dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This study was supported by  INRAE  and  University of Bordeaux ;  Idex Bordeaux "chaire d'installation"  ( ANR-10-IDEX-03-02 ) (PT),  NARSAD   Young Investigator Grants  from the  Brain and Behavior Foundation (PT) ,  ANR  " SynLip " ( ANR-16-CE16-0022 ) ( PT ),  ANR  " FrontoFat " ( ANR-20-CE14-0020 ) ( PT ),  ANR StriaPOM  ( ANR-21-CE14-0018 ) (PT),  Institut de Recherche en Santé publique (IReSP) Aviesan APP-addiction 2019 (PT) ,  Labex "BRAIN" (PT and RW) ,  Region Nouvelle Aquitaine   2014-1R30301-00003023  (PT);  Fondation pour la Recherche Médicale (FRM "Environnement et Santé")  (GF and  PT ),  ANR  " HA-CTion " ( ANR-19-HBPR-0002 ) (GF and PT);  PRESTIGE-2017-2-0031  (AC), " Fondation pour la Recherche Médicale " SPF201809007095 (AC);  Canadian Institute for Health Research  ( 201309OG-312343-PT ;  201803PJT-399980-PT ) (BG); FD and RW are recipients of PhD fellowships from the  French Ministry of Research.
A random forest classification model was built on a training set using crossvalidation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. RESULTS: rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group.
METHODS AND MATERIALS Dataset: Imaging Data We used a subset of ENIGMA resting-state functional magnetic resonance imaging data (n = 584) aggregated from 9 sites around the world that assessed PTSD symptoms of TE individuals using CAPS-4 or CAPS-5, while also providing itemlevel CAPS data. CAPS-4 and CAPS-5 scores were homogenized by calculating the percentage of the severity score relative to the maximum score possible for each instrument  (21). The final sample included 239 PTSD participants (of whom 100% also met ITRED criteria), 106 ITRED-only participants, and 239 TE-only participants. Each individual was assigned to 1 of 3 groups: those meeting a PTSD diagnosis, irrespective of their ITRED status, were assigned to the PTSD group; those meeting the ITRED criteria, without meeting PTSD criteria, were assigned to the ITRED group; TE individuals not meeting either diagnosis criteria were considered TE-only participants.
Image Acquisition and Processing All imaging data were acquired at contributing sites and processed with standardized protocols of the ENIGMA-PGC (Psychiatric Genomics Consortium) Consortium previously used in large-scale studies of other disorders  (22). Preprocessing was carried out with ENIGMA HALFpipe workflow (https://github. com/HALFpipe/HALFpipe), which is based on fMRIPrep (https://fmriprep. org/en/stable). Briefly, processing steps for T1 images included skull stripping, tissue segmentation, and spatial normalization to Montreal Neurological Institute space. Preprocessing steps for ITRED Functional Connectivity Feature Classification functional images included motion correction using FSL MCFLIRT (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/MCFLIRT), slice time correction using AFNI (Analysis of Functional Neuro-Images) 3dTshift (https://afni.
ML Analysis We built classification models for distinguishing 3 groups: 1) PTSD; 2) ITRED-only; and 3) TE-only (no PTSD or ITRED). A random forest (RF) classifier was used for classification. An RF classifier has been widely used to classify individuals with psychiatric disorders from control individuals using neuroimaging data  (28). RF uses a nonparametric method that does not depend on the distribution of the dataset. It also provides better generalization power and is able to handle multicollinearity, a problem that is common in the neuroimaging field  (29). ML algorithms and cross-validation (CV) pipelines were implemented in Python's scikit-learn library  (30). First, we randomly split the data into two subsets: 70% of the data was used for training and validation, and the remaining 30% was used as a hold-out test dataset.
Brain features with 30% of missing data were dropped from further analysis. RobustScaler from the scikit-learn library was used to scale the data, and missing values were imputed with the mean of the training dataset. The same scaler was applied to the test set. Based on previous research, we used 10-fold CV within the training sample, which generally provides better and more stable performance across different datasets, compared with leave-one-out CV  (31). To achieve an equal number of samples for each group for each site, random undersampling was applied to the imbalanced groups, with the undersampling transform applied to the training dataset on each split of a repeated 10-fold CV. For each model, classification performance was measured using standard metrics including accuracy, sensitivity, specificity, and area under the receiveroperating characteristic curve (Table  4 ).
