dataesr/scientific-paragraphs-categorization huggingface dataset:
Inclusion Visit and Retrospective Data Collection Inclusion and exclusion criteria verification will be based on the patients' records. At T0, between March 17 th and May 10 th , 2020, sociodemographic characteristics, psychiatric diagnoses, and lockdown conditions will be collected in medical records if available. Data will be anonymized and stored in a database. Anxiety (GAD-7) and coping scores (Brief COPE) will be collected for Saint Etienne participants during clinical visits and reported in the database.
Obviously, the more transitory the neural substrate, the more individual perceptual factors or biases can change over time. Past studies that have measured individual biases in a few sessions over time have noted that biases generally seem stable (e. g. ,  Carter & Cavanagh, 2007; Drissi-Daoudi et al. , 2020). In our own previous work, we have shown that idiosyncratic differences-while remaining quite stable in many cases-do undergo change, and aspects of this change are systematic. Using large datasets with measurements on time scales ranging from seconds to months, we have shown that idiosyncratic perceptual biases undergo changes that obey certain regularities. For example, idiosyncratic biases can be momentarily deviated from their stable values, but tend to 'decay' or return to their original values over several tens of seconds  (Wexler et al.
Participants Participants were recruited using the crowdsourcing platform Prolific. co, where they had previously signed up to participate in internetbased studies. All demographic data was self-reported and gathered on the crowdsourcing platform prior to signing up for our study. Participants were required to have reported being fluent in English, and to have no mental health conditions that are uncontrolled by medication and that impact their daily lives. Participants were also required to have a desktop or laptop computer available to perform the study (rather than a tablet or telephone). 1000 participants completed the study, and were paid £2. 10 (the median duration of the study was 16. 4 min). According to self-reports, 58. 3% of the participants were men, 41. 2% were women, and 0. 5% did not report their gender.
Apparatus The experiment was performed within an internet browser window on each participant's own computer (either desktop or laptop, but neither tablet nor telephone). From data reported by the participant's internet browser, we determined that 73. 7% of the participants had Windows as their operating system, 19. 2% Mac OS, 4. 1% Chrome OS, and 3% other types of Linux. The most common browsers were Chrome (73. 7%), Firefox (14%), Edge (6. 1%), Safari (4. 4%), and Opera (1. 5%) (Internet Explorer was excluded because it was incompatible with the experiment software). The most common monitor resolutions were 1920x1080 (23. 9%), 1366x768 (22. 7%), 1440x900 (12%), 1536x864 (11. 3%), and 1280x720 (5. 8%). The experimental software automatically switched to full-screen mode whenever possible (and otherwise requested that the participant do so manually).
Participants and procedure The data come from the EU Kids Online IV project  (Smahel et al. , 2020; Zlamal et al. , 2020). It consists of international representative survey data centered on online behavior and activities, including the online risks and possible negative experiences of European adolescents. Our survey is based on a representative sample of adolescents who use the internet and are aged 11 to 17 (N = 3 855, M age = 13. 98, SD = 1. 91, 52. 1% girls) from the Czech Republic (n = 2 227, M age = 14. 03, SD = 1. 80, 51. 3% girls), Poland (n = 928, M age = 13. 71, SD = 2. 09, 54. 1% girls), and Slovakia (n = 700, M age = 14. 17, SD = 1. 97, 52. 1% girls). Data collection in the individual countries took place from 2017 to 2018. The collection method differed, including both computer-assisted classroom surveys (the Czech Republic, Poland) and computer-assisted personal interviews done in households (Slovakia).
Analysis A multiple-group confirmatory factor analysis was used to assess alternative models of cyberaggression. Because the indicator variables were dichotomous, the weighted least squares estimator with adjusted means and variances, employing a matrix of polychoric correlations, was used (Mplus 8. 5 software,  Muthén & Muthén, 1998 -2020). These models were (1) the one-factor model that assumes that all six indicators of cyberaggression represent one common factor, (2) the two-factor model that assumes that cyberhate and cyberbullying represent two different correlated factors, and (3) the bifactor model that assumes that all six indicators reflect a common general risk factor, and, at the same time, reflect two specific factors that pertain to cyberhate and cyberbullying (see Fig. 1 ). While the bifactor model does not allow intercorrelations between general and specific factors, intercorrelations between specific factors are theoretically admissible  (Reise at al.
