Methods and Analyses - Two more studies
Transcript of Methods and Analyses - Two more studies
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Interindividual Differences in Behavior and Cognition Predicted by
Local Brain Structure: A Strictly Confirmatory Replication Study.
Authors: Eric-Jan Wagenmakers, Birte Forstmann, Luam Belay, Wouter Boekel.
Abstract
We seek to conduct a purely confirmatory replication study of eleven studies that have
previously reported an association between behavior and structural properties of the brain.
In order to ensure that our replication is strictly confirmatory, this document outlines the
details of our experimental design and plan for data analysis.
Background
Recently, much interest has concerned the relation between brain structure on the one
hand and behavior and/or cognition on the other hand. For instance, Kanai and colleagues
(2012) found that individuals with greater gray matter volume in specific brain regions have
larger online social networks (i.e., more Facebook friends) than individuals with less gray
matter volume in these areas. Here, we propose to conduct a strictly confirmatoryreplication study for eleven recently published studies, each of which associated certain
structural properties of the human brain with certain behavioral measures. Specifically, the
behavioral measures relate to behavioral activation (Xu et al., 2012), control over speed and
accuracy in perceptual decision making (Forstmann et al., 2010), percept duration in
perceptual rivalry (Kanai et al., 2010, 2011a), components of attention (i.e., executive
control and alerting; Westlye et al., 2011), aspects of social cognition (i.e., (online) social
network size; Bickart et al., 2011; Kanai et al., 2012), distractibility (Kanai et al., 2011d),
political orientation (Kanai et al., 2011c), moral values (Lewis et al., 2012), and empathy
(Banissy et al., 2012).
In order to ensure that the data analyses and hypothesis tests are strictly
confirmatory, this document describes the eleven experiments and associated analyses that
we aim to replicate. At the time of publishing, 36 participants have been tested but none of
the data are analyzed or inspected. The study-specific information is preceded by the
General Methods and Analyses section describing the participants, the general procedure,
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the MRI data acquisition, the MRI preprocessing and analysis, and finally the Bayesian
hypothesis test for correlations.
Note that we leave open the possibility of conducting additional exploratory analyses.
However, any exploratory analyses will be clearly labeled as such.
General Methods and Analyses
Local gray matter volume and cortical thickness will be quantified by means of voxel-based
morphometry (VBM) and white matter integrity will be quantified by diffusion tensor
imaging (DTI) and probabilistic tractography.
Participants. Participants are recruited from a 43-participant MRI study that was recently
conducted by our own research group. These participants are young, healthy undergraduate
students (mean age = 20.12, SD = 1.73) from the University of Amsterdam with normal or
corrected-to-normal vision. Participants received a monetary compensation for their time and
effort.
A subset of 36 students has been tested for the present study, but data have not been
analyzed or inspected, yet. The earlier MRI experiment featured extensive measurements of
brain structure, and hence the additional effort involved in replicating the eleven studiesconsisted primarily in having these participants complete a battery of behavioral tests. Therefore,
each of our eleven replication attempts features the same set of participants. However,
participants whose behavioral or structural measures deviate more than 2.5 standard deviations
from the respective mean will be excluded from further analysis.
Even though our initial focus is on the 36 participants who have completed the behavioral
test battery, we leave open the possibility of testing additional participants in order to obtain
clearer results. We are permitted this flexibility in data collection, because we use a Bayesian
hypothesis test to quantify the evidence for and against the null hypotheses. When using the so-
called Bayes factor, researchers are allowed to monitor the evidence as the data come in, and
continue testing until a point has been proven or disproven, or until the experimenter runs out of
time, money, or patience (Edwards et al., 1963).
General procedure. Prior to the test session participants receive an information brochure
with a brief description of all tasks and questionnaires. At the beginning of the test session
participants sign an informed consent form. Participants are tested in individual computer
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booths. All instructions are shown on the computer screen or printed on top of the
questionnaires. Participants begin with filling out the following questionnaires: Behavioral
Inhibition System/Behavioral Activation System Scales, Social Network Index, Social Network
Size Questionnaire, Cognitive Failures Questionnaire, and Political Orientation
Questionnaire. The order in which participants fill out the questionnaires is randomized.
Note that all questionnaires have been translated into Dutch and can be found in Appendix
A.After completing the questionnaires, participants continue with the computerized tasks:
Random dot motion task, bistable structure-from-motion task, and attention network test
(please find a detailed description of each task below). Again, the order of these tasks is
randomized per participant. The total duration of the test session is at most 1 hour and 30
minutes.
MRI data acquisition. In the earlier MRI experiment, DTI and T1 images have been obtained
on a 3T Philips scanner using a 32-channel head coil. For each subject, a T1 anatomical scan
was acquired (T1 turbo field echo, 220 transverse slices of 1 mm, with a resolution of 1mm3,
TR = 8.2 ms, TE = 3.7 ms). In addition, four repetitions of a multi-slice spin echo (MS-SE),
single shot diffusion weighted imaging (DWI) scans were obtained using the following
parameters: TR = 7545 ms, TE = 86 ms, 60 transverse slices, 2 mm slice thickness, FOV: 224 x
224 mm2, voxel size 2 mm isotropic resolution. For each slice, one image without diffusion
weighting (b = 0) and 32 diffusion-weighted images (b=1000s/mm 2) along 32 directions
were acquired.
MRI data preprocessing and analysis. We will carry out all MRI data analyses in FMRIBs
Software Library (FSL 4.0; www.fmrib.ox.ac.uk/fsl) and FreeSurfer
(http://surfer.nmr.mgh.harvard.edu) software.
VBM preprocessing. Voxel-Based Morphometry will be performed using FSL's
default VBM pipeline (http://www.fmrib.ox.ac.uk/fsl/fslvbm/index.html). First, non-brain
tissue will be removed from T1 images using FMRIB's Brain Extraction Tool (BET). Second,
brain-extracted images will be segmented into gray matter (GM), white matter (WM), and
cerebrospinal fluid (CSF). GM images will be non-linearly registered to GM ICBM-152, and
averaged to create a study-specific template at 2mm resolution in standard space. All GM
images will then be non-linearly registered to the study-specific template. During this stage,
each voxel of each registered grey matter image is divided by the Jacobian of the warp field
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(Good et al., 2001). Finally, images will be smoothed using a Gaussian kernel with a sigma of
3 mm.
DTI preprocessing. All four runs of DTI will be merged and corrected for eddy
currents. Affine registration will be used to register each volume to a reference volume. A
single image without diffusion weighting (b=0) will be extracted from the merged data and
non-brain tissue will be removed using BET to create a brain-mask which will be used in
subsequent analyses. DTIFIT will be then be applied to fit a tensor model at each voxel of
the data. Finally, tract based spatial statistics (TBSS) will be run. To compute probabilistic
tractography, Bayesian estimation of diffusion parameters using sampling techniques
(BedpostX) will be applied. BedpostX uses a dual fiber model which can account for crossing
fibers.
TBSS.Tract-Based Spatial Statistics will be performed using FSL's default TBSS
pipeline (http://www.fmrib.ox.ac.uk/fsl/tbss/index.html). First, FA images are slightly
eroded and end slices are zeroed, in order to remove likely outliers from the diffusion
tensor fitting. Second, all FA images will be aligned to 1 mm standard space using non-linear
registration to the FMRIB58_FA standard-space image. Affine registration is then used to
align images into 1 x 1 x 1 mm MNI152 space, and a skeletonization procedure is
subsequently applied to a mean FA image resulting from averaging all individual MNI-
aligned images. Subsequently, the mean skeletonized FA image is thresholded at > 0.2 in
order to accurately represent white-matter tracts. Subject's FA data are then projected onto
the mean skeletonized FA image and concatenated. Finally, the non-linear warps and
skeletonization procedures resulting from aforementioned FA-TBSS will be used in order to
construct skeletonized images of other measures (i.e., parallel eigenvalue (1) and mean
diffusivity (MD)).
