dataesr/scientific-paragraphs-categorization huggingface dataset:
23 Hz in Experiments 1 and 2 respectively, p < 0. 001). No differences were found in our sample regarding F2 in the stimuli of any of the experiments (p > 0. 05). It is also worth noting that the [l#a] productions elicited less variability than the [la#] productions in two measures: duration of the first syllable and f0 values of the first vowel, which suggests a more systematic pronunciation. For further details concerning the different values of each production, see Table  1  and 2. The acoustic feature extractions, amplitude envelopes, and spectrograms were performed with the software Praat (Boersma & Weenink, Phonetic Sciences, University of Amsterdam, The Netherlands). It is to note that measurements are based on mean values over the vowel duration. Since the stimuli were excised in the first experiment to match 140ms duration, the time window selected for the measurement differed between experiments.
EEG recording, pre-processing and analyses EEG recording was performed using the Biosemi system with 32 active electrodes (Electro-Cap International, Inc. , Ohio; Biosemi, ActiveTwo, version 5. 36) positioned according to the International 10-20 system. The EEG data were collected at a sampling rate of 2 kHz over a [0. 1 -400 Hz] bandwidth and referenced to a common reference (CMS) and ground (DRL) directly integrated into the cap. Offline analyses were performed using EEGLAB toolbox  (Delorme & Makeig, 2004 ) and customed routines. Bad channels were spherically interpolated. Continuous data were downsampled to 1000 Hz and visually inspected to discard artifactual segments. Data were re-referenced to the average of the mastoid electrodes and a 0. 5 -30 Hz bandpass filter was applied. Regarding ocular artifacts, an independent component analysis allowed the identification and subtraction of this type of artifacts.
Epochs were generated related to stimulus onset over a time window from -100 ms to 800 ms. They were baseline-corrected by subtracting the average signal activity across the 100 ms pre-stimulus period. Finally, epochs were separately averaged for each test and standard stimuli in each of the two blocks for each participant. We did not collapse data across blocks to be able to study potential asymmetries depending on the nature of the standard stimulus. As mentioned at the end of the introduction, the presence of one of the homophones could allow discriminating the other one more easily. The MMN component was computed by subtracting the ERP waveform elicited by the standard from the ERP elicited by the deviant. This procedure minimizes the influence of the physical differences between the two types of stimuli in the early ERP responses.
Grand-averages ERPs were submitted to statistical analysis. The grand-grand average MMN difference wave was maximal at Fz, followed by Cz, as already described in the literature (see  Bishop, 2007  for a review). The Fz electrode was therefore selected for statistical analysis. The Identical condition, which was not expected to elicit an MMN response, was used as a control condition. For each subject, the amplitude of each MMN component was measured within a 50-ms window centered on the peak of the grand-grand average difference wave (i. e. component mean amplitude for each condition; see  Zora et al. , 2015 , for another example of MMN mean amplitude measurement centered at the peak latency in a study of lexical stress). Differences in mean amplitude between conditions were assessed using a multilevel mixed model approach, including data from both experiments.
This allowed the study of between and within subjects' effects in the same statistical test, which is regarded as a hierarchical or nested mixed model. Fixed effects corresponded to the variables of interest, namely condition (Identical, Homophonous and Dissimilar), type of standard ([la#] and [l#a]) and experiment (syllable or word). The interaction between condition and experiment was also considered in the model. Type of standard was included as random slope for the random effect of subjects. These analyses were run in R, using the lmer function from the lme4 package  (Bates & Maechler, 2009). Since we compared models with different nested fixed effects, the maximum likelihood (ML) was used to estimate variance components  (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). The lmerTest package  (Kuznetsova, Brockhoff, & Christensen, 2015)  allowed to determine p-values (for more methodological detail see  Luke, 2017).