Statistical Analysis Group sizes were pre-determined with the G*Power v3. 1. 9. 4 software (Heinrich-Heine-Universität Düsseldorf), to detect differences of at least 1. 25 standard deviations between groups, with 80% power an alpha error of 0. 05. Data were analysed with GraphPad Prism7. 02 software. For behaviour and electrophysiology, outliers have been identified using Grubbs' test and data tested for normality and variance homogeneity. Statistical differences between mean values between two groups were tested using Student's T-test or Mann-Whitney. When multiple groups were compared, one-way ANOVA analysis followed by Friedman's, Tukey's, or Dunnett's multiplecomparison test and Kruskal-Wallis analysis followed by Dunn's multiple-comparison test were used, as indicated in the figure legends.
Acknowledgements We thank the  Charcot-Marie-Tooth Association  for its continuous support. We are grateful to  Dr. Patrizia D'Adamo  and the  Mouse Behaviour Facility at IRCCS Ospedale San Raffaele. Part of this work was carried out in ALEMBIC, an advanced microscopy laboratory established by  IRCCS Ospedale San Raffaele and Università Vita-Salute San Raffaele. M. D. is supported by  Fondazione Telethon  ( GGP19099 ),  CMTA  ( Charcot-Marie-Tooth Association, USA)  and  CMTRF (Charcot-Marie-Tooth Research Foundation ).
ACKNOWLEDGMENTS We thank all the members of  Marsicano's lab  for useful discussions and for their invaluable support. This work was funded by:  INSERM, European Research Council  (Endofood,  ERC-2010-StG-260515  and  CannaPreg ,  ERC-2014-PoC-640923 ,  MiCaBra ,  ERC-2017-AdG-786467 ),  Fondation pour la Recherche Medicale (FRM ,  DRM20101220445 ,  SPF201809006908 ,  SPF201909009268 ),  Université de Bordeaux (IdEx Bordeaux) ,  Region Nouvelle Aquitaine ,  Agence Nationale de la Recherche  ( ANR , NeuroNutriSens  ANR-13-BSV4-0006 ;  ORUPS ANR-16-CE37-0010-01 ;  CaCoVi ANR 18-CE16-0001-02 ; MitObesity  ANR 18-CE14-0029-01 ;  BRAIN ANR-10-LABX-0043 ). Graphical abstract created with biorender. com.
Funding information Grant/ Award Numbers :  ANR-10-LABX-0043 ,  ANR 18-CE14-0029-01 ,  ANR 18-CE16-0001-02 ,  ANR-16-CE37-0010-01 ,  ANR-13-BSV4-0006 ;  Université de Bordeaux (IdEx Bordeaux) ;  Fondation pour la Recherche Medicale , Grant/ Award Numbers:  SPF201909009268 ,  SPF201809006908 ,  DRM20101220445 ;  European Research Council , Grant/Award Numbers:  ERC-2017-AdG-786467 ,  ERC-2014-PoC-640923 , ERC-2010-StG-260515; INSERM.
We measure this here by asking participants to self-report whether they preemptively avoided these situations. Making categorical comparisons between patient groups and controls is an important line of research that has delivered major insights into changes in cognition and behaviour in psychological illness. An alternative approach is to use a transdiagnostic dimensionality-based approach. We chose this approach here due to its ability to extract multiple distinct dimensions within large population data and across different questionnaires and traditional diagnostic boundaries  [16, 17]. Specifically, we collected questionnaires with measures covering a wide range of psychiatric symptoms and then extracted symptom dimensions cutting across traditional disorder boundaries. This is particularly relevant for 3 reasons.
Here, we wanted to examine the relationship between OCD symptoms and the presence of preemptive decision-making strategies that occur at the beginnings of search sequences and which prevent the person from entering situations later in the sequence in which they would be likely to perform poorly because of a bias in decision-making. We hypothesise that OCD symptoms are associated with self-reported avoidance of situations in which biases will manifest (hypothesis 3). For data collection and analysis, we employed a "split sample" approach  [33, 34]. We first collected a discovery sample that we used to refine and develop hypotheses in a data-driven way. We then used a confirmation sample to test the preregistered hypotheses in a statistically rigorous way. This approach thus combines the benefits of preregistration-reduced risk of false positives due to analytical freedoms-with those of exploratory science-the analytical approach being best adapted to the specific data.