Probabilistic tractography. First, affine registration will be used to align relevant
MNI masks to subjects individual high-resolution T1 images, and subsequently to subjects
DTI images. Probabilistic tractography will then be performed using 5.000 tract-following
samples at each voxel with a curvature threshold of 0.2. Single masks will be used as seed
space, and we will use classification masks in order to estimate the number of samples
reaching the relevant target mask. In addition, contralateral exclusion masks will be used to
discard pathways crossing over to the contralateral seed mask before travelling to the
classification mask. For the estimation of tract strength, resulting images will be thresholded
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at 10 samples and binarized, such that the size of a resulting image will be the amount of
voxels reliably (that is, in at least 10 samples) reaching the classification mask. This size is
then divided by the total size of the seed mask, such that the result is a proportion of the
seed mask, reliably connected to the classification mask. The same is then done for the
opposite analysis (i.e. when the seed mask and classification mask are switched), and the
average of the proportions is computed. These steps are necessary to correct for the sizes of
the masks.
Bayesian hypothesis test for (partial) correlations. The Bayes factorcompares the
probability of the observed data under H1 versus H0,and hence quantifies the evidence that
the data provide for and against the models underconsideration. In what follows, H0 is the
null model in which correlation is absent, and H1is the alternative model in which
correlation is present. We distinguish between an H1 thatdoes not commit to a direction for
the correlation (i.e., a two-sided test), and an H1 thatdoes commit to such a direction (i.e., a
one-sided test). The latter test is recommendedfor replication projects, or anytime
researchers have strong prior expectations about thedirection of the association.
The two-sided test. Under H1, Jeffreys assigned the correlation coefficient a
uniform prior distribution from -1 to 1 (Jeffreys, 1961). Further, he assumed that the means
of the observed variablesxand yare 0.
Then,
BF10 =
(1)
where n is the number of (x, y) pairs and ris the classic Pearson correlation coefficient.
Note that when the means forxand yneed to be estimated from the data, as is usually the
case, n in the above equation needs to be replaced by n - 1.
The above Bayes factor contains a uni-dimensional integral that can be computed
numerically in R:
# Jeffreys' earthquake data:
S
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integrand
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Wagenmakers, Lodewyckx, Kuriyal, and Grasman (2010) and Hoijtink, Klugkist, and Boelen
(2008).
We will only use the one-sided test, because we aim to replicate the specific
correlations reported in the studies-to-replicate.
Study-specific Methods and Analyses
Replication 1. Individuals with high BAS-Total scores (i.e., sensitivity to signals of reward
and non-punishment) show increased parallel eigenvalues within left corona radiata (CR)
and left superior longitudinal fasciculus (SLF). Individuals with high BAS-Fun scores (i.e.,
tendency to seek out new potentially-rewarding experiences) show increased paralleleigenvalues and fractional anisotropy within left CR and left SLF. These individuals also
show increased mean diffusivity within left inferior longitudinal fasciculus and left inferior
fronto-occipital fasciculus.
Xu, J. Kober, H., Caroll, K. M., Rounsaville, B. J., Pearlson, G. D., & Potenza, M. N. (2012).
White matter integrity and behavioral activation in healthy subjects. Human Brain Mapping,
33, 994-1002.
BIS/BAS questionnaire and procedure. Participants fill out a Dutch version of the Behavioral
Inhibition System/Behavioral Activation System (BIS/BAS; Carver et al., 1994; see Appendix
A) scale. The BIS/BAS is a 20-item questionnaire. Our interest will be focused on the BAS
scale which comprises 13 items (BAS-Total) and has three subscales, Drive (BAS-Drive), Fun-
Seeking (BAS-Fun), and Reward-Responsiveness (BAS-Reward). Administration time is
approximately 5 minutes.
Behavioral analysis. The behavioral measures of interest are BAS-Total scores and BAS-Funscores. BAS-Total scores assess the sensitivity to signals of reward and non-punishment.
BAS-Fun scores assess the tendency to seek out new potentially-rewarding experiences. For
each participant these scores will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) for the Bayesian correlation test.
TOI generation. Xu and colleagues (2012) reported significant positive correlations between
the BAS-Total scores and parallel eigenvalue (1) within left corona radiata (CR) and left
superior longitudinal fasciculus (SLF). Furthermore, they reported significant positive
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correlations between the BAS-Fun scores and 1 as well as fractional anisotropy (FA) within
left CR and left SLF. The authors also reported significant positive correlations between the
BAS-Fun scores and mean diffusivity (MD) within left inferior longitudinal fasciculus (ILF) and
left inferior fronto-occipital fasciculus (IFOF). We defined all these white matter (WM) tracts
as our tracts of interest (TOIs). Dr. Xu kindly provided us with the masks/templates that
were used in the original study. We will use these for the segmentation of our confirmatory
TOIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract FA, MD, and 1 values from all voxels contained in the
respective TOIs and average them. This will be done for every subject. These WM tract
measures are then corrected for age and gender using partial correlations. Unlike Xu and
colleagues we will not correct for education. Our participants are all Psychology freshmen,
therefore we can rule out substantial differences in education. The corrected mean WM
tract measures per TOI will be imported into R (R Foundation for Statistical Computing,
http://www.R-project.org) software for the Bayesian correlation test. Specifically, we will
test for positive correlations between BAS-Total scores and mean 1 within left CR and left
SLF. Furthermore, we will test for positive correlations between BAS-Fun scores and mean 1
as well as mean FA within left CR and left SLF. Finally, we will test for positive correlations
between BAS-Fun scores and mean MD within left ILF and left IFOF.
Replication 2. Individuals with high LBA flexibility (i.e., good control over speed and
accuracy in perceptual decision making) show increased tract strength of white matter
fibers connecting right pre-SMA and right striatum.
Forstmann, B. U., Anwander, A., Schfer, A., Neumann, J., Brown, S., Wagenmakers, E.-J.,
Bogacz, R., et al. (2010). Cortico-striatal connections predict control over speed and accuracy
in perceptual decision making. Proceedings of the National Academy of Sciences of the
United States of America, 107(36), 15916-15920.
Random dot motion task and procedure.We use the same random dot motion (RDM) task
(Gold & Shadlen, 2001) as Forstmann and colleagues (2010; see Figure 1).
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The experiment features 360 trials in total, with 180 speed and 180 accuracy trials. A cloud
consisting of 120 white dots with 50% coherently moving dots and 50% randomly moving
dots is presented against a black background. A single dot consists of 3 pixels and the whole
cloud has a diameter of 250 (uniformly distributed) pixels. At the beginning of each trial
either a speed- or an accuracy-cue is presented for 1000 ms. The speed-accuracy tradeoff(SAT) in this task is manipulated by pseudo-randomized presentation of the two different
cue-types. The speed cue instructs participants to adopt a liberal level of cautiousness,
responding as quickly as possible. The accuracy cue, however, instructs participants to adopt
a more conservative level of cautiousness, responding as accurate as possible. After the cue,
a fixation cross is presented at the center of the screen for 500 ms. Subsequently, the RDM
stimulus is shown until a response is made, but at most for 1500 ms. Responses have to be
made within this time window. Participants respond by pressing a with their left index
finger when they perceive a leftward motion and l with their right index finger when they
perceive a rightward motion. Immediately after the response participants receive feedback
(400 ms) that is dependent on the trial-type. They see either te traag (Dutch for too
slow)/fout (Dutch for incorrect) or op tijd (Dutch for in time)/goed (Dutch for
correct). After 120 and 240 trials participants can choose to take a break of up to 45
seconds. The entire experiment takes approximately 20 minutes.
