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In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus. The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the putamen and the insula.
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Neural Correlates of Hate
Semir Zeki*, John Paul Romaya
Wellcome Laboratory of Neurobiology, Department of Cell and Developmental Biology, University College London, London, United Kingdom
In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI
study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of
acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and
this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity
in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial
insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right
premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus.
The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the
pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the
putamen and the insula.
Citation: Zeki S, Romaya JP (2008) Neural Correlates of Hate. PLoS ONE 3(10): e3556. doi:10.1371/journal.pone.0003556
Editor: Jan Lauwereyns, Victoria University of Wellington, New Zealand
Received August 27, 2008; Accepted October 7, 2008; Published October 29, 2008
Copyright: ß2008 Zeki et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The work was supported by the Wellcome Trust, London, UK. The funding body had no role in the design, execution or analysis of the study.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail:
In pursuing our studies of the affective states generated by visual
inputs, we concentrate in this work on the sentiment of hate. Like
romantic and maternal love which we reported on previously
(Bartels and Zeki 2001, 2004) [1,2], hate is a complex biological
sentiment which throughout history has impelled individuals to
heroic as well as evil deeds. Unlike romantic love, it need not be
directed against an individual; it may instead assume many
varieties, being directed against an individual, a society, or an
ethnic group. In this study, we were interested to explore the
neural correlates of hate directed against an individual. There are
varieties even within such a confine. The hatred may be directed
against a public figure or a personally known individual, for a
variety of reasons. We made no attempt to distinguish between
different types of personal hatred. Instead, we recruited subjects
through advertisements, asking them only to volunteer if they
experienced an intense enough hate for an individual, without
distinguishing further between different categories of individual
hate. We conformed as much as possible to our previous studies on
romantic and maternal love, asking subjects to complete a
questionnaire which allowed us to correlate the declared subjective
experiences with changes in the blood oxygen level dependent
(BOLD) signal. We hypothesized that the pattern of activity
generated by viewing the face of a hated person would be quite
distinct from that produced by viewing the face of a lover. In
particular, we did not anticipate activation of the brain’s reward
system but believed that it would result in a different pattern of
activity within the emotional brain. Given the common association
between love and hate, and the relative frequency with which one
of these sentiments can transform into the other, we also
hypothesized that there would be some strong correlation in the
brain sites activated during the experience of these two antipodean
sentiments. The results surprised us.
Materials and Methods
17 healthy subjects (10 male, 12 right handed, mean age 34.8
years) were recruited through advertisements. Informed written
consent was obtained from all participants and the study was
approved by the joint Research Ethics Committee for the National
Hospital for Neurology and Neurosurgery and the Institute of
Neurology. Only subjects expressing a strong hatred for an
individual were selected. With one exception, all our subjects
testified to the hatred of an individual, either an ex-lover or a
competitor at work. The one exception was a female who
expressed an intense hatred of a very famous political figure.
During a primary visit to the laboratory, some two weeks prior to
scanning, each subject provided picture portraits of the hated
person and of three other people of the same sex towards whom
they had neutral feelings, all pictures being matched as far as
possible for expression and general appearance. The nature of the
experiment was explained to the subject and an example stimulus
using random anonymous faces was demonstrated. Subjects also
completed a questionnaire during the first session to assess their
feelings about the hated person and obtain a hate score which
could subsequently be used as a covariate in the second level
analysis (see below).
Once during the first visit and once directly after the scanning
session, we tried to assess each subject’s feelings about the hated
person. We did so by asking them to complete a score sheet that
we devised, the Passionate Hate Scale (PHS). This has a certain
similarity to the Passionate Love Scale (Hatfield and Sprecher
1986 [3]) that we used in our study of romantic love. We based the
PHS partially on Sternberg’s (2004) [4] triangular theory of hate
and on the assumption, derived from numerous studies, that there
is a good correlation between declared subjective mental states,
including emotional ones, and the observed BOLD signal (e.g.
Kawabata and Zeki 2004) [5]. The questionnaire revolved around
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three elements of hate: (a) negation of intimacy, when an
individual seeks a distance from the hated person. This is usually
because the hated person arouses feeling of revulsion and disgust,
exactly the opposite of the desire for greater intimacy in the
context of love; (b) passion, expressing itself in intense anger at,
and fear of, the hated person; and (c) devaluation of the hated
person through expressions of contempt. Three negative state-
ments and one positive statement were constructed around each of
these elements. Subjects were invited to indicate their level of
agreement with each statement, the questions being presented in a
different random order on both occasions (for full details see
Supporting Information File S1: Hate Questionnaire). The
questionnaire could yield a ‘‘hate score’’ ranging from 0 (minimum
hate) to 72 (maximum hate).
Stimuli were generated using Cogent 2000 and Cogent Graphics
(, http://www.vislab.ucl. Four images provided by each subject
were digitised and an image-editing program (AdobeHPhotoshopH
CS2) was used to remove any superfluous features such as earrings,
scarves etc. The background detail was replaced with a flat mid-grey
tone and the images were normalised in terms of spatial frequency,
visual area, average brightness and contrast (see Figure 1). Full
details on the preparation of the stimuli are given in Supporting
Information file S1: Processing of face images.