Results A total of 830 participants completed all parts of this preregistered study (link to OSF https:// osf. io/dfg2u). Exclusions: 33 participants (4%) due behavioural responses suggesting lack of task understanding; 4 participants (0. 5%) due to not completing all questionnaires or failing the questionnaire attention checks and 112 participants (13%) from the behavioural analyses of initial searches; and 44 participants (5%) from later searches due to too little variation in their responses to allow analysis (see Methods). This resulted in 756 participants included for preregistered analyses linking transdiagnostic dimensions and behaviour and 795 participants included in analyses of only the questionnaires (demographics in Table  1 ). Power analyses of the discovery sample (n = 449 for analyses linking transdiagnostic dimensions and behaviour; see link/reference for detailed results of the discovery sample data submitted for preregistration) had suggested a sample size of 750 participants to achieve 95% power for all preregistered analyses.
Participants Participants were recruited via the online platform Prolific. ac. We aimed to acquire 750 data sets that fulfilled the preregistered inclusion criteria detailed below; 756 were collected instead due to technical reasons in the Prolific system. This sample size was determined based on the discovery sample shown below to give a power of 0. 95 (see Fig  6  and section "Power calculation" below for details). Ethical approval for the study was given by the Oxford University Central University Research Ethics Committee (CUREC) (Ref-number: R54722/RE001). All participants provided informed consent before taking part in the study.
7. Second, parameter recovery showed Pearson correlations of r > 0. 85 between simulated and recovered parameters for all parameters. Third, parameter recovery across all schedules showed r > 0. 85 for all parameters, suggesting that parameter estimates obtained using different schedules were comparable i. e. , there was no systemic schedule bias affecting the results. Any given participant shares the schedule they performed with a tenth of the other participants. The study was programmed in JavaScript and the study was run using JATOS  [67].
Participant exclusion criteria As our data were acquired online, we took several steps to ensure data quality. First, we ensured participants fully understood the task instruction by giving them an automated tutorial followed by a multiple choice test that they needed to pass with 100% correct before being able to do the task. Second, we gave participants bonus payments depending on their performance to increase motivation. Third, we excluded participants based on task performance as well as poor completion of the questionnaires. We excluded participants whose behaviour either suggested they did not engage with the task or whose responses were too unbalanced to allow analysis. Behavioural analyses were performed separately for the initial search decision on each trial and for all later searches, see below, and data exclusion reflected this.
Participants were excluded due to bad performance if, when considering only the initial choice of each trial, for the 10 trials with highest offer minus search value (i. e. , strongest value pressure to accept) they accepted the initial offer less often than for the 10 trials with the lowest offer minus search value. Participants were excluded due to imbalanced responses if-separately for the initial search decision of each trial and for later searches-they had fewer than 25 accept or spin responses. Exclusion based on questionnaire responses: 1. Not completing all psychiatric questionnaires 2. Five questions were repeated. If a participant's responses were different by 2 or more points on average, then they were excluded. Participants who only failed inclusion criteria for the behavioural models were included in the factor analyses of the questionnaire data (see below).
Participants who only failed inclusion criteria for one behavioural model (e. g. , only for the initial search) were included in the analyses of the questionnaire data and the other behavioural model. derive prospective value") for details. We illustrate the key factors contributing to our modelbased prospective value estimate (see Fig  1D ). For this, we first conducted a regression predicting prospective value based on the average value of all alternative options in the environment (myopic value), the standard deviation of all alternatives, the number of searches available, the cost of searching, and all possible 2-to 4-way interactions. To compute how well these components together approach prospective value, we computed the variance explained (R 2 ).