Test Sample We used the remaining 30% of the data as the independent test dataset. Again, we used an RF classifier among 3 main contrasts (PTSD vs. ITRED-only; PTSD vs. TE-only; ITREDonly vs. TE-only) to test the classification performance to distinguish between the groups in an independent dataset (see Table  4 ). We found that PTSD versus TE-only and ITRED-only versus TE-only classifications showed similar AUC results of medium effect sizes (test AUC = 60%, Cohen's d = 0. 354; and test AUC = 61%, Cohen's d = 0. 396, respectively) (Figure  1A, B ). Conversely, classifying PTSD from ITRED-only showed AUC results at chance level (test AUC = 45%, Cohen's d = 0) (Figure  1C ).
DISCUSSION The present study explored brain-correlates of ITRED using rsFC data aggregated from 9 worldwide sites of the ENIGMA PTSD Consortium. We used RF ML to detect differences between PTSD participants, ITRED-only participants, and TEonly participants. Our RF classification performance was rigorously examined on both CV AUC and test AUC using a fully independent dataset. Our results showed that rsFC features differentiated TE-only participants from PTSD participants and ITRED-only participants with about 60% accuracy and medium effect sizes on the CV sample and test sample. As hypothesized, our results showed that rsFC features did not differentiate PTSD participants from ITRED-only participants, performing below chance level (i. e. , 45%), even though the PTSD group had significantly higher symptom levels (i.
However, large-scale imaging datasets, such as the one used here, show comparable results (around 62%) across different psychopathologies  (32) (33) (34) , including PTSD classification (X. Zhu, Ph. D. , et al. , unpublished data, 2022). Importantly, ML studies using largescale imaging datasets rectify some major limitations of single-site studies, especially data overfitting, which may result in overly optimistic results. Thus, using large-enough samples improves generalization beyond what can be achieved by independent datasets  (35). The medium effect sizes of both the CV and test AUC data support the validity of current results. Unlike the results differentiating TE-only participants from both PTSD and from ITRED-only participants, the ML model differentiated between PTSD and ITRED-only participants below chance level (45% test AUC), despite the fact that the PTSD group showed significantly higher symptom levels than the ITRED-only group.
This work was supported by  Fyssen Foundation , by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  grant agreement No  716931 -GESTIMAGE -ERC-2016-STG , by the " Agence Nationale de le Recherche "  ANR-16-CONV-0002  (ILCB) and the  Excellence Initiative of Aix-Marseille University  (A* MIDEX ).
Data pre-processing Annotation data were preprocessed by linearly interpolating between time points with a resolution of 4 Hz. This value was chosen as a compromise between being low enough as to capture most of the movements of the participants in the interface, and high enough so as to reduce the size of the dataset for data analysis. Synoptic graphs of the annotations were produced indicating at each time which area of the triangle (i. e. , "with", "against", "without" or "center") each musician was in. Two examples of such graphs can be seen in Figure  2   1. As can be seen in Figure  2 , there was a lot of variability between the performances (including between performances by the same duo): for example, the musicians' relational intents were much more stable and continuous in Duo 5 Track 4 than in Duo 6 Track 1.
Potential interactions between co-presence and familiarity were also tested. Thus, the successive models were the following: The models were fitted with the function lmer from the R package lme4 and compared using a likelihood ratio test, with Satterthwaite estimations of degrees of freedom. In the section "Effects of co-presence and familiarity on musicians' interactional dynamics" below, we report the results of model comparisons, as well as beta, standard errors, t-values and p-values for the effect of co-presence and familiarity on each dependent variable.
We selected three acoustic features that reflect meaningful properties of CFI performances  (Goupil et al. , 2021)  to run our analysis: -Root-Mean-Square (related to the musician's loudness) -Spectral centroid (related to the musician's timbre) -Fundamental frequency (f0) estimate (related to the fundamental frequency of the sounds produced by the musician) These features were computed on all of the individual WAV files, using the python library Librosa with a window size of 46 milliseconds, ending up with three vectors of data for each musician and each performance. These data were linearly interpolated between time points with a resolution of 4 Hz so as to match the same time points as the annotation data. We then computed two Pearson correlation coefficients for each performance: one between the vectors of data at the times when both musicians intended to play "with" one another ("withwith" condition) and the other between the vectors of data at the times when at least one musician did not intend to play "with" her partner ("other" condition).
