some build more bridges, others focus on bonding. Moreover, we posit that the relationship between social capital and social media addiction is moderated by social media user privacy self-efficacy. We collected the data using a survey approach and the data was analyzed using covariance-based structural equation modeling. The findings support our hypotheses. First, we found that social media users with high bridging experience lesser social media addiction. Those with high bonding have more social media addiction. Second, social media users' privacy self-efficacy moderates the relationship between social capital and social media addiction. This occurs by reinforcing the negative association between social capital bridging and social media addiction and the positive association between social capital bonding and social media addiction. Our findings provide important theoretical contributions and implications for practice.
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Resumen Abstract Alcohol consumption is often associated with violence against women. The aim of this paper is to analyze the relationship between alcohol and other relevant variables in the intervention with men convicted of intimate partner violence, both at the individual and contextual spheres. Clinical symptomatology, Drug abuse, Impulsivity, Self-esteem, Assumption of responsibility, Intimate support perception, Social rejection perception, Accumulation of stressful life events, Income perception and Social support in community are assessed in a sample of 291 participants in an intervention program for men condemned for intimate partner violence. Data were analyzed using bivariate correlations and ANOVAs. Statistically significant differences were obtained among Risk consumers and Not risk consumers in Clinical symptomatology, Drug abuse, Impulsivity, Self-esteem and Attribution of blame to personal context as individual variables and Intimate support perception, Social rejection and Accumulation of stressful life events as contextual variables. Results of previous work are confirmed and the importance of considering social factors in the participants' environment when considering decreasing alcohol consumption and intimate partner violence is demonstrated. New tools for enhancing interventions in rehabilitation programs with men convicted for violence against women is provided.
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The authors of this article participated in a panel session at the Americas Conference on Information Systems (AMCIS) 2012 with the objective to advance knowledge in areas related to the "dark side" of information technology (IT) use in organizations. We introduced new areas of exploration and disseminated new points of view on the potentially negative impacts of IT use. We drew upon our collective research and practice-related insights in five areas that characterize the dark side of IT use, namely-IT-usage-related stress, work overload, interruptions, addiction, and misuse. These are clearly important areas to examine, given that the ubiquitous and functionally pervasive nature of IT use in organizations is expected to expose users to ever greater levels of conditions that are potent for experiencing this dark side. We discussed the relevance and implications of the topic to the IS research and practice communities.
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Technology addiction is a relatively new mental condition that has not yet been well integrated into mainstream MIS models. This study bridges this gap and incorporates technology addiction into technology use processes in the context of online auctions. It examines how user cognition and ultimately usage intentions toward an information technology are distorted by addiction to the technology. The findings from two empirical studies of 132 and 223 eBay users, using three different operationalizations of addiction, indicate that the level of online auction addiction distorts the way the IT artifact is perceived. Informing a range of cognitionmodification processes, addiction to online auctions augments user perceptions of enjoyment, usefulness, and ease of use attributed to the technology, which in turn influence usage intentions. Overall, consistent with behavioral addiction models, the findings indicate that users ' levels of online auction addiction influence their reasoned IT usage decisions by altering users ' belief systems. The formation of maladaptive perceptions is driven by a combination of memory-, learning-, and bias-based cognition modification processes. Implications of the findings are discussed.
This paper presents a biopsychological theory of drug addiction, the 'Incentive-Sensitization Theory'. The theory addresses three fundamental questions. The first is: why do addicts crave drugs? That is, what is the psychological and neurobiological basis of drug craving? The second is: why does drug craving persist even after long periods of abstinence? The third is whether 'wanting' drugs (drug craving) is attributable to 'liking' drugs (to the subjective pleasurable effects of drugs)? The theory posits the following. (1) Addictive drugs share the ability to enhance mesotelencephalic dopamine neurotransmission. (2) One psychological function of this neural system is to attribute 'incentive salience' to the perception and mental representation of events associated with activation of the system. Incentive salience is a psychological process that transforms the perception of stimuli, imbuing them with salience, making them attractive, 'wanted', incentive stimuli. (3) In some individuals the repeated use of addictive drugs produces incremental neuroadaptations in this neural system, rendering it increasingly and perhaps permanently, hypersensitive ('sensitized') to drugs and drug-associated stimuli. The sensitization of dopamine systems is gated by associative learning, which causes excessive incentive salience to be attributed to the act of drug taking and to stimuli associated with drug taking. It is specifically the sensitization of incentive salience, therefore, that transforms ordinary 'wanting' into excessive drug craving. (4) It is further proposed that sensitization of the neural systems responsible for incentive salience ('for wanting') can occur independently of changes in neural systems that mediate the subjective pleasurable effects of drugs (drug 'liking') and of neural systems that mediate withdrawal. Thus, sensitization of incentive salience can produce addictive behavior (compulsive drug seeking and drug taking) even if the expectation of drug pleasure or the aversive properties of withdrawal are diminished and even in the face of strong disincentives, including the loss of reputation, job, home and family. We review evidence for this view of addiction and discuss its implications for understanding the psychology and neurobiology of addiction.
This study investigated the prevalence of Internet addiction among South Korean adolescents and explored family factors associated with such addiction. The study participants were middle and high school students residing in Seoul. One-tenth (10.7%) of the 903 adolescents surveyed scored at least 70 on the Internet Addiction Scale. These youths were considered at high risk for Internet addiction and in need of further assessment and intervention. Results show that parenting attitudes, family communication, family cohesion, and family violence exposure (e.g., conjugal violence and parent-to-child violence) were associated with Internet addiction. These findings indicate that families play an important role in preventing Internet addiction and must be considered when programs are developed to minimize excessive Internet usage by high-risk adolescents.