These investigations highlight that patterns of ongoing thought have complex and often heterogeneous neural correlates  [29, 30] , which depend on the functional interaction of multiple neural and cognitive components  [31] [32] [33]. For example, Wang and colleagues identified distinct patterns of ongoing thoughts that were linked to reductions in both within and between network connectivity for neural systems important for external attention (ventral, dorsal and fronto parietal systems), which was in turn related to reduced performance in tasks of cognitive aptitude  [34]. Other studies have illustrated that different types of spontaneous thought depend on differential patterns of functional connectivity FIG. 1. Experimental data collection and analysis pipeline. A large-cohort of participants (n = 211) were fMRI scanned during a state of wakeful rest for a total of 9-minutes.
A comprehensive pipeline of fMRI preprocessing and data denoising procedures were followed in order to ensure maximal removal of nuisance variables. The thought sampling scores collected at the end of the resting state scanning session were first hierarchically clustered into two major groups, each of which were then reduced to three patterns of thought using principal component analysis (PCA). Fully-connected, weighted correlation matrices based on the Power et al. 2011 parcellation (264 regions of interest) scheme and the individual component scores on the identified patterns of thought were then carried forward on to Network-Based Statistics (NBS) with the aim of identifying components of brain connections that related to the participants thought patterns. T-tests were carried out for all six patterns of thought with an initial T-score of 3.
Dimensions of variation in ongoing thought patterns With the aim of investigating the differential influence of distinct thought patterns on the link between brain functional network topology and mental well-being, we collected 9-minutes of fMRI data from a large cohort of neurotypical participants (n = 211) during a period of wakeful rest. This neural measure was complemented with a session of retrospective thought sampling, administered immediately after the resting state scanning, as well as self-assessed ratings of psychological and social well-being on a cross-culturally validated questionnaire from the World Health Organization Quality of Life (WHOQOL) group  [40] , collected outside the scanner in a separate behavioural session. The workflow for the data collection and analysis techniques utilized in this study is presented in Figure  1  with further details provided in the Methods and Supplementary Information.
Parts of this dataset have been previously employed in our prior investigations  [27, 32, 34, 41]. Our first objective was to establish the dimensions of variation within the participants ongoing thought patterns. The employed retrospective thought sampling method required participants to subjectively characterize their thoughts using 4-choice (Likert scale) ratings on a set of questions derived from our previous investigations  [18, 30, 33, 39, 42]  (Supplementary Table  S1 ). With the aim of reducing dimensionality and improving interpretability, these ratings were first hierarchically clustered and then decomposed into distinct dimensions using principal component analysis (PCA), which established the main patterns of thought reported in our sample (Fig. 2 ). The total number of thought patterns were selected using scree-plots, based on the eigenvalue (>1) and the explanatory power gained by each additional decomposition (Supplementary Fig.
Moreover, an extensive motion correction procedure was followed (described in detail below) that resulted in the exclusion of 12 participants due to excessive head motion inside the scanner, and three participants were removed due to the impartial completion of the thought sampling method. In total, 211 participants imaging and thought sampling data were used in this analysis. The average age for this group was 20. 85 years (range = 18 -31, SD = 2. 44) with a 129/82 female to male ratio.
S1a ). This technique was utilized in order to partition the thought ratings into two distinct groups, thus reducing the number of variables to be decomposed into interpretable patterns of thought  [67]. Subsequently, both groups of ratings were reduced to three factors each (six in total) using principal component analysis (PCA) in SPSS (Version 23) (https://www. ibm. com/products/ spss-statistics). The number of components was chosen based on scree-plots indicating the eigenvalue of each subsequent decomposition, and its ability to explain variability in the data (Supplementary Fig. 1b ). The component loadings for the total number of six decompositions were then rotated using the Varimax method and the resulting factors were visualized on heat maps (Supplementary Fig. 2a-b ) and word clouds (Fig.