Each subject was exposed to either two or three identical
stimulus sessions. The session began with a flat grey background
(intensity 9.06 cd/m
) (blank condition) which was present for 20 s
during which the first six brain volumes were discarded to allow
T1 equilibration effects to subside. A face was then presented for
16.07 s followed by another blank interval of 2.07 s. Occasionally,
a blank condition of 16.07 s was displayed instead of a face, to
increase the proportion of baseline acquisition during the scanning
session. Subjects were instructed to press a key each time a face
disappeared. The faces and blanks were presented in a pseudo-
random, symmetrically balanced sequence (see Supporting
Information file S1:Stimulus Design). The session ended with a
terminal blank period of 30 s, during which the scanner continued
to acquire decaying BOLD signal. A block design incorporating
null events with ca. 16 s epochs was chosen for direct comparison
with our previous studies on romantic and maternal love [1,2].
Scanning details
Scanning was done in a 1.5-T Siemens Magneton Sonata MRI
scanner fitted with a head volume coil (Siemens, Erlangen,
Germany) to which an angled mirror was attached, allowing
subjects to view a screen onto which stimuli were projected using
an LCD projector. An echo-planar imaging (EPI) sequence was
applied for functional scans, measuring BOLD signals (echo time
TE = 50 mS, repeat time TR = 90 ms, volume time 3.42 s). Each
brain image was acquired in a descending sequence comprising 38
axial slices each 2 mm thick with an interstitial gap of 1 mm and a
voxel resolution of 36363 mm, covering nearly the whole brain.
After functional scanning had been completed a T1 mdeft
anatomical scan was acquired in the saggital plane to obtain a
high resolution structural image (176 slices per volume, constant
isotropic resolution 16161 mm, TE = 3.56 s, TR = 12.24 s).
Data were analysed using SPM5 [6] (Statistical Parametric
Mapping V5 The time series of
functional brain volume images for each subject was realigned and
normalized into MNI (Montreal Neurological Institute) space [7]
and then smoothed using a Gaussian smoothing kernel of
96969 mm.
The stimulus for each subject was modelled as a set of regressors
in the SPM5 general linear model (GLM) (first-level) analysis. The
stimulus was a block design and boxcar functions were used to
define regressors which modelled the onsets and durations of the
appearances of each of the neutral faces and the hated face.
Keypresses were modelled as delta functions in an additional
regressor. Head movement parameters calculated from the
realignment pre-processing step were included as regressors of
no interest. Regressors were convolved with the default SPM5
canonical Hemodynamic Response Function (HRF) and estimated
using classical ReML (Restricted Maximum Likelihood).
The resultant parameter estimates for each regressor (at each
voxel) were compared using t-tests to establish the significance of
differences in activation between conditions. The main effects of
interest were Hated face.Neutral faces and Neutral faces.Hated face.
We were also interested in All faces.Baseline. Contrast images for
these effects for each subject were entered into a random effects
(second-level) analysis, reported below; this included the PHS
score for each subject as a covariate.
Figure 1. An example set of four processed face images (faces not from this study). The images are converted to greyscale and normalised
with respect to visual area and average brightness. They are roughly matched in terms of spatial frequency and intensity contrast. The faces are all of
the same sex, the expressions are similar and a vertically aligned full face image has been selected in each case. An individual set of four such faces
was presented to each subject. One of the faces was of a person hated by that particular subject, the other three faces were known to the subject,
but were of a neutral relationship, neither loved nor hated.
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Unless otherwise stated, we report probabilities at significance
p#0.05 which have been corrected family-wise for multiple
comparisons using Gaussian random field theory [8], either over
the whole brain, or restricted to a defined search volume. Some
activations were significant only at cluster level; in these cases the
underlying voxel-level probability threshold which generated the
clusters is always p,0.00025 (uncorrected) and this is the
displayed threshold in all figures. Voxel co-ordinates are quoted
in millimetres in MNI space.
Activations with faces
Given that we are dealing in this work with a sentiment that has
not been studied before, we used the contrast All faces.Baseline to
learn whether it revealed activity in the part of the fusiform gyrus
that has been implicated in the perception of faces, and thus
validate the activity produced by the main contrast (Hated
faces.Neutral faces) (Figure 2). The contrast led to activity in the
fusiform face area at (a) (39, 248, 218), almost identical to the
locus that has been pinpointed in previous studies of face
perception. In addition, it produced activity bilaterally elsewhere
in the fusiform gyrus, at (b) (42, 281, 215) and (c)(242, 281,
212), close to the visual motion area, V5. Activity in the latter
area has been observed in other studies that have used faces in
imaging experiments (e.g. Hadjikhani et al 2008 [11]).
Activation with hated faces
Our principal interest was to learn whether there are any
cortical areas that are especially active in the contrast Hated
face.Neutral face. Across all seventeen subjects, there was a voxel
level activation in the medial frontal gyrus (at 6, 9, 60) (Figure 3).