Figure 1. Random Dot Motion paradigm with cues
emphasizing speed (SN Dutch abbreviation for fast)and accuracy (AC Dutch abbreviation for accurate).
Note. Figure taken from Forstmann et al. 2010 and edited.
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LBA model. The linear ballistic accumulator (LBA; Brown et al., 2008) model serves to
decompose the response time and accuracy measures into latent psychological processes.
The decision process of interest, here, is response caution, which can be quantified by
means of the LBA. We will apply the same parameter constraints as Forstmann and
colleagues (2010). In this design only one parameter - response threshold (b) - will be free to
vary with the speed vs. accuracy cue, while all other parameters (start point distribution (A),
drift rate for the response (v), variability of the drift rate (s) and nondecision time (t0)) will
be fixed.
Behavioral data analysis. The behavioral measure of interest is the LBA flexibility
parameter, assessing efficacy of changing response caution. It is assumed that changes in
response caution originate from adjustments of response thresholds (Forstmann et al.,
2010; pp. 1516). Therefore, LBA flexibility is computed as the difference between the LBA
threshold estimates for the accuracy (baccuracy) and the speed (bspeed) conditions. We will fit
the LBA model to each participant's RT and accuracy measures on speed and accuracy trials
separately. The only parameter allowed to vary will be the response threshold b. The
resulting individual LBA flexibility estimates will be imported into R for the Bayesian
correlation test.
TOI analysis. Forstmann and colleagues (2010) reported a significant positive correlation
between LBA flexibility and tract strength of white matter (WM) fibers connecting right pre-
SMA and right striatum. We defined this WM fiber tract as our tract-of-interest (TOI). We
will use the mask/template that was used in the original study for the segmentation of our
confirmatory TOI.
Probabilistic tractography. We will limit our tractography to delineate tracts that the
authors found to significantly correlate with LBA flexibility. Hence, probabilistic tractography
will be performed only on fibers connecting right pre-SMA and right striatum. We will
perform the probabilistic tractography conform the protocol stated in the general methods
and analyses section (see above).
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract tract strength values from all voxels contained in the
relevant TOI and average them. This will be done for every subject. These tract strength
measures are then corrected for age and gender using partial correlations. The corrected
mean tract strength measures will be imported into R (R Foundation for Statistical
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Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for a positive correlation between LBA flexibility and WM tract
strength of fibers connecting right pre-SMA and right striatum.
Replication 3. Individuals with short percept durations in perceptual rivalry show
increased cortical thickness within superior parietal lobe (SPL) and postcentral gyrus,
increased gray matter volume within SPL, and increased fractional anisotropy of white
matter fibers underneath SPL.
Kanai, R., Bahrami, B., & Rees, G. (2010). Human parietal cortex structure predicts individual
differences in perceptual rivalry. Current Biology, 20(18), 1626-1630.
Bistable SFM task. We use the same ambiguous rotating structure-from-motion (SFM)
stimulus task as Kanai and colleagues (2010; see Figure 2).
Figure 2. Bistable Structure-From-Motion Stimulus.
Note. Figure taken from Kanai et al. 2010.
The task includes 8 trials and 1 practice trial. Participants are presented with an ambiguous
rotating sphere, consisting of 200 full white dots. The dots move sinusoidally at a constant
speed (151 deg/s). However, the rotation of the sphere can be perceived as either rightward
or leftward. A red fixation dot is presented at the center of the screen and participants are
instructed to steadily fixate. The trial duration is 48 seconds. Participants report the
duration of their percept of the rotation direction (right or left) of the SFM stimulus by
holding the spatially compatible key (left arrow or right arrow on a regular keyboard) with
their left or right index finger until the percept switches to the other direction. The entire
experiment takes approximately 10 minutes.
Behavioral data analysis. The behavioral measure of interest is percept duration, assessing
bistable perception. Besides computing the mean percept duration, we will also compute its
reciprocal, the switch rate, for each participant and import these values into R (R
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Foundation for Statistical Computing, http://www.R-project.org) software for the Bayesian
correlation test.
ROI generation. Kanai and colleagues (2010) reported significant negative correlations
between percept duration and cortical thickness (CT) within bilateral superior parietal lobe
(SPL) and bilateral postcentral gyrus. For gray matter (GM) volume they reported significant
negative correlations with percept duration within right and left SPL. White matter
fractional anisotropy (FA) showed a significant negative correlation with percept duration
underneath right and left SPL. We defined all these regions as our regions of interest (ROIs).
Dr. Kanai kindly provided us with the masks/templates that were used in the original study.
We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract CT, GM, and FA values from all voxels contained in the
respective ROIs and average them. This will be done for every subject. These structural brain
measures are then corrected for age and gender using partial correlations. The corrected
mean CT, GM, and FA measures per ROI will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for negative correlations between percept duration and mean CT
within bilateral SPL and bilateral postcentral gyrus. We will also test for negative
correlations between percept duration and mean GM volume within right and left SPL.
Finally, we will test for negative correlations between percept duration and mean FA
underneath right and left SPL.
Replication 4. Individuals with long percept durations in perceptual rivalry show increased
gray matter volume within right anterior superior parietal lobe.
Kanai, R., Carmel, D., Bahrami, B., & Rees, G. (2011a). Structural and functional fractionation
of right superior parietal cortex in bistable perception. Current Biology, 21(3), R106-R107.
Bistable SFM task. We use the same ambiguous rotating structure-from-motion (SFM)
stimulus task as Kanai and colleagues (2011a; see Figure 2). For a detailed description of the
task please refer back to Replication 3.
Behavioral data analysis. The behavioral measure of interest is percept duration (and
switch rate), assessing bistable perception. We will compute the mean percept duration for
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each participant and import these values into R (R Foundation for Statistical Computing,
http://www.R-project.org) software for the Bayesian correlation test.
ROI generation. Kanai and colleagues (2011a) reported a significant positive correlation
between percept duration and gray matter (GM) volume within right anterior superior
parietal lobe (aSPL). We defined this region as our region of interest (ROI). Dr. Kanai kindly
provided us with the mask/template that was used in the original study. We will use this
mask/template for the segmentation of our confirmatory ROI.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract GM values from all voxels contained in the ROI and
average them. This will be done for every subject. These GM measures are then corrected
for age and gender using partial correlations. The corrected mean GM measures will be
imported into R (R Foundation for Statistical Computing, http://www.R-project.org)
software for the Bayesian correlation test. Specifically, we will test for a positive correlation
between percept duration and mean GM volume within right aSPL.
Replication 5. Individuals with a small difference in reaction times between trials with
incongruent and congruent stimuli (i.e., good executive control) show increased cortical
thickness within left caudal anterior cingulate cortex, left superior temporal lobe, and
right middle temporal lobe. Individuals with a small difference in reaction times between
trials with no cue and a central cue (i.e., good alerting ability) show increased cortical
thickness within medial and lateral aspects of the left superior temporal lobe.
Westlye, L. T., Grydeland, H., Walhovd, K. B., & Fjell, A. M. (2011). Associations between
regional cortical thickness and attentional networks as measured by the attention network
test. Cerebral Cortex, 21(2), 345-356.