MRI Data Acquisition All MRI data acquisition was carried out at the York Neuroimaging Centre, York with a 3T GE HDx Excite Magnetic Resonance Imaging (MRI) scanner using an eight-channel phased array head coil. A single run of 9-minute resting state fMRI scan was carried out using single-shot 2D gradient-echo-planar imaging. The parameters for this sequence was as follows: TR = 3 s, TE = minimum full, flip angle = 90, matrix size = 64 x 64, 60 slices, voxel size = 3 x 3 x 3 mm3, 180 volumes. During resting state scanning, the participants were asked to focus on a fixation cross in the middle of the screen. Subsequently, a T1-weighted structural scan with 3D fast spoiled gradient echo was acquired (TR = 7. 8 s, TE = minimum full, flip angle= 20, matrix size = 256 x 256, 176 slices, voxel size = 1.
MRI Data Preprocessing preprocessing and denoising steps for the MRI data were carried out using the SPM software package (Version 12. 0) (http://www. fil. ion. ucl. ac. uk/spm/) and Conn functional connectivity toolbox (Version 17. f) (https://www. nitrc. org/projects/conn) [68] , based on the MATLAB platform (Version 16. a) (https:// uk. mathworks. com/products/matlab. html). The first three functional volumes were removed in order to achieve steady state magnetization. The remaining data was first corrected for motion using six degrees of freedom (x, y, z translations and rotations), and adjusted for differences in slice-time. Subsequently, the high-resolution structural images were co-registered to the mean functional image via rigid-body transformation, segmented into grey/white matter and cerebrospinal fluid probability maps, and were spatially normalized to Montreal Neurological Institute (MNI) space alongside with all functional volumes using the segmented images and a priori templates.
1 and T threshold = 3. 3 are reported in the Supplementary Results section (Supplementary Fig. S5 ). The resulting connected brain components, the links of which showed a significant relationship with individual variability in thought patterns, were then defined as mask graphs to threshold individual functional connectomes, which were then carried forward onto graph theoretical analyses  [87]. Network Neuroscience Analysis. Graph theoretical metrics in this study were calculated using MATLAB functions obtained from the publicly available Brain Connectivity Toolbox (https://sites. google. com/site/ bctnet/). Commonly used in the identification of hub regions that greatly influence the efficiency of a network in distributing information  [88] , network strength denotes one of the most fundamental measures in weighted functional connectomes and thus formed the basis of our network neuroscience analysis approach.
This behavioural and imaging data were preprocessed using the same procedures, resulting in the exclusion of four participants due to excessive motion. For the thought decomposition scores, the hierarchical clustering and PCA decompositions obtained from the initial cohort was imposed on the ratings obtained from the second session  [90]. Intraclass correlation coefficients (ICC) were employed to assess the reliability of thought component scores and the associated brain connectivity (as denoted by fractional strength) from the first and second sessions. Furthermore, we assessed a potential link between the change in thought patterns between the two sessions and the change in the associated brain functional connections using Pearson correlations. Mediation Analysis. Finally, we used linear regressions to identify the relationship between component scores on the identified patterns of thought, the fractional strength (natural log) of connected components and the participants self-reported scores on the psychological and social domains of the WHOQOL-BREF questionnaire.
ACKNOWLEDGMENTS This study was supported by the  European Research Council  (Project ID:  646927 ) (https://cordis. europa. eu/project/rcn/198057/factsheet/en) awarded to JS and a grant from the  John Templeton Foundation , " Prospective Psychology Stage 2: A Research Competition ". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors extend their gratitude to  Mladen Sormaz ,  Charlotte Murphy ,  Hao-Ting Wang  and  Giulia Poerio  for their invaluable contribution to the scanning of participants. In addition, the authors thank  Andre Gouws ,  Ross Devlin ,  Jane Hazell  and the rest of the  York Neuroimaging Centre  staff for their support in setting up the imaging protocol and scanning. Finally, we thank all the participants for their time and effort in taking part in this study.