This was part of a cluster of 269 voxels (with an underlying voxel-
level threshold of p,0.00025). In addition, there were 6
activations significant at the cluster level. The maximally
significant voxel in each cluster was located as follows: (a) the
right putamen (at 24, 0, 12); (b,c) bilaterally in the premotor
cortex at (45, 3, 39) and (239, 3, 45); (d) in the frontal pole (at
Figure 2. Activations for the contrast
All Faces.Baseline
.Reported probabilities at voxel level are corrected family-wise for multiple
comparisons over the whole brain volume.
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215, 57, 27) and (e,f) bilaterally in the medial insula (at 51, 12,
26 and 248, 9, 0) (see Figure 4)
We wanted, next, to learn whether there was a relationship
between brain activity and the degree of hate as determined from the
total scores obtained from the PHS. To do so, we entered the hate
questionnaire score for each subject as a covariate in the GLM for the
second level analysis. A search volume of 5,225 voxels was defined
using the t-statistic for the effect Hated face.Neutral faces (a contrast
orthogonal to the PHS covariate) with an uncorrected statistical
threshold of p#0.01. Within this search volume there were three
voxels where the effect Hated face.Neutral face co-varied linearly and
significantly (p
(search vol.)
#0.05) with the hate questionnaire score.
They were: (a) in the right insula at (51, 9, 26), (b)intheright
premotor cortex at (39, 26, 60) and (c) the right fronto-medial gyrus
(6, 15, 45) (Figure 5). Voxel (a) lies within the cluster in Figure 4e;
voxel (c) lies within the cluster shown in Figure 3. The locus of activity
in the right premotor cortex was 23 mm from the maximally active
voxel in Figure 4b. In all three loci, the change in parameter estimates
was directly proportional to the hate score (Figure 5, right).
No voxels were found which showed a second order polynomial
(quadratic) relationship with the hate score, nor were any
significantly covarying voxels found in the voxels excluded by
the search volume.
Deactivations with hated faces
In the contrast Neutral faces.Hated Faces, there was a cluster-level
deactivation with a maximally significant voxel situated in the
right superior frontal gyrus at (33, 18, 57) (Figure 6).
To simplify our task in approaching so complex a sentiment, we
concentrated on the sentiment of hate directed against an
individual. Even within such a limit, the problem has many facets
that this initial study could not address. Hatred against an
individual may be seemingly irrational and rooted in remote
anthropological instincts. Hate based on race or religion would
probably fall under this heading. On the other hand, an individual
may trace the hatred to a past injustice and hence find a justifiable
source for it. There are no doubt many other ways in which the
sentiment can be sub-categorized. But it seemed to us that
concentrating on individual hate, regardless of the categories to
which it could potentially be assigned, had the merit of revealing at
least a basic network in the brain and thus acting as a template for
future, more specialized and sophisticated studies.
Our studies did indeed reveal a basic pattern. As far as we can
determine, it is unique to the sentiment of hate even though
individual sites within it have been shown to be active in other
conditions that are related to hate. The network has components
that have been considered to be important in (a) generating
aggressive behavior and (b) translating this behavior into motor
action through motor planning. Finally, and most intriguingly, the
network involves regions of the putamen and the insula that are
almost identical to the ones activated by passionate, romantic, love.
It is important to note that the pattern revealed is distinct from
that of other, closely related, emotions such as fear, anger,
aggression and danger, even though it shares common areas with
these other sentiments. Thus, the amygdala which is strongly
activated by fear (Noesselt et al. 2005 [9], Morris et al. 2002 [10],
Hadjikhani et al. 2008 [11]) and by aggression (Beaver et al., 2008
[12]) was not activated in our study. Nor were the anterior
cingulate, hippocampus, medial temporal regions, and orbitofron-
tal cortex, apparently conspicuous in anger and threat (Denson et
al. 2008 [13]; Bufkin and Luttrell 2007 [14]; McClure et al. 2004
[15]), evident in our study. It would thus seem that, though these
sentiments may constitute part of the behaviour that results from
hatred, the neural pathways for hate are distinct.
One region of activation in our study, involving multiple foci,
lies in the frontal cortex, both medially and laterally. Numerous
studies have activated one part or another of this relatively large
expanse of cortex. What seems not to be in doubt is that this
cortical zone involves the premotor cortex, a zone that has been
implicated in the preparation of motor planning and its execution
(Hanakawa et al. 2008) [16]. We hypothesize that the sight of a
hated person mobilizes the motor system for the possibility of
attack or defense. In addition, the involvement of the frontal pole
is in a location which Ramnani and Miall (2003) [17] consider to
be critical in predicting the action of others, arguably an important
feature when confronted by a hated person. Another forebrain site
that was active in our study and which has been implicated in
motor planning, though seemingly in an affective context, is the
right putamen, a structure that has also been implicated in the
perception of contempt and disgust (Phillips et al. 1998 [18];
Sprengelmeyer et al. 1998 [19]; Sambataro et al, 2006 [20];
Thielscher and Pessoa 2007 [21]) and fear (Surguadze 2003 [22]),
possibly within an aggressive context since dopamine turnover
Figure 3. Activation for the contrast
Hated faces.Neutral faces
.The reported voxel-level probability has been corrected family-wise for
multiple comparisons over the whole brain volume.