Analysing behaviour-RTs We analysed how different factors influenced participants' RTs using multiple regressions similar to the decision analyses to look at the factors influencing deliberation times. For the initial decision, we can look at which planning factors participants deliberate over during the initial planning. In the later decisions, we can look at the factors participants consider during execution and updating of the decision strategy as the number of opportunities depletes. First, as there was no response deadline, outlier RTs (very slow RTs) were possible. RTs over 10 seconds were therefore discarded. After outlier rejection, RTs were modelled using a hierarchical regression with a shifted lognormal distribution, fit using the brms package that implements Bayesian multilevel models in R, using exactly the same regressors as for the decision model ("RT model-initial search"), shown here in standard regression modelling notation (as implemented in R): RT � 1 þ Offer þ Cost þ Myopic þ Prospective þ Average prospective value change þ ð1 þ Offer þ Cost þ Mypoic þ Prospective þ Average prospectiive value change jIDÞ; where "|ID" appears in Wilkinson notation and indicates that all regressors were given a "random" in addition to a "fixed" effect or, in other words, that they were modelled hierarchically.
Factor analysis of questionnaire measures Summary: In the discovery sample, we performed an exploratory factor analysis. Here, this was validated using confirmatory and exploratory factor analysis. As this showed good replication of results, we next applied the factor weights from the discovery sample to the confirmation sample to obtain factor scores for each person for each factor. g  In detail: We performed exploratory factor analysis of the discovery sample to derive the transdiagnostic clinical dimensions within the psychiatric questionnaires using the R package "psych. " The aim of factor analysis is to explain the relationship (correlation) between the questionnaire items or subscales as a function of underlying factors. For this, we computed the previously established subscale summary scores for each questionnaire and entered those scores into a factor analysis.
Validation of factor analysis h To validate the factor analysis in the confirmation sample, we ran 2 complementary checks. First, we performed a confirmatory factor analysis. For this, we used the Lavaan package  [85] , specifying 4 factors, as in the exploratory factor analysis on the discovery sample. Factors were allowed to correlate but subscales were assigned to a single factor (the one with the highest loading in the discovery sample). However, in reality, the loadings of some subscales were actually relatively evenly spread across factors, making the categorical assignment less than ideal  (Fig 3, S3 Fig). We judged fit using 3 distinct indices, i. e. , CFI, SRMR, and RMSEA. As the results of the confirmatory factor analysis did not unambiguously suggest that the 4-factor solution was a good fit to the data, we followed our prespecified procedure and next performed an exploratory factor analysis on the confirmation sample.
Relating questionnaires to task performance and self-reports We used the discovery sample to establish the exact links to be tested in the confirmation sample between questionnaire measurements, i. e. , clinical dimensions and task performance (decisions, RTs, and post-task self-reports). Specifically, for the analyses of decisions and RTs, we performed linear regressions (R using Stan and brms software) in which the behavioural/selfreport measures were related to the 4 psychiatric factors (compulsivity, apathy, depression, and social anxiety), while controlling for the covariates gender (coded as categorical variable with levels female, male, or "other"); education (coded as ordered factor  [75]  with levels: GCSE, A-levels/High school, BSc, MSc, and PhD; and age. In the analyses of parameters derived from the decision model, we included inverse temperature as a measure of general task performance and choice consistency.
For the ordinal regressions (predicting selfreports based on task behaviour), Bayesian credible intervals on the parameter estimates (odds ratios) were judged as significant if they did not include one and they were judged as not significant if they included one. i We choose the 95% credible intervals as those are the intervals that determine the 95% likelihood of containing the true parameter value, given our data. Mediation and moderation analyses (see below) were run using the R package processR (via R package lavaan)  [80, 91]. Significance was be determined as p < 0. 05 1 tailed. From the discovery sample, we had derived specific compound hypotheses that were be tested in the confirmation sample (summarised in Table  3 ). For completeness, we also report all significant findings from the discovery sample again in the confirmation sample but marked as exploratory (see below).
This work was funded by the  Medical Research Council  ( MR/N014448/1 , Skills Development Fellowship,JS;  G0902373 , grant MFSR, https://mrc. ukri. org/), the  Biotechnology and Biological Sciences Research Council  ( BB/ R01803/1 ,  BB/V004999/1 ,  AFL  Fellowship NK; Discovery Fellowship JS, https://bbsrc. ukri. org/), the  Clarendon Fund  (studentship, HAT https:// www. ox. ac. uk/clarendon) and the  Wellcome Trust  ( WT100973AIA  and  203139/Z/16/Z , https:// wellcome. org/MFSR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data analysis Data analysis was performed in MATLAB and R (version 4. 0. 4), including the packages rstan  [73]  (version 2. 21. 2), shinystan  [74]  (version 2. 5. 0), brms  [75] [76] [77] [78]  (version 2. 15. 0), psych  [79]  (version 2. 1. 3), processR c (based on PROCESS  [80] , version 0. 2. 6) implemented using lavaan  [81]  (version 0. 6-8), ggplot2  [82]  (version 3. 3. 3), ggpubr  [83]  (version 0. 4. 0), tidyverse  [84]  (version 1. 3. 1), and sjPlot  [85]  (version 2. 8. 7).