Data analysis Categorical variables were summarised by frequencies and percentages. Continuous data were presented as medians with interquartile range (IQR). We compared groups using nonparametric tests for continuous variables and chisquare test for categorical variables. To assess regional differences, the countries of participants were categorised into five categories used regularly by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and a sixth category of non-European countries (eTable 2). In univariable analyses we assessed which sociodemographic and mentorship characteristics were associated with mentorship satisfaction. Multivariable logistic regression analysis was performed, which included only the factors with a p value < 0. 20 according to univariable analyses.
ECG was recorded at 1000 Hz (using PowerLab equipment with Chart Pro software; AD Instruments, Dunedin, New Zealand). A distance of 10 cm was marked with a pen in the middle of their arm, to designate the area of skin to be stroked. Participants were instructed to look straight across the room and to concentrate on the upcoming tactile stimulation, which was applied with a soft brush of 7. 5 cm width. Prior to the experiment, the experimenter practiced delivering controlled brush strokes at different velocities (guided by a visually-timed meter), with a constant pressure of 0. 4 N by brushing on a weighing scale. The experimenter manually delivered brush-strokes over the arm in a proximal-distal direction, at 5 different velocities: 0. 3, 1, 3, 10, or 30 cm/s. Each velocity was presented 5 times in a pseudo-randomized order.
Data analysis The satisfaction with touch frequency, sum of touch in the last week, Arnetz-Hasson score, BDI score, the STQ total score and subscores for both groups, and heart rate measures were compared using independent t-tests. Tactile thresholds were compared by a Mann-Whitney U test. Frequencies for the three different categories of "general touch frequency" were compared between both groups by means of Fisher's exact test. ECG heart rate (HR) data were averaged separately for the first 3 min and the last three minutes of stimulation. For these intervals and for each participant, the mean heart rate and mean standard deviation of inter-beat intervals as a measure of heart rate variability (HRV) were calculated. The difference between the last and the first 3 min was calculated, constituting the variables "HR change" and "HRV change".
Acknowledgements We thank  Petra Valej  for collecting the data, and  Thomas Eggert  for calculating the model fits. This study was supported by the  Swedish Research Council  (grant  2011-1529  to U. S. ) and a grant from the  European Union  ( FP7-People-COFUND  ( Marie Curie Actions)  to  R. A,  under  REA  grant agreement No. 608743 ). This publication reflects only the view of the authors and the  European Union  is not liable for any use that may be made of the information contained herein.
Participants We recruited participants for Study 1 using Amazon Mechanical Turk (MTurk). Participants were paid $1. 35 for completing the task. Institutional Review Board approval was obtained before data collection. All tasks in Study 1 were hosted online using Survey Monkey. Participants who did not successfully complete all of the attention check items within the SST or did not complete the entire survey were excluded from data analysis. A total of 17 participants were removed based on incorrect responses to attention check items. After data cleaning, we had a total sample of 219 participants. The majority of participants were female (58%) and White (79%). Median participant age was 36 (range: 20-69) years of age.
Procedure After completing the SST, 54% of the sample (n = 118) completed the RMET and a self-report SI scale. Following these tasks, this group completed verbal ability and cognitive reflection tasks. After completing all tasks, participants were asked to report their sex, age, race, highest level of educational attainment, and estimated annual household income. Median time to complete this version of the study was 36 min. The remaining 46% of the full sample (n = 101) completed the SSQ along with self-report measures of trait anxiety and positive and negative affect. We split our sample into these two groups in order to collect data on more assessments while attempting to keep the length of the study to less than one hour. This group also completed the verbal ability and cognitive reflection tasks and reported the same demographic information.
Results First, we examined the reliability and item functioning of the initial SST form. All SST items were scored as correct or incorrect based on a scoring key developed before data collection. All SST scores were calculated as the sum of all correct responses. Overall, the SST displayed modest internal consistency (α = 0. 66). All SST item statistics are reported in Table  1. All but four of the 32 SST items were observed to have positive corrected item-total correlations (CITCs). Nearly half of the test items had CITCs ≥0. 20 (17 of 32 items). Yet, the average inter- Three shapes wait in line to enter the building in the top right corner (A). The orange X is the first to enter (B). After the orange X exits the building (C), the purple star enters (D). After the purple star exits, the yellow plus cuts in front of the blue triangle (E).