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level is apparently higher in the putamen of aggressive mice
(Tizabi et al., 1980 [23]). Moreover, damage to the putamen and
insula apparently compromises a patient’s ability to recognize
signals of disgust (Calder et al. 2000 [24]). Animal studies suggest
that the putamen may constitute part of the motor system that is
mobilized in the context of hate. It contains neurons that are active
in phases preparatory to motor acts (Alexander and Crutcher 1990
[25]) and has been shown to be active in conditions in which
cognitive planning is required to trigger a motor act (Monchi et al
2006 [26]; Boecker et al. 2008 [27]).
We note with considerable interest that the parts of the right
putamen and the medial insula activated in this study correspond
closely to the parts activated in our earlier study of romantic love
(Bartels & Zeki 2004 [2]). The insula has been implicated in a
variety of functions and of interest in this context is its involvement
in expressions of disgust and the appraisal of disagreeable stimuli
Figure 4. Clusters of activation for the contrast
Hated face.Neutral faces
.The statistical threshold was set at p#0.05 at the cluster level,
corrected for multiple comparisons, with an underlying voxel-level threshold of p#0.00025, as displayed.
Neural Correlates Of Hate
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(Phillips et al. 1997 [28]). Reiman et al. (1997) [29] suggest that the
insula may be involved in responses to distressing sensory stimuli,
of which a hated face would be one example but there are also
conditions in which a loved face may constitute a distressing signal.
The putamen could also be involved in the planning of aggressive
motor acts within the context of romantic love – for example,
when a rival presents a danger. It is difficult in the present state of
knowledge to be more precise about the nature of the links
between the parts of the insula and putamen that are active in
these two different conditions. What is not in doubt is that there is,
in the behavioural sense, a strong link between the two sentiments
and one can easily transmute into the other.
It is noteworthy that there was a linear relationship between the
hate scores and the parameter estimates for the contrast Hated
face.Neutral faces. Two of the three activations were located within
significantly active clusters in the Hated face.Neutral face contrast,
while the third one was located in close vicinity of the active cluster
in the right premotor cortex. Such a linear relationship is of
considerable interest in adding further to the accumulating
evidence that subjective mental states can be quantified in terms
of cortical activity (see for example Elliot et al., 2003 [30] and
Knutson et al. 2001 [31]; Kawabata and Zeki 2004 [4]). The
general pattern is also similar to other studies of subjective mental
states, in that activity in only some of the areas is linearly related to
declared subjective mental states.
Equally interesting was the observed pattern of deactivation.
Unlike the study of romantic love, when we observed a
deactivation pattern that included frontal, temporal and parietal
Figure 5. Voxels covarying with hate questionnaire score. A search volume of 5,225 voxels was defined using the t-statistic for the effect
Hated face.Neutral faces with the statistical threshold set at p#0.01 (uncorrected). Within this search volume voxels were identified where the effect
Hated face.Neutral faces covaried with the hate questionnaire score. The voxel-level statistical threshold was set at p#0.05, family-wise corrected for
multiple comparisons within the search volume . The graphs in the right hand column plot the parameter estimate of Hated face.Neutral faces
against the questionnaire score at each voxel.
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regions of the cerebral cortex (Bartels & Zeki 2000 [1]), the
deactivation pattern observed in this study was much more
restricted. It involved the right superior frontal gyrus. The
deactivated locus in the frontal cortex is close in position to the
one which previous studies had shown to be negatively correlated
with obsessive-compulsive states (McGuire, Bench, Frith, Marks et
al. 1994 [32]), a deactivation hypothesized to relate to a shift in
attention from extrapersonal space to an internal experience
associated with anxiety.
This difference in the extent of deactivated cortex, compared to
the deactivated cortex in the context of romantic love, may seem
surprising, since hate too can be an all consuming passion. But
whereas in romantic love, the lover is more likely to be less critical
and judgmental regarding the loved person, it is more likely that in
the context of hate the hater may want to exercise judgment in
calculating moves to harm, injure or otherwise extract revenge.
In summary, our results show that there is a unique pattern of
activity in the brain in the context of hate. This pattern, while
being distinct from that obtained in the context of romantic love,
nevertheless shares two areas with the latter, namely the putamen
and the insula. This linkage may account for why love and hate
are so closely linked to each other in life.
Supporting Information
File S1 Hate Questionnaire
Found at: doi:10.1371/journal.pone.0003556.s001 (0.04 MB
We are especially grateful to Karl Friston, Chris Frith and Ray Dolan for
their suggestions and for commenting on the manuscript.
Author Contributions
Conceived and designed the experiments: SZ. Performed the experiments:
JPR. Analyzed the data: SZ JPR. Wrote the paper: SZ JPR.