Attention Network Test. We use the same Attention Network Test (ANT) as Westlye and
colleagues (2011; downloaded from Dr. Jin Fans website
www.sacklerinstitute.org/users/jin.fan; see Figure 3). The task includes 2 runs of 96 trials
and 20 practice trials. Each trial begins with the presentation of a fixation cross in the center
of the screen for variable durations (400, 800, 1200, or 1600 ms). Subsequently, one of
three cues is presented for 100 ms: (1) no cue, (2) center cue (*, replacing fixation cross), or
(3) spatial cue (*, above or below fixation cross). Then the target is presented for a
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maximum duration of 1700 ms (until a response is made). The target is an arrow in the
center of a row of 5 arrows, presented below or above the fixation cross. The flanking
arrows can be (1) two congruent arrows (pointing in the same direction as the target), (2)
two incongruent arrows (pointing in the opposite direction of the target), or (3) two lines on
each side of the target (neutral). Participants are instructed to report the direction (left or
right) of the target arrow by pressing the spatially compatible key (left mouse button and
right mouse button) with their left or right thumb. The entire experiment takes
approximately 15 minutes.
Figure 3. Attentional Network Test Paradigm.
Behavioral data analysis. The behavioral measures of interest are executive control (EC)
and alerting network scores, assessing the executive control and the alerting components of
attention, respectively. We will apply the same processing steps as described by Westlye
and colleagues (2011) before we compute the two network scores:
To remove outliers, all RTs > 1500 ms and < 200 ms were removed (). Next, since error responses
are assumed to originate from a different RT distribution than correct responses, we only analyzed
correct responses. Also, because responses following erroneous responses typically are slower than
responses following correct responses (posterror slowing), we also removed responses following
erroneous responses. Since RTs are not normally distributed, we used median RT per condition as
raw scores for each subject (pp. 348).
However, we will not adjust the component scores with the baseline RT in order to control
for an effect of age on RT, because our participants form a homogenous age group
(Psychology freshmen).
Based on median RT the EC score will be computed as follows:
EC = [RTincongruent - RTcongruent] / RTcongruent
Based on median RT the alerting score will be computed as follows:
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Alerting = [RTno cue RTcenter cue] / RTcenter cue
For each participant the resulting scores will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
ROI generation. For their subsample of young participants, Westlye and colleagues (2011)
reported significant negative correlations between EC scores and cortical thickness (CT)
within left caudal anterior cingulate cortex (ACC), left superior temporal lobe (STL), and right
middle temporal lobe (MTL). The A scores showed a significant negative correlation with CT
within left superior parietal lobe (SPL). We defined all these regions as our regions of
interest (ROIs). Dr. Westlye kindly provided us with the masks/templates (i.e., labels used in
FreeSurfer (http://surfer.nmr.mgh.harvard.edu) software) that were used in the original
study. We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract CT values from all voxels contained in the ROIs and
average them. This will be done for every subject. These CT measures are then corrected for
age and gender using partial correlations. The corrected mean CT measures will be imported
into R (R Foundation for Statistical Computing, http://www.R-project.org) software for the
Bayesian correlation test. Specifically, we will test for negative correlations between EC
scores and mean CT within left caudal ACC, left STL and right MTL. Furthermore, we will test
for a negative correlation between alerting scores and mean CT within left SPL.
Replication 6. Young individuals with high scores on the Social Network Index (i.e., large
social networks) show increased gray matter volume within bilateral amygdala and
increased cortical thickness within right subgenual anterior cingulate cortex, left caudal
superior frontal gyrus and left caudal inferior temporal sulcus.
Bickart, K. C., Wright, C. I., Dautoff, R. J., Dickerson, B. C., & Barrett, L. F. (2011). Amygdala
volume and social network size in humans. Nature Neuroscience, 14(2), 163-164.
Social Network Index questionnaire and procedure. Participants fill out a Dutch version of
the Social Network Index (SNI) questionnaire. The questionnaire has 12 items, measuring
aspects of social cognition: Social network diversity (SND), social network size (SNS) and
social network complexity (SNC). Administration time is approximately 10 minutes.
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Behavioral data analysis. The behavioral measures of interest are SNS (i.e., the total
number of people with whom the respondent has regular contact) and SNC (i.e., the
number of different groups that these contacts belong to). Thus, for each participant the
resulting SNS and SNC scores will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
ROI generation. For their subsample of young participants, Bickart and colleagues (2011)
reported significant positive correlations between SNS/SNC (similar results) and gray matter
(GM) volume within left and right amygdala. Furthermore, they reported significant positive
correlations between SNS/SNC (similar results) and cortical thickness (CT) within right
subgenual anterior cingulate cortex (sgACC), left caudal superior frontal gyrus (cSFG), and
left caudal inferior temporal sulcus (cITS). We defined all these regions as our regions of
interest (ROIs). Dr. Feldman Barrett, Dr. Dickerson, and Kevin Bickart kindly agreed to
provide us with the masks/templates that were used in the original study. We will use these
for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract gray matter (GM) and cortical thickness (CT) values from all
voxels contained in the respective ROIs and average them. This will be done for every
subject. These GM and CT measures will then be corrected for age and gender using partial
correlations. The GM measures will additionally be corrected for total intracranial volume.
The corrected mean GM and CT measures will be imported into R (R Foundation for
Statistical Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for positive correlations between SNS/SNC and mean GM volume
within left and right amygdala as well as mean CT within right sgACC, left cSFG, and left cITS.
Replication 7. Individuals with a large number of friends on Facebook (i.e., large online
social network) show increased gray matter volume within the following regions: left
middle temporal gyrus, right posterior superior temporal sulcus, right entorhinal cortex,
and bilateral amygdala. Individuals with high scores on the Social Network Size
Questionnaire (i.e., large real-world social network) show increased gray matter volume
within right amygdala.
Kanai, R., Bahrami, B., Roylance, R., & Rees, G. (2012). Online social network size is reflected
in human brain structure. Proceedings of the Royal Society Biological sciences, 279(1732),
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1327-1334.
Social Network Size Questionnaire and procedure. Participants fill out a Dutch version of
the Social Network Size questionnaire (Stileman & Bates, 2007; see Appendix A). This
questionnaire consists of 9 items. One of its items is: How many friends do you have on
Facebook?. We ask participants to make a note of the number of friends they have on
Facebook or an alternative comparable social network site such as myspace or the Dutch
Hyves and bring it to the test session. Administration time is approximately 10 minutes.
Behavioral data analysis. The behavioral measures of interest are online social network size
(i.e., the number of Facebook friends (FBN)) and real-world social network size. Subjects
answers to the 9 subquestions contained in this questionnaire will be square-root
transformed to correct for skewness. We will compute the FBN as the square root of
subjects answer to the question: How many friends do you have on Facebook?. A
normalized real-world social network size score will be computed per participant by
averaging the z-scores for the questionnaire items 1, 2, 4, 5, 6, 8 and 9 after skewness
correction. Thus, for each participant an online social network size score and a real-world
social network size score will be imported into R (R Foundation for Statistical Computing,
http://www.R-project.org) software for the Bayesian correlation test.