Study 2 The goal of Study 2 was to create an improved SST form suitable for use as an individual differences measure of SI in other research projects. Prior to Study 2, we tested several, alternate questions for each of the 32 animations. All of the new questions used the same format as the original items in Study 1. Each question was paired with an individual video and four response options. These new questions, along with questions from the original form, were tested using a sample of participants gathered from MTurk. Items were divided up into four test forms of 32 items each. Each participant completed one form (ns between 70 and 74 per form). Complete item text and data from this exercise are included in the online archive associated with this project. Based on these results, we constructed a revised, 25-item form of the SST.
Participants We recruited participants from MTurk to complete all measures using an online survey hosted by Qualtrics. Participants were paid $7. 25 for completing the task. Our Institutional Review Board determined that this study was exempt from review. 28 participants were removed because they had responded incorrectly to at least one of four attention check items. After data cleaning, we had a total sample of 505 participants. A majority of participants were male (56%) and White (64%). Median participant age was 33 with participants ranging from 19 to 73 years of age. In order to collect adequate sample sizes to investigate for potential subgroup differences, we recruited additional MTurk participants from racial/ethnic minorities using TurkPrime b n = 101. (Litman, Robinson, & Abberbock, 2017).
To determine how to model the SST items, we conducted a parallel analysis  (Hayton, Allen, & Scarpello, 2004). The parallel analysis results suggested that two components be retained, but the eigenvalue for the second component (=1. 36) was nearly equal to the eigenvalue generated in the simulated data (=1. 34). Based on this result and the general tendency for parallel analysis to retain too many components  (Hayton et al. , 2004) , we modeled the SST items as a single factor. All models included 23 SST items and 12 cognitive items as indicators and were estimated using the lavaan package in R  (Rosseel, 2012). Based on nested model comparisons, loading the SST items and cognitive items onto two correlated latent factors provided better fit than a one-factor model (Δχ 2 = 81. 13, Δdf = 1, p <.
It is also important that future research investigate how the SST may fit within or complement existing models of SI or EI. For example, much work has been done to develop a theoretical framework of EI  (Mayer et al. , 2016) , but this model has not yet incorporated research on ToM or social cognition. Second, all participants in both studies were recruited using MTurk. Even though MTurk has been reported to typically provide more diverse samples and equally reliable data as undergraduate or typical Internet samples  (Buhrmester, Kwang, & Gosling, 2011) , our reliance on this data source may limit the generalizability of our results. Third, because our validation samples consisted exclusively of adults for whom we did not attempt to measure or select for psychopathology, the SST has not yet been validated for use with children or clinical populations.
Acknowledgements We thank  Jaclyn Mandart  for her research assistance and  David Winn  for his work in developing the animations used in Studies 2 and 3. We also thank  Patrick Heck ,  Anh Huynh ,  Eric Loken , and  Jonathan Wai  for their helpful feedback on this paper. Portions of this research were conducted while AR, SLH, JBF, and EH were students, and  CFC  was a professor, at  Union College in Schenectady, New York.
Participants received either course credits or £10 for taking part in the study. Stimuli and task. Figure  1  (panel a) shows a schematic illustration of a trial (the figure has been adapted from the doctoral thesis of X. J. 29  ). Spatial frequency gratings (4 cycles/degrees, contrast 100%, 4. 5° eccentricity) were displayed on the horizontal meridian, on the left and right side (37% and 63% of the screen width, respectively) of a centred white fixation dot (visual angle 6°) on a grey background (see Fig. 1b ). Stimuli were generated using MATLAB 2012a (64 bit) and presented using the Psychophysics Toolbox Version 3. 0. 8  30. Each trial was initiated by resting the right hand on a lever device. A movement cue ("R" or "L") was then presented on the screen, indicating which grasp should be prepared with the right hand.