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Neural Correlates Of Hate
PLoS ONE | 8 October 2008 | Volume 3 | Issue 10 | e3556

Supplementary resource (1)

December 2010
... Marka nefretinin bir diğer özelliği, sadece bireysel değil gruplar için de tutum ve davranışların açıklanmasında kullanılmasıdır. Başka bir ifadeyle, tek boyutlu bir duygu olarak çoğunlukla ele alınmayan ve çok karmaşık bir kavram olan nefret, "romantik aşk"tan farklı olarak sadece birine yönelik değil, bir topluma, bir gruba yönelik de gerçekleşebilmektedir (Zeki ve Romaya, 2008). Hem kolektif boyutunun bulunması hem de benlik için önemli konularda daha da öne çıkması nedeniyle, marka nefretinin taraftarlık konusunda incelenmeye uygun bir kavram olduğu düşünülmektedir. ...
... Oysa biyolojik olarak beyin aktiviteleri ele alındığında, beyinde aşk ve nefretin harekete geçirdiği ortak kısımlar bulunmaktadır. Bu nedenle, aşk ve nefretin birbiriyle zıt değil hatta yakın-ilgili kavramlar olduğu öne sürülmektedir (Zeki ve Romaya, 2008). Benzer şekilde, Rempel ve Burris (2005) de aşk ve nefretin zıt kavramlar olmadığını, hatta kimi kavramsal paralellikler barındırdığını ifade etmektedir. ...
... Bunun yanında, "aşk" ve nefretin birbiriyle zıt değil bir biçimiyle paralellikler içeren, ilgili kavramlar olmasından hareketle (Rempel ve Burris 2005;Zeki ve Romaya, 2008), Ankaragüçlülerin tribün liderlerinin Beşiktaş'a ilişkin söz konusu olumlu söylemleri, çalışmadaki şimdiye kadarki tartışma ile çelişkili olarak değerlendirilmemelidir. Bununla birlikte, elbette Ankaragücü taraftarları ile Beşiktaş kulübü ve taraftarları arasında "aşk" benzeri bir ilişkinin bulunduğu düşünülmemektedir. ...
... Although hate has been associated with some realistic threats (e.g., threats to life or security) (Baumeister, 1997;Beck, 2000), previous theorizing has clearly emphasized the role of threats to people's morality and self-concept on the development of hate (Baumeister & Butz, 2005;Beck, 2000;Fromm, 1992;Kucuk, 2016;Staub, 2011;Van Doorn, 2018). For example, hate has been described as a reaction to perceived moral violations (Van Bavel, Ray, & Cunningham, 2018), injustice (Kucuk, 2016;Van Doorn, 2018) the appraisal of the targets as dispositionally dangerous, immoral, or evil (Baumeister, 1997;Zeki & Romaya, 2008), and threats to values, self-esteem, and identity (Baumeister & Butz, 2005;Staub, 2005). ...
... Hate was measured with an adaptation of the Passionate Hate Scale (PHS) developed by Zeki and Romaya (2008). The hate scale is based in the triangular theory of hate (Sternberg & Sternberg, 2008;Sternberg., 2003), and has been used previously in experimental research by the authors. ...
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Despite growing scientific attention for hate, little is known about how perceived threats may influence hate and aggression. In four preregistered online studies (Ntotal = 1422), we test a threat – hate – aggression model, examining the differential effects of symbolic and realistic threats on the emergence of hate, and the associations between hate and specific aggressive behaviors, across interpersonal and intergroup hate targets. In Study 1 we specify models testing the threat – hate – aggression paths. In Studies 2 (interpersonal hate) and 3 and 4 (intergroup hate) we manipulate realistic and symbolic threat perceptions, measuring hate and aggression. Across studies, hate is better predicted by symbolic than realistic threats. Also, hate consistently predicts aggressive tendencies and hurting behaviors, and interpersonal hate mediates the relationship between symbolic threats and the two aggressive behaviors while intergroup hate mediates the relationships between symbolic (and partially realistic) threats and the two aggressive behaviors. We discuss the implications of our findings for hate, threat, and prejudice research.
... To measure the mediating variable -brand hate, we combined items from the scales of Hegner et al., (2017), Zeki and Romaya (2008) which is an updated version of Sternberg (2003). The construct deficit value, the negative past experience, the ideological incompatibility was adopted, justified and developed from scales of Romani et al. (2012), Thomson et al., (2012) and Hegner et al., (2017). ...