ROI generation. Kanai and colleagues (2012) reported significant positive correlations
between online social network size and gray matter (GM) volume within left middle
temporal gyrus (MTG), right superior temporal sulcus (STS), right entorhinal cortex (EC), and
bilateral amygdala. Real-world social network size was significantly and positively correlated
with GM only within right amygdala. We defined all these regions as our regions of interest
(ROIs). Dr. Kanai kindly provided us with the masks/templates that were used in the original
study. We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract gray matter (GM) values from all voxels contained in the
ROIs and average them. This will be done for every subject. These GM measures will then be
corrected for age, gender and total gray matter volume. The corrected mean GM measures
will be imported into R (R Foundation for Statistical Computing, http://www.R-project.org)
software for the Bayesian correlation test. Specifically, we will test for positive correlations
between FBN and mean GM volume within left MTG, right STS, right EC, and bilateral
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amygdala. Furthermore, we will test for a positive correlation between real-world network
size scores and mean GM volume within right amygdala. Since our Bayesian correlation test
allows us to quantify evidence in favor of a null-effect, we will also test for the absence of
positive correlations between real-world social network size and mean GM volume within
left MTG, right STS, right EC, and left amygdala.
Replication 8. Individuals with high scores on the Cognitive Failure Questionnaire (i.e.,
high distractibility) show increased gray matter volume within left superior parietal lobule
and decreased gray matter volume within left mid prefrontal cortex.
Kanai, R., Dong, M. Y., Bahrami, B., & Rees, G. (2011d). Distractibility in daily life is reflected
in the structure and function of human parietal cortex.Journal of Neuroscience, 31(18),
6620-6626.
Cognitive Failures Questionnaire and procedure.Participants fill out a Dutch version of the
Cognitive Failures Questionnaire (CFQ, Broadbent et al., 1982; see Appendix A).
Administration time is approximately 5 minutes.
Behavioral data analysis. The behavioral measure of interest is distractibility as assessed by
the CFQ. As in Kanai et al. (2011d), we will quantify distractibility by computing the standard
loadings derived from a previous factor analysis (Wallace et al., 2002). Specifically, we will
use the following 9 items: 1, 2, 3, 4, 15, 19, 21, 22, and 25. Scores on these items will be
imported into R (R Foundation for Statistical Computing, http://www.R-project.org)
software for the Bayesian correlation test.
ROI generation. Kanai and colleagues (2011d) reported a significant positive correlation
between CFQ scores and gray matter (GM) volume within left superior parietal lobe (SPL).
Furthermore, the authors reported a weak negative correlation between CFQ scores and
GM volume within left mid prefrontal cortex (mPFC). We defined these regions as our
regions of interest (ROIs).Dr. Kanai kindly provided us with the masks/templates that were
used in the original study. We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract gray matter (GM) values from all voxels contained in the
respective ROIs and average them. This will be done for every subject. These GM measures
are then corrected for age, gender and total gray matter volume using partial correlations.
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The corrected mean GM measures will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for a positive correlation between CFQ scores and mean GM
volumes within left SPL and for a negative correlation within left mPFC.
Replication 9. Individuals with high scores on the Political Orientation Questionnaire (i.e.,
conservative) show increased gray matter volume within right amygdala and left insula,
and decreased gray matter volume within right entorhinal cortex. Individuals with low
scores on the Political Orientation Questionnaire (i.e., liberal) show increased gray matter
volume within anterior cingulate cortex.
Kanai, R., Feilden, T., Firth, C., & Rees, G. (2011c). Political orientations are correlated with
brain structure in young adults. Current Biology, 21, 677-680.
Political Orientation Questionnaire and procedure.Participants fill out a Dutch version of
the Political Orientation Questionnaire (POQ) used by Kanai and colleagues (2011c; see
Appendix A). The POQ consist of a five-point scale: (1) very liberal, (2) liberal, (3) middel-of-
the-road, (4) conservative, (5) very conservative. Administration time is approximately 1
minute.
Behavioral data analysis. The behavioral measure of interest is political orientation. For
each participant a political orientation score will be imported into R (R Foundation for
Statistical Computing, http://www.R-project.org) software for the Bayesian correlation test.
ROI generation. Kanai and colleagues (2011c) reported significant positive correlations
between high POQ scores (i.e., conservatism) and gray matter (GM) volume within right
amygdala and left insula. GM volume was significantly and negatively correlated with
conservatism within right entorhinal cortex (EC). Furthermore, the authors reported a
significant negative correlation between low POQ scores (i.e., liberalism) and GM volume
within anterior cingulate cortex (ACC). We defined all these regions as our regions of
interest (ROIs). Dr. Kanai kindly provided us with the masks/templates that were used in the
original study. We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract gray matter (GM) values from all voxels contained in the
respective ROIs and average them. This will be done for every subject. These GM measures
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are then corrected for age, gender, and total gray matter volume using partial correlations.
The corrected mean GM measures will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for positive correlations between conservatism and mean GM
volume within right amygdala and left insula. We will also test for a negative correlation
between conservatism and mean GM volume within right entorhinal cortex. Furthermore,
we will test for a negative correlation between liberalism and mean GM volume within ACC.
Finally, we will attempt to replicate the absence of a significant positive correlation between
conservatism and mean GM volume within left amygdala.
Replication 10. Individuals with high individualizing scores (i.e., values of harm/care and
fairness; aggregate score on corresponding subscales) show increased gray matter volume
within left dorsomedial prefrontal cortex (DMPFC) and a marginally significant decrease in
gray matter volume within bilateral precuneus. Specifically, individuals scoring high on the
harm subscale show decreased gray matter volume within bilateral precuneus and left
postcentral gyrus. Individuals scoring high on the fairness subscale show a trend to
significantly increased gray matter volume within left DMPFC.
Individuals with high binding scores (i.e. deference to authority, purity/sanctity and in-
group loyalty; aggregate score on corresponding subscales) show increased gray matter
volume within bilateral subcallosal gyrus and a marginally significant increase in gray
matter volume within left anterior insula. Specifically, individuals scoring high on the
authority subscale show increased gray matter volume within bilateral subcallosal gyrus.
Likewise, individuals scoring high on the purity subscale show increased gray matter
volume within bilateral subcallosal gyrus. Furthermore, they show increased gray matter
volume within left anterior insula and a trend to increased gray matter volume within
right anterior insula. This trend is significant for individuals scoring high on disgust (based
on a subset of the purity items dealing with disgust).
Lewis, G., Kanai, R., Bates, T. & Rees, G. (2012). Moral values are associated with individual
differences in regional brain volume.Journal of Cognitive Neuroscience, 24(8), 1657-1663.
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Moral Foundations Questionnaire and procedure.Participants fill out a Dutch version of the
Moral Foundations Questionnaire (MFQ; Graham et al., 2009; see Appendix A). The MFQ
measures five foundations of moral behavior:
(1) harm (minimizing harm to others), (2) fairness (maximizing fairness to all), (3) in-group loyalty
(the importance of the in-group), (4) authority (respect for status and hierarchy), and (5) purity
(avoiding impure or disgusting acts/entities) (Lewis et al., 2012; pp. 1657).
Administration time is approximately 10 minutes.
Behavioral data analysis. The behavioral measures of interest are individualizing and
binding, assessing moral values.For each participant an individualizing (aggregate score on
harm and fairness components) and a binding score (aggregate score on authority, in-group
loyalty and purity) will be imported into R for the Bayesian correlation test.