Italian-speaking sample Seventy-five native Italian-speaking adult participants took part in this behavioral study. Criteria for inclusion were the following: (i) no diagnosis of psychological/neurological disorders; (ii) no reading delay in word, non-word and text reading tasks (-1. 5 sd from published norms); (iii) reported normal or corrected-to-normal vision and hearing, (iv) intelligence within the normal range (cut-off score ≥ 7) as measured with the WAIS-IV subtest of Matrix Reasoning  (Wechsler, 2008). This latter cut-off score led to the removal of three participants; thus, data from seventy-two (72) participants remained (40 females, mean age: years old).
French-speaking sample Sixty-four French-speaking adult participants participated in the study. Criteria for inclusion were the following: (i) no diagnosis of psychological/neurological disorders; (ii) no reading delay in word, non-word and text reading tasks (-1. 5 sd from published norms); (iii) reported normal or corrected-to-normal vision and hearing. One female participant was excluded from the analysis due to undergoing an ADHD diagnosis process. In total, data from sixty-three (63) participants remained (53 females, mean age: 25 years old). In Supplementary Section 3, we have included an additional sample consisting of fourty-one French-speaking participants. For this group, data was collected solely for domain-general tasks and our novel audiovisual learning task. In both samples, written informed consent was obtained prior to participation.
The Multiple Object Tracking task was programmed using Matlab v. R2017, Psychophysics Toolbox  (Brainard, 1997; Kleiner et al. , 2007; Pelli, 1997). The audio-visual associative task was programmed using Psy-choPy v1. 82  (Peirce, 2007). The Auditory Attention task was programmed using Javascript. Finally, the Odd-one-out task was provided by Creyos (see,  Hampshire, Highfield, Parkin, & Owen, 2012).
Data analysis 2. 3. 1. Audio-visual learning task The audio-visual associative learning task was first screened for participants who did not demonstrate learning. Participants were excluded according to two different exclusion criteria: either percent correct was not significantly above chance on the last 1 / 3 of trials (64 trials; chance performance of 33%; assessed using a one-tailed binomial test) or their accuracy decreased with time (i. e. , mean accuracy from the first 64 trials to the second, or from the second 64 trials to the third). With partially overlapping sets of participants, this led to 10 out of Italian-speaking participants being excluded, and 6 out of 63 Frenchspeaking participants being excluded. The data was then fit with mixed-effects Bayesian nonlinear regression using the TEfits package in R  (Cochrane, 2020 ; see Supplement for model code) which itself utilizes Stan via the brms package  (Bürkner, 2017;  for similar modeling approaches see  Dale, Cochrane, & Green, 2021).
3). accuracy = asymptote + (startasymptote) × trialNumber rate (1) accuracy = asymptote + (start -asymptote) × 2 (1-trialNumber)/rate (2) accuracy = asymptote + (start -asymptote) × 2 ((1-trialNumber)/rate ) shape (3) In Eqs. 1 and 2 there are three free parameters, however, in all three functions' models we fixed start to 1 / 3 or the expected chance performance when first encountering the task. Then, within the nonlinear mixed-effects model, the trajectories of accuracy change defined by asymptote, rate [a time constant associated with a fixed percent of change from start to asymptote], and shape [acceleration] were simultaneously estimated within generalized linear mixed-effects models (see Eqs. 4, & 6, in brms/lme4 "Wilkinsen" model notation) logit(asymptote) ∼ Intercept + trialDifficulty + language + (trialDifficulty | participant) (4) log2(rate) ∼ Intercept + trialDifficulty + language + (trialDifficulty | participant) (5) log(shape) ∼ Intercept + (1 | participant) (6) Estimation of parameters on logit or log scales provided the ability for parameter estimates to, in principle, vary along all real numbers, while constraining the trajectories of learning to have accuracies [asymptotes] bounded at [0,1] while time constants [rates] and shapes were bounded to positive reals.