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In the era of tough competition, the customer's emotional attachment to brand plays a vital role to the successes and failures of enterprises. Specifically in the case of doing business online, brands have to cope with the troubles of rising from brand hate as brand avoidance, negative word of mouth and brand retaliation. Traditionally, the brand communication is very hard to control and with online communities, the problems tend to be even more severe. This paper aims to explore and discuss the core concept, the driven factors and the actionable consequences of brand hate among netizens. A total of 358 valid responses were obtained from surveys taken from the internet users across the nation. Partial Least Square-Structural Equation Modeling (PLS-SEM) was conducted using Smart PLS to assess the hypotheses. The result shows that the expression of brand hate among netizen consists of active hate and passive hate. Deficit value, deceptive advertising, negative past experience and ideology incompatibility have been confirmed as influencing factors on customers' brand hate emotion. Then brand hate itself causes the customer's actionable outcomes such as brand avoidance, brand negative word of mouth and brand retaliation. Along with the theoretical contributions and managerial implications have been recommended for enterprises to avoid netizens' brand hate. JEL Classification Code: M10, M30, M37 bad experiences spread much faster than good experiences. Kanouse (1984) supported this opinion by suggesting that people tend to weigh negative information more heavily than the positive information. However, according to Sternberg (2003), the literature of hate is underdeveloped and that is the reason why the topic of hate is even less studied in the domain and marketing and consumer research. Zarantonello et al. (2016) also stated that treatments of brand hate have selectively focused on narrow emotions whereas hate is a very complex emotion with several primary and secondary emotions. Basically, the emotional experiences can be divided to two main groups: the positive and the negative so that customers' emotions towards brands also have positive and negative aspects. When investigating the basic level of emotion categories, Fehr and Rusell (1984) found love and hate was the second most important emotion. Study of Shaver et al. (1987) also confirmed that hate was in the third place out of 213 emotional words. Positive aspects have been frequently discussed and examined in marketing literature as customer satisfaction, customer loyalty, brand romance, brand love, etc., yet the research about negative emotions of brand is scarce (Dalvand et al., 2019).
... An association between dorsostriatal functional networks and sleep quality is strongly supported by accumulated evidence in favor of a fundamental role of the basal ganglia in regulating the sleep-wake cycle [74]. e dorsal striatum modulates behavior [75][76][77] via connections with sensorimotor and association cortices [78,79] and is believed to enhance wakefulness [74]. us, it is possible that the greater connectivity in dorsostriatal-sensorimotor associations with worse sleep quality in the control group Note. ...
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Aging is associated with poor sleep quality and greater chronic pain prevalence, with age-related changes in brain function as potential underlying mechanisms. Objective. The following cross-sectional study aimed to determine whether self-reported chronic musculoskeletal pain in community-dwelling older adults moderates the association between sleep quality and resting state functional brain connectivity (rsFC). Methods. Community-dwelling older individuals (mean age = 73.29 years) part of the NEPAL study who completed the Pittsburg Sleep Quality Index (PSQI) and a rsFC scan were included (n = 48) in the present investigation. To that end, we tested the effect of chronic pain-by-PSQI interaction on rsFC among atlas-based brain regions-of-interest, controlling for age and sex. Results and Discussion. A significant network connecting the bilateral putamen and left caudate with bilateral precentral gyrus, postcentral gyrus, and juxtapositional lobule cortex, survived global multiple comparisons (FDR; q < 0.05) and threshold-free network-based-statistics. Greater PSQI scores were significantly associated with greater dorsostriatal-sensorimotor rsFC in the no-pain group, suggesting that a state of somatomotor hyperarousal may be associated with poorer sleep quality in this group. However, in the pain group, greater PSQI scores were associated with less dorsostriatal-sensorimotor rsFC, possibly due to a shift of striatal functions toward regulation sensorimotor aspects of the pain experience, and/or aberrant cortico-striatal loops in the presence of chronic pain. This preliminary investigation advances knowledge about the neurobiology underlying the associations between chronic pain and sleep in community-dwelling older adults that may contribute to the development of effective therapies to decrease disability in geriatric populations.
... Hate, an intense basic human emotional response, plays an important role in psychological behavior and human evolution (Halperin, 2012). The hate circuit shows an altered activation when people watch stimuli they hate (Zeki and Romaya, 2008). Disgust at its core feeling of hate has been implicated in a wide range of psychological and mental conditions (Turnell et al., 2019). ...
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Background Abnormalities of functional connectivity (FC) in certain brain regions are closely related to the pathophysiology of major depressive disorder (MDD). Findings are inconsistent with different presuppositions in regions of interest. Our research focused on voxel-wise brain-wide FC changes in patients with MDD in an unbiased manner. Method We examined resting-state functional MRI in 23 patients with MDD and 26 healthy controls. Imaging data were analyzed by using global-brain FC (GFC) and used to explore the correlation of abnormal GFC values with clinical variables. Results Increased GFC values in the left medial superior frontal gyrus (SFGmed) and decreased GFC values in the right supplementary motor area (SMA) were observed in the patients with MDD compared with the controls. The decreased GFC values in the right SMA had a positive correlation with vitamin D and Hamilton Anxiety Scale (HAM-A) scores. Conclusion Abnormal GFC in the hate circuit, particularly increased GFC in the left SFGmed and decreased GFC in the right SMA, appears to be a new sight for comprehending the pathological alterations in MDD.
... Later on, Darwin (1872) provided one of the first scientific accounts of hate, describing it as a feeling that lacks a distinct facial sign and manifests itself as rage (Royzman et al., 2005). More recently, neuroscience research has made the first steps toward mapping a hate circuit in the brain involving the putamen, the insula and the frontal cortex (Zeki & Romaya, 2008). But what is hate exactly? ...