ROI generation. Lewis and colleagues (2012) reported a significant positive correlation
between individualizing scores and gray matter (GM) volume within left dorsomedial
prefrontal cortex (DMPFC) and a marginally significant negative correlation between these
measures within bilateral precuneus. The authors also reported significant negative
correlations between harm subscores and GM volume within bilateral precuneus and left
postcentral gyrus. A marginally significant positive correlation was reported between
fairness subscores and GM volume within left DMPFC. Furthermore, the authors reported a
significant positive correlation between binding scores and GM volume within bilateral
subcallosal gyrus, and a marginally significant positive correlation between these measures
within left anterior insula. For authority as well as purity subscores the authors reported a
significant positive correlation with GM volume within bilateral subcallosal gyrus. In
addition, a significant positive correlation and a trend to a significant positive correlation
were found between purity subscores and GM volume within left anterior insula and right
anterior insula, respectively. Finally, the authors reported a significant positive correlation
between disgust subscores (based on a subset of purity items dealing with disgust) and GM
volume within right anterior insula. We defined all these regions as our regions of interest
(ROIs). Dr. Kanai kindly agreed to provide us with the masks/templates that were used in the
original study. We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract gray matter (GM) values from all voxels contained in the
respective ROIs and average them. This will be done for every subject. These GM measures
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are then corrected for age, gender, and total gray matter volume using partial correlations.
The corrected mean GM measures will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for a positive correlation between individualizing scores and mean
GM volume within left DMPFC, and a negative correlation between these measures within
bilateral precuneus. Furthermore, we will test for negative correlations between harm
subscores and mean GM volume within bilateral precuneus and left postcentral gyrus. We
will also test for a positive correlation between fairness subscores and mean GM volume
within left DMPFC. Subsequently, we will test for positive correlations between binding
scores and mean GM within bilateral subcallosal gyrus and left anterior insula. Authority and
purity subscores will be tested for a positive correlation with mean GM volume within
bilateral subcallosal gyrus. In addition, purity subscores will be tested for positive
correlations with mean GM volume within left and right anterior insula. Finally, we will test
for a positive correlation between disgust scores (based on a subset of purity items dealing
with disgust) and mean GM volume within right anterior insula.
Replication 11. Individuals with high empathic concern scores show decreased gray matter
volume within the following regions: left precuneus, left anterior cingulate, and anterior
insula. Individuals with high personal distress scores show increased gray matter volume
within left anterior insula and decreased gray matter volume within left somatosensory
cortex. Individuals with high perspective taking scores show increased gray matter volume
within left anterior cingulate. Individuals with high fantasy scores show increased gray
matter volume within right dorsolateral prefrontal cortex.
Banissy, M. J., Kanai, R., Walsh, V., & Rees, G. (2012). Inter-individual differences in empathy
are reflected in human brain structure. NeuroImage, 62(3), 2034-2039.
Interpersonal Reactivity Index and procedure.Participants fill out a Dutch version of the
Interpersonal Reactivity Index (IRI; Davis, 1980; translated and validated by De Corte et al.,
2007; see Appendix A). The IRI has 28 items and comprises four subscales:
() perspective taking; personal distress; empathic concern; and fantasy (Davis, 1980; Davis, Luce,
and Kraus, 1994). Empathic concern and personal distress measure affective reactions but differ in
their targets. Personal distress is self-oriented and associated to aversive emotional responses in the
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observer (e.g. feelings of fear or discomfort at witnessing negative experiences of others). Empathic
concern is other-oriented and related to feelings of compassion and sympathy for the observed
individual. Perspective taking examines the tendency to think from another perspective (i.e.
cognitive responses). Fantasy examines participants abilities to transpose themselves into fictional
situations (e.g. books, movies, daydreams).
Each subscale contained seven items. They were measured on a five point Likert scale
ranging from 0 (Does not describe me well) to 4 (Describes me very well). For each subscale, a
minimum score of 0 or maximum score of 28 was possible (Banissy et al., 2012; pp. 2035).
Administration time is approximately 10 minutes.
Behavioral data analysis. The behavioral measures of interest are empathic concern (EC),
personal distress (PD), perspective taking (PT), and fantasy (FS), assessing different aspects
of empathy.For each participant we will import one score for each measure into R (R
Foundation for Statistical Computing, http://www.R-project.org) for the Bayesian
correlation test.
ROI generation. Banissy and colleagues (2012) reported significant negative correlations
between EC and gray matter (GM) volume within left precuneus, left anterior cingulate, and
left anterior insula. Furthermore, the authors reported a significant negative correlation
between PD and GM volume within left somatosensory cortex and a positive correlationbetween these measures within left anterior insula. PT was positively correlated with GM
volume within left anterior cingulate and FS was positively correlated with GM volume
within right dorsolateral prefrontal cortex. We defined all these regions as our regions of
interest (ROIs). Dr. Kanai kindly agreed to provide us with the masks/templates that were
used in the original study. We will use these for the segmentation of our confirmatory ROIs.
Correlational analysis. Before performing the Bayesian hypothesis test for correlations (as
described above), we will extract gray matter (GM) values from all voxels contained in the
respective ROIs and average them. This will be done for every subject. These GM measures
are then corrected for age, gender, and total gray matter volume using partial correlations.
The corrected mean GM measures will be imported into R (R Foundation for Statistical
Computing, http://www.R-project.org) software for the Bayesian correlation test.
Specifically, we will test for negative correlations between empathic concern scores and
mean GM volume within left precuneus, left anterior cingulate and left anterior insula.
Furthermore, we will test for a negative correlation between personal distress scores and
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mean GM volume within somatosensory cortex, and a positive correlation between these
measures within left anterior insula. We will also test for a positive correlation between
perspective taking scores and mean GM volume within left anterior cingulate. Finally, we
will test for a positive correlation between fantasy scores and mean GM volume within right
dorsolateral prefrontal cortex.
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Appendix A:Translated questionnaires used in the present replication study.
1. Dutch Translation of Behavioral Inhibition System/Behavioral Activation System
(adapted from Carver et al., 1994)
Op de volgende bladzijden vindt je een aantal beweringen. De bedoeling is dat je deze
beweringen doorleest en dat je nagaat of zij van toepassing zijn.
Naast elke bewering staan vier antwoordmogelijkheden die variren van "helemaal mee
eens" tot "helemaal mee oneens". Het is de bedoeling dat je telkens met een kruisje in n
van de hokjes aangeeft in hoeverre een bewering op jou van toepassing is.
Laat geen vraag onbeantwoord en beperk je tot de gegeven antwoordmogelijkheden.
Neem je tijd, maar denk niet al te lang na over een vraag.
1. Als ik denk dat er iets onprettigs gaat gebeuren, raak ik meestal behoorlijk "opgefokt".
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
2. Ik ben bezorgd om het maken van fouten.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
3. Als ik iets wil, ga ik er meestal helemaal voor.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
4. Vaak doe ik dingen om geen andere reden dan dat het wel eens leuk zou kunnen zijn.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens0 helemaal mee oneens
5. Kritiek of een standje raken mij behoorlijk.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
6. Als ik iets krijg wat ik wil, voel ik me opgewonden en opgeladen.
0 helemaal mee eens0 beetje mee eens
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0 beetje mee oneens
0 helemaal mee oneens
7. Ik doe een hoop moeite om dingen die ik wil te krijgen.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
8. Ik verlang sterk naar spanning en nieuwe sensaties.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
9. Ik voel me behoorlijk overstuur als ik denk of weet dat iemand boos op me is.0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
10. Ik ben altijd bereid iets nieuws te proberen als ik denk dat het leuk zal zijn.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
11. Als ik iets goed doe, wil ik er graag mee doorgaan.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
12. Zelfs als mij iets ergs staat te gebeuren, ervaar ik zelden angst of nervositeit.
0 helemaal mee eens
0 beetje mee eens0 beetje mee oneens
0 helemaal mee oneens
13. Ik handel vaak zoals het moment me ingeeft.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
14. Als ik een kans zie iets te krijgen wat ik wil, ga ik er meteen op af.0 helemaal mee eens
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0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
15. Als mij goede dingen overkomen, raakt dat me sterk.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
16. Ik voel me bezorgd als ik denk dat ik slecht heb gepresteerd op iets.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
17. Ik zou het spannend vinden een wedstrijd te winnen.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
18. Vergeleken met mijn vrienden heb ik erg weinig angsten.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens0 helemaal mee oneens
19. Als ik een mogelijkheid zie iets te krijgen wat ik leuk vind, word ik direct opgewonden.
0 helemaal mee eens
0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
20. Als ik ergens werk van maak, gooi ik ook mijn volle gewicht er tegenaan.
0 helemaal mee eens0 beetje mee eens
0 beetje mee oneens
0 helemaal mee oneens
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2. Dutch Translation of Social Network Index
(adapted from Cohen et al., 1997)
Instructies: Deze vragenlijst gaat over met hoeveel mensen u regelmatig afspreekt of praat,
inclusief familie, vrienden, collega's, buren, etc. Lees en beantwoord elke vraag nauwkeurig.