SELECTION CRITERIA Research was done following PRISMA guidelines using computerized databases (PubMed/MEDLINE, Scopus)  (12). An independent review was conducted by two of the authors (SSA and MAC) in order to identify original research articles published in English and French languages at any time till November 2021. The following key terms were used: ( "MS" OR "multiple sclerosis") AND ("fatigue") AND ("non-invasive brain stimulation" OR "NIBS" OR "transcranial magnetic stimulation" OR "TMS" OR "theta burst stimulation" OR "TBS" OR "motor evoked potential" OR "MEP" OR "cortical excitability" OR "corticospinal excitability" OR "intracortical inhibition" OR "intracortical facilitation" OR "silent period" OR "interhemispheric inhibition" OR "transcranial direct current stimulation" OR "tDCS" OR "transcranial random noise stimulation" OR "tRNS").
g. ,  Astington & Jenkins, 1999; Peterson & Siegal, 2000; Hale & Tager-Flusberg, 2003; Ruffman et al. , 2003; Astington & Baird, 2005; Slade & Ruffman, 2005; Miller, 2006; de Villiers & de Villiers, 2014; Richardson et al. , 2020). There is thus reason to suspect a close cognitive and neural connection between language and ToM processing, and perhaps even overlap between the neural resources that support both kinds of skills. Overlap (bottom row) is observed primarily in and around the angular gyrus/temporo-parietal junction (TPJ) and along the superior temporal sulcus, with some scattered overlap in the lateral frontal cortex. B. Whole-brain probabilistic atlases for the language and ToM localizer contrasts created from two large fMRI datasets by overlaying individual activation maps (see  Lipkin et al.
's claim is based on a single paradigm evaluated in a single (albeit large) dataset, and alternative explanations in terms of, for example, general visual semantic processing (e. g. ,  Zaidel, 1987; Joseph, 1988)  cannot be ruled out. Under such accounts, the somewhat stronger responses to social conditions would be explained by greater overall attention to social content. Thus, more research is needed to understand the precise contribution of the RH language homotopes to semantic and specifically social cognition. If the language network is not involved in making inferences about others' thoughts, how then do these inferences enter into language processing in order to inform rapid incremental sentence comprehension (e. g. ,  Shibata et al. , 2010; Regel et al. , 2011; Kaakinen et al. , 2014) ? We hypothesize that this occurs via rapid feedback from the ToM network, which can then be used to inform interpretation.
Participants Nineteen students (age range, 19-47 years; seven male) from the University of Bern participated in the experiment in exchange for course credit or on a voluntary basis. All observers reported normal or corrected-to-normal visual acuity. Observers were naïve regarding the aim of the study. Before the experiment, participants signed a consent form and were informed about the general procedure. The experimental protocols were approved by the local ethics committee at the University of Bern. All procedures were in accordance with the Declaration of Helsinki. The datasets generated during the study are available on the OSF database (https://osf. io/6t4qh/).
, 2017). The number of lines presented, the location of the lines, and the spacing conditions were specified as fixed effects and subject as a random effect. Predicted values were calculated with the ggpredict function of the ggeffects package  (Lüdecke, 2018). The marginal (R 2 m ) and conditional (R 2 c ) pseudo R-squared statistics were computed to quantify goodness of fit using the r. squaredGLMM() function from the MuMIn package  (Bartón & Bartón, 2014; Johnson, 2014). R 2 m represents the variance explained by fixed effects and R 2 c the variance explained by both fixed and random effects. Assumptions underlying the models were checked with diagnostic plots of residuals using the DHARMa package  (Hartig, 2017). Analysis of deviance tables (using type II Wald chi-square tests) for the model were calculated using the car package.