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Is hate fundamentally different from other negative emotions? Despite a fair amount of theorizing about hate, there is little empirical evidence that helps to answer this basic question. The present research examines how people construe interpersonal and intergroup hate and provides an empirical analysis of how hate is conceptually different from dislike, anger, contempt, and disgust. In five preregistered studies, using exploratory (Pilot Study) and confirmatory (Studies 1, and 2a through 2c) within-subjects designs, we asked adult participants in the United States (Ntotal = 1,074) to describe examples of their interpersonal and intergroup targets of hate, dislike, anger, contempt, and disgust. We assessed their subjective experiences of each emotion by measuring the associated intensity, duration, arousal, valence, perceived threats, and action tendencies. Across studies, results revealed that participants feel consistently more emotionally aroused, personally threatened, and inclined toward attack-oriented behaviors when experiencing hate as compared with dislike, anger, contempt and disgust toward interpersonal targets. At the intergroup level, results revealed that participants experience hate as more arousing than the three moral emotions, more intense than dislike, anger and contempt, and feel more inclined toward attack-oriented behaviors than when they feel dislike and contempt. Results are in line with a general pattern of increasing differentiation suggesting that hate is conceptually closer to disgust and contempt than to anger and dislike. We discuss the specific differences and similarities between hate and these emotions across targets and their implications for future research on hate and negative emotions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
„Ich werde nicht hassen.“ Mit diesem Satz endet die Inszenierung am Staatsschauspiel Dresden über Izzeldin Abuelaishs Biographie, der in dem gewaltsamen Konflikt zwischen Israel und Palästina drei seiner Töchter verloren hat. Diese besondere Haltung innerhalb einer Krisensituation wird zum Ausgangspunkt und Rahmen einer Unterrichtseinheit, in der sich die Schüler*innen mit den folgenden Fragen auseinandersetzen: Was ist Hass? Ist es möglich, nicht zu hassen? Wie lässt sich Hass im Spannungsfeld zwischen der Triebnatur des Menschen und einer bewussten Haltung einordnen? Wie stehen Krisen und Orientierungsverlust mit Hass in Verbindung? Kann eine Gesellschaft krank sein? Wie hängen Izzeldins Haltung und Fromms Theorie einer produktiven Charakterorientierung zusammen und wie ist Izzeldins bzw. Fromms Orientierungsangebot vor dem Hintergrund der Krisen und Konflikte in der Lebenswelt der Schüler*innen zu beurteilen? Durch einen Aufführungsbesuch sowie theaterpädagogische Arbeitsformen soll ein Zugang zu diesem komplexen Themenfeld geschaffen werden.
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The current study develops a research model and explores the correlation between customer sense of online betrayal, brand hate, and anti-brand activism. The outrage customers’ anti-brand behaviors consist of negative online word of mouth, online public complaining, and online boycott. Data from an online survey of 383 online shoppers were used to test seven proposed hypotheses. The partial least square–structural equation modeling (PLS-SEM) was adopted to assess the measurement and structural model. The findings showed that the sense of online betrayal positively and significantly affects brand hate and anti-brand behaviors. In addition, brand hate is also the leading cause of customers’ anti-brand actions. The present study highlights the mediation role of brand hate in eliciting revenge from consumers subjected to online betrayal. This study also gives some recommendations to customers to stop the misconduct behaviors of online betrayals, such as spreading their betrayal cases to friends and relatives via social media, then asking for supports and help from governmental and legal agencies and participating in boycotts; raising boycott movements against the betraying brand should be considered as the most extreme punishment.
Background Major depressive disorder (MDD) is often accompanied with a classic diurnal mood variation (DMV) symptoms. Patients with DMV symptoms feel a mood improvement and prefer activities at dusk or in the evening, which is consistent with the evening chronotype. Their neural alterations are unclear. In this study, we aimed to explore the neuropathological mechanisms underlying the circadian rhythm of mood and the association with chronotype in MDD. Methods A total of 126 depressed patients, including 48 with DMV, 78 without, and 67 age/gender-matched healthy controls (HC) were recruited and underwent a resting-state functional magnetic resonance imaging. Spontaneous neural activity were investigated using amplitude of low-frequency fluctuation (ALFF) and region of interest (ROI)-based functional connectivity (FC) analyses were conducted. The Morningness-Eveningness Questionnaire (MEQ) was utilized to evaluate participant chronotypes and Pearson correlations were calculated between altered ALFF/FC values and MEQ scores in patients with MDD. Results Compared with NMV, DMV group exhibited lower MEQ scores, and increased ALFF values in the right orbital superior frontal gyrus (oSFG). We observed that increased FC between the left suprachiasmatic nucleus (SCN) and supramarginal gyrus (SMG). ALFF in the oSFG and FC of rSCN-SMG were negatively correlated with MEQ scores. Limitation Some people's chronotypes information is missing. Conclusion Patients with DMV tended to be evening type and with abnormal brain function in frontal lobes. The synergistic changes between frontotemporal lobe, SCN-SMG maybe the characteristic of patients with DMV symptoms.