Beantwoord subvragen waar nodig.
1. Welk van onderstaande alternatieven beschrijft je huwelijkse staat het best?
.... (1) Op dit moment getrouwd en samenwonend, of samenwonend met een vaste relatie.
.... (2) Nooit getrouwd geweest, en nooit samengewoond met een vaste relatie.
.... (3) Uit elkaar.
.... (4) Gescheiden, of voorheen samengewoond met iemand in een vaste relatie.
.... (5) Weduwe/weduwnaar.
2. Hoeveel kinderen heb je? (Als je geen kinderen hebt, schrijf dan '0' op en ga verder met
vraag 3.)
....
2a. Met hoeveel van je kinderen heb je minstens eens per twee weken contact?
....
3. Zijn je vader en moeder nog in leven? (Als je beide ouders overleden zijn, zet dan eenkruisje bij '0' en ga verder met vraag 4.)
.... (0) Geen van beide
.... (1) Alleen moeder nog in leven
.... (2) Alleen vader nog in leven
.... (3) Beide nog in leven
3a. Zie je of spreek je je vader en moeder minstens eens per twee weken?
.... (0) Geen van beide
.... (1) Alleen moeder
.... (2) Alleen vader
.... (3) Beide
4. Leven je schoonvader en je schoonmoeder nog (of de ouders van je partner)? (Als je geen
schoonouders hebt, zet dan een kruisje bij 'Niet van toepassing' en ga verder met vraag5.)
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.... (0) Geen van beide
.... (1) Alleen schoonmoeder leeft nog
.... (2) Alleen schoonvader leeft nog
.... (3) Beide leven nog
.... (4) Niet van toepassing
4a. Zie je of spreek je je schoonouders minstens eens per twee weken?
.... (0) Geen van beide
.... (1) Alleen schoonmoeder
.... (2) Alleen schoonvader
.... (3) Beide
5.Aan hoeveel familieleden (anders dan uw echtgeno(o)t(e), ouders, en kinderen) voel je jegehecht? (Als dit er geen zijn, schrijf dan op '0', en ga verder met vraag 6.)
....
5a. Hoeveel van deze familieleden zie je of spreek je minstens eens per twee weken?
....
6. Hoeveel goede vrienden heb je? (met goede vrienden bedoelen we hier mensen bij wie u
zich gemakkelijk voelt, met wie u over persoonlijke zaken kunt praten, en die u om hulp
kunt vragen)
....
6a. Hoeveel van deze vrienden zie je of spreek je minstens eens per twee weken?
....
7.Ben je lid van een religieuze groep? (bv. een kerkgenootschap) Als dit niet het geval is, zetdan een kruisje bij 'nee' en ga verder met vraag 8.
... (1) Ja
... (2) Nee
7a. Hoeveel leden van je religieuze groep spreek je minstens eens per twee weken?
(Inclusief gesprekken rondom bijeenkomsten en diensten).
....
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8. Volgt u regelmatig onderwijs (via een school, universiteit, technische training, of
volwassenen onderwijs)? Zo niet, zet dan een kruisje bij 'nee' en ga door met vraag 9.
... (1) Ja
... (2) Nee
8a. Hoeveel medestudenten of docenten spreek je minstens eens per twee weken?
(Inclusief gesprekken rondom de lessen)
....
9.Werkt u op dit moment voltijds of in deeltijd? (Als dit niet het geval is, zet dan een kruisjebij 'nee' en ga door met vraag 10.)
.... (0) nee
.... (1) ja, in mijn eigen bedrijf/als zelfstandig ondernemer
.... (2) ja, in loondienst
9a. Over hoeveel mensen heeft u de supervisie?
....
9b. Hoeveel collega's (uitgezonderd hen die u superviseert) spreekt u minstens eens per
twee weken?
....
10.Hoeveel van je buren bezoek je of spreek je minstens eens per twee weken?....
11.Verricht je op dit moment regelmatig vrijwilligerswerk? (Zo nee, zet dan een kruisje bij'nee' en ga verder met vraag 12.)
... (1) Ja
... (2) Nee
11a. Hoeveel mensen die betrokken zijn bij dit vrijwilligerswerk spreek je minstens eens per
twee weken over zaken die te maken hebben met het vrijwilligerswerk?
....
12. Hoor je bij groepen waarin je met een of meerdere leden minstens eens per twee weken
over groepsgerelateerde zaken praat? Voorbeelden van zulke groepen zijn onder andere
gezelligheidsverenigingen; hobbyverenigingen; vakbonden; commercile groepen;
beroepsorganisaties; groepen die te maken hebben met kinderen, zoals ouderverenigingenop scholen of scouting; groepen die te maken hebben met de gemeenschap waarin je
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woont; etc. (Als je niet bij zulke groepen hoort, zet dan een kruisje bij 'nee' en sla het
volgende deel van de vragenlijst over.)
... (1) Ja
... (2) Nee
Bedenk in welke van deze groepen je minstens eens per twee weken met een groepslid
praat, en geef de volgende informatie voor elke groep: De naam of het soort groep, en het
totale aantal groepsleden in die groep met wie je minstens eens per twee weken praat.
Naam/soort groep Aantal
groepsleden
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3. Dutch Translation of Social Network Size Questionnaire
(adapted from Stileman & Bates, 2007)
Hoeveel mensen waren er in totaal aanwezig op het feest voor je 18de
of 21ste
verjaardag?
....
Als je nu een feestje zou geven, hoeveel mensen zou je dan uitnodigen?
....
Wat is het totaal aantal vrienden in de contactlijst van je telefoon?
....
Schrijf de namen op van alle mensen die je een sms-bericht zou sturen bij een feestelijke
gebeurtenis (bijv. een verjaardag, Kerstmis, nieuwe baan, goed examenresultaat, etc.).Hoeveel mensen zijn dit in totaal?
....
Schrijf de namen op van alle mensen in de contactlijst van je telefoon die je zou kunnen
ontmoeten voor een praatje in een besloten groep van twee tot vier personen. Hoeveel
mensen zijn dit in totaal?
....
Met hoeveel vrienden uit je schooltijd zou je nu nog vriendschappelijk gesprek kunnen
voeren?
....
Hoeveel vrienden heb je op Facebook?
....
Hoeveel vrienden heb je buiten de universiteit?
....
Schrijf de namen op van alle mensen waarvan je vindt dat je ze om een gunst kan vragen in
de verwachting dat die ook wordt verleend. Hoeveel mensen zijn dit in totaal?
....