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically,Statistical Parametric Mappingprovides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. * An essential reference and companion for users of the SPM software * Provides a complete description of the concepts and procedures entailed by the analysis of brain images * Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data * Stands as a compendium of all the advances in neuroimaging data analysis over the past decade * Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes * Structured treatment of data analysis issues that links different modalities and models * Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible.
Females appear to be more sensitive and responsive to social cues, including threat signals, than are males. Recent theoretical models suggest that developmental changes in brain functioning play important roles in the emergence of such gender differences.
The horrific events of September 11, 2001, and the terrorism that has followed in its wake have made it even more important now than in the past to understand the nature of hate. Yet psychologists have had relatively little to say about the nature of hate and its origins. Given the overwhelming displays of hate currently being displayed in the world, psychologists have a responsibility to seek an understanding of hate, its causes, and its consequences and how to combat it and achieve a culture of peace. The author's duplex theory of hate applies to both individuals and groups. Evidence suggests that the basic processing system that applies to the formation and processing of impressions about groups and about individuals is the same. The basic thesis to be presented in this chapter makes five fundamental claims: 1. Hate is very closely related psychologically to love. 2. Hate is neither the opposite of love nor the absence of love. 3. Hate, like love, has its origins in stories that characterize the target of the emotion. 4. Hate, like love, can be characterized by a triangular structure generated by these stories. 5. Hate is a major precursor of many terrorist acts, massacres, and genocides. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Recognition of facial expressions is critical to our appreciation of the social and physical environment, with separate emotions having distinct facial expressions. Perception of fearful facial expressions has been extensively studied, appearing to depend upon the amygdala. Disgust-literally 'bad taste'-is another important emotion, with a distinct evolutionary history, and is conveyed by a characteristic facial expression. We have used functional magnetic resonance imaging (fMRI) to examine the neural substrate for perceiving disgust expressions. Normal volunteers were presented with faces showing mild or strong disgust or fear. Cerebral activation in response to these stimuli was contrasted with that for neutral faces. Results for fear generally confirmed previous positron emission tomography findings of amygdala involvement. Both strong and mild expressions of disgust activated anterior insular cortex but not the amygdala; strong disgust also activated structures linked to a limbic cortico-striatal-thalamic circuit. The anterior insula is known to be involved in responses to offensive tastes. The neural response to facial expressions of disgust in others is thus closely related to appraisal of distasteful stimuli.
1. The purpose of this study was to compare the functional properties of neurons in three interrelated motor areas that have been implicated in the planning and execution of visually guided limb movements. All three structures, the supplementary motor area (SMA), primary motor cortex (MC), and the putamen, are components of the basal ganglia-thalamocortical "motor circuit." The focus of this report is on neuronal activity related to the preparation for movement. 2. Five rhesus monkeys were trained to perform a visuomotor step-tracking task in which elbow movements were made both with and without prior instruction concerning the direction of the forthcoming movement. To dissociate the direction of preparatory set (and limb movement) from the task-related patterns of tonic (and phasic) muscular activation, some trials included the application of a constant torque load that either opposed or assisted the movements required by the behavioral paradigm. Single-cell activity was recorded from the arm regions of the SMA, MC, and putamen contralateral to the working arm. 3. A total of 741 task-related neurons were studied, including 222 within the SMA, 202 within MC, and 317 within the putamen. Each area contained substantial proportions of neurons that manifested preparatory activity, i.e., cells that showed task-related changes in discharge rate during the postinstruction (preparatory) interval. The SMA contained a larger proportion of such cells (55%) than did MC (37%) or the putamen (33%). The proportion of cells showing only preparatory activity was threefold greater in the SMA (32%) than in MC (11%). In all three areas, cells that showed only preparatory activity tended to be located more rostrally than cells with movement-related activity. Within the arm region of the SMA, the distribution of sites from which movements were evoked by microstimulation showed just the opposite tendency: i.e., microexcitable sites were largely confined to the caudal half of this region. 4. The majority of cells with task-related preparatory activity showed selective activation in anticipation of elbow movements in a particular direction (SMA, 86%; MC, 87%; putamen, 78%), and in most cases the preparatory activity was found to be independent of the loading conditions (80% in SMA, 83% in MC, and 84% in putamen).(ABSTRACT TRUNCATED AT 400 WORDS)
Theorists such as Farber argue that in adolescence passionate love first appears in all its intensity. Both adolescence and passion are "intense, overwhelming, passionate, consuming, exciting, and confusing". As yet, however, clinicians have been given little guidance as to how to deal with adolescents caught up in their passionate feelings. Nor has there been much research into the nature of passionate love. In Section I of this paper, we define passionate love, explain the necessity of developing a scale to measure this concept, and review evidence as to the nature of passionate love. In Section 2, we report a series of studies conducted in developing the Passionate Love Scale (the PLS). We present evidence as to the PLS's reliability, validity, and relationship to other factors involved in close relationships. We end by describing how we have used this scale in family therapy to open conversations about the nature of passionate love/companionate love/and intimacy... and discussing profitable directions for subsequent research.