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4. Dutch Translation of Cognitive Failures Questionnaire
(adapted from Broadbent et al., 1982)
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5. Dutch Translation of Political Orientation Questionnaire
(adapted from Kanai et al., 2011c)
Geef alstublieft je politieke voorkeur aan door een van de onderstaande alternatieven te
omcirkelen:
(1) erg progressief
(2) progressief
(3) niet progressief maar ook niet conservatief
(4) conservatief
(5) erg conservatief
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6. Dutch Translation ofMoral Foundations Questionnaire
(adapted from Graham et al., 2009)
Deel 1. Wanneer je besluit of iets goed of slecht is, in welke mate zijn de volgende overwegingen dan
van belang voor jouw oordeel.
[0] = Helemaal niet relevant (Deze overweging heeft niets te maken met mijn besluit over goed en
slecht)
[1] = Niet erg relevant
[2] = Enigszins relevant
[3] = Redelijk relevant
[4] = Erg relevant
[5] = Heel erg relevant (Dit is een van de belangrijkste factoren wanneer ik oordeel over goed en
slecht)
______ Of iemand emotioneel heeft geleden
______ Of sommige mensen anders behandeld werden dan anderen
______ Of iemands daden liefde toonden voor zijn of haar land
______ Of iemand een gebrek aan respect voor autoriteit heeft getoond
______ Of iemand standaarden van puurheid en fatsoenlijkheid geschonden heeft______ Of iemand goed was in wiskunde
______ Of iemand zorgde voor een zwak of kwetsbaar iemand
______ Of iemand oneerlijk heeft gehandeld
______ Of iemand zijn of haar groep verraden heeft
______ Of iemand zich conformeerde aan de tradities van de maatschappij
______ Of iemand iets walgelijks heeft gedaan
______ Of iemand wreed was
______ Of iemands rechten zijn ontzegt
______ Of iemand een gebrek aan loyaliteit heeft getoond
______ Of iemands actie chaos of wanorde veroorzaakte
______ Of iemand zich gedroeg op een wijze die God zou goedkeuren
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Deel 2: Zou je voor de volgende stellingen aan willen geven in welke mate je het ermee eens of
oneens bent.
[0] [1] [2] [3] [4] [5]
zeer mee
oneens
redelijk mee
oneens
enigszins mee
oneens
enigszins mee
eens
redelijk mee
eens
zeer mee
eens
______ Medeleven met degenen die lijden, is de belangrijkste deugd.
______ Wanneer de overheid wetten maakt, dan moet de garantie dat iedereen eerlijk behandeld
wordt het belangrijkste principe zijn.
______ Ik ben trots op de geschiedenis van mijn land.______ Respect voor autoriteit is iets dat alle kinderen moeten leren.
______ Mensen behoren geen walgelijke dingen te doen, zelfs wanneer er niemand schade
berokkend wordt.
______ Het is beter iets goeds te doen dan iets slechts.
______ Een van de ergste dingen die een mens kan doen is een weerloos dier pijn doen.
______ Rechtvaardigheid is de belangrijkste behoefte voor een maatschappij.
______ Mensen behoren loyaal te zijn aan hun familieleden, zelfs wanneer zij iets slechts hebben
gedaan.
______ Mannen en vrouwen hebben elk verschillende rollen in de maatschappij.
______ Ik vind sommige daden slecht, omdat zij onnatuurlijk zijn.
______ Het kan nooit goed zijn om een mens te doden.
______ Ik vind dat het moreel onjuist is dat rijke kinderen een heleboel geld erven, terwijl arme
kinderen niets erven.
______ Het is belangrijker om een teamspeler te zijn dan om jezelf te uiten.
______ Als ik een soldaat was en ik was het oneens met de orders van mijn leidinggevende, dan zou
ik toch gehoorzamen omdat dit mijn plicht is.
______ Kuisheid is een belangrijke en waardevolle deugd.
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7. Dutch Translation ofInterpersonal Reactivity Index
(adapted from Davis, 1980)
Instructies:
Onderstaand vindt je een aantal beweringen. De bedoeling is dat je deze beweringen
doorleest en dat je nagaat of zij jou goed beschrijven.
Hieronder staan vier antwoordmogelijkheden die variren van "beschrijft mij heel goed" tot
"beschrijft mij helemaal niet goed". Het is de bedoeling dat je telkens met een letter voor de
zin aangeeft in hoeverre een bewering jou goed beschrijft.
Laat geen vraag onbeantwoord en beperk je tot de gegeven antwoordmogelijkheden.
Neem je tijd, maar denk niet al te lang na over een vraag.
Antwoordmogelijkheden:
A B C D E
Beschrijft mij Beschrijft mij
helemaal niet heel
goed goed
Beweringen:
______Ik dagdroom en fantaseer, met enige regelmaat, over dingen die zouden kunnen
gebeuren met mij.
______Ik heb vaak tedere, bezorgde gevoelens voor mensen die minder gelukkig zijn dan ik.
______Ik vind het soms moeilijk om dingen te zien vanuit andermans gezichtspunt.
______Soms heb ik niet veel medelijden met andere mensen wanneer ze problemen
hebben.
______Ik raak echt betrokken bij de gevoelens van de personages uit een roman.
______In noodsituaties voel ik me ongerust en niet op mijn gemak.
______Ik ben meestal objectief wanneer ik naar een film of toneelstuk kijk, en ik ga er niet
vaak volledig in op.
______Ik probeer naar ieders kant van een meningsverschil te kijken alvorens ik een
beslissing neem.
______Wanneer ik iemand zie waarvan wordt geprofiteerd, voel ik me nogal beschermend
tegenover hen.
______Ik voel me soms hulpeloos wanneer ik in het midden van een zeer emotionele
situatie ben.
______Ik probeer mijn vrienden soms beter te begrijpen door me in te beelden hoe de
dingen eruit zien vanuit hun perspectief.
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______Uitermate betrokken geraken in een goed boek of film is eerder zeldzaam voor mij.
______Wanneer ik zie dat iemand zich bezeert, ben ik geneigd kalm te blijven.
______Andermans ongelukken verstoren me meestal niet veel.
______Als ik zeker ben dat ik over iets gelijk heb, verspil ik niet veel tijd aan het luisteren
naar andermans argumenten.
______Na het zien van een toneelstuk of film, heb ik mij gevoeld alsof ik een van de
karakters was.
______In een gespannen emotionele situatie zijn, schrikt me af.
______Wanneer ik zie dat iemand unfair wordt behandeld, voel ik soms weinig medelijden
met hen.
______Ik ben meestal behoorlijk effectief in het omgaan met noodsituaties.
______Ik ben vaak nogal geraakt door dingen die ik zie gebeuren.
______ Ik geloof dat er twee zijden zijn aan elke vraag en probeer te kijken naar hun beide.
______Ik zou mijzelf beschrijven als een vrij teerhartig persoon.
______Wanneer ik naar een goede film kijk, kan ik mezelf zeer gemakkelijk in de plaats
stellen van het hoofdpersonage.
______Ik neig ertoe controle te verliezen tijdens noodsituaties.
______Wanneer ik overstuur ben door iemand, probeer ik mijzelf meestal voor een tijdje in
zijn schoenen te verplaatsen.
______Wanneer ik een interessant verhaal of roman aan het lezen ben, beeld ik me in hoe
ik me zou voelen indien de gebeurtenissen in het verhaal mij zouden overkomen.
______Wanneer ik iemand zie die zeer hard hulp nodig heeft in een noodsituatie, ga ik
kapot.
______Alvorens iemand te bekritiseren, probeer ik mij voor te stellen hoe ik mij zou voelen
mocht ik in hun plaats zijn.
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