Estimated model fixed effects were first assessed. Then, point estimates of by-participant (i. e. , random-effects) parameters were extracted from the model, and these parameters were subsequently used in tests of relation to other measures (e. g. , attention or memory; see  Cochrane & Green, 2021a , 2021b; Dale et al. , 2021). Last we used a simulation-based approach to determine the sensitivity of our analyses to individual-level variations and to identify a lower bound on the experimental method's number of trials. We simulated true learning trajectories for new participants given the means and standard deviations of participant-level parameter point estimates, with each simulated dataset to test recovery varying in number of trials (from 15 to 200 in 5-trial increments) and number of participants (from 10 to 100 in 5-participant increments).
e. , with a shape parameter fixed a priori). There is evidence in previous work that relaxing this assumption and allowing the Weibull function's shape parameter to be estimated provides better fit to learning data  (Cochrane & Green, 2021b; Gallistel et al. , 2004; Leibowitz, Baum, Enden, & Karniel, 2010). Indeed, when allowing by-participant estimates of Weibull shape to augment the previous exponential (with a fixed starting and asymptotic accuracy), we observed decisive evidence for the Weibull model over the more restricted exponential model (over 15 runs, median BF log3 = 36. 90). As such, we used the fixed-asymptote Weibull model in all further analyses. After selecting the learning model using the above comparisons, we extracted by-participant point estimates of parameters. The fixed-asymptote Weibull model also fit better than the descriptive model with basis functions allowing for flexible estimation of changes over time (median BF log3 = 331.
Recovery analyses: Sensitivity of methods to individual differences A core question addressed in these two studies involved the extent to which inter-individual differences in learning could be rapidly assessed. Our behavioral results demonstrated that individual differences in learning were reliably measured and related to cognitive measures. As an additional demonstration of our ability to capture learning, we used the empirical model to simulate 1024 new datasets and applied the same analytical approach to these new datasets. The ability to recover interindividual differences in learning (i. e. , corresponding to the point estimates on the y-axis of Fig. 4  and Fig. 5 ) was then assessed. Recovery of inter-individual differences was quite good (see Fig. 7 ). Participant-level Spearman rank correlations between estimated and true generative parameters were fairly insensitive to participant number, and tended to be very high with trial numbers over about 112.
In the current study, we gathered data from both an Italian-and a French-speaking sample, for confirmation of the results' language-independent nature. While many of the previous studies examined learning in a test phase that followed passive exposure to the audio-visual pairs (e. g. ,  Xu et al. , 2020) , we developed a task that tracks learning progression on a trial-totrial basis, thus allowing us to fit a fully continuous-time model to the data. Research to date has typically averaged participants' performance over long periods of time (e. g. , blocks) and used rather indirect measures of learning speed (e. g. , training duration). Such procedures typically assume that learning is constant over large portions of time. Many of the previous approaches thus use questionable assumptions in the estimation of learning rates.
This contrasts with learning measured at the level of blocks or testing sessions (e. g. ,  Xu et al. , 2020). Bayesian model comparison showed that inter-individual variations in performance asymptotes should not be included in our learning models, and similar model comparisons would be straightforward to implement in novel datasets. In this way, we confirmed the primacy of individual differences in learning rate, as opposed to variations in asymptote. By applying the aforementioned methods, we were thus able to demonstrate that between-participant and difficultymodulated variations in learning were due to differential rates of learning, as opposed to differences in asymptotic performance. This observationand especially the absence of differences in asymptotic performancesuggests that the observed disparities between participants are not due to variations in stimulus "learnability" (i.
e. , because all stimuli had the same fixed asymptote). All models we tested enforced monotonic increases in performance over time (i. e. , treating decrements as noise rather than signal), which in longer studies or with richer datasets could be relaxed in order to test models including additional time-sensitive phenomena (e. g. , fluctuations in sustained attention, fatigue). While our comparison to the more-flexible descriptive model did not support the need for such flexibility in our data, increased flexibility may be particularly important in less high-functioning populations (e. g. , young children, patients). Fourth, we identified the functional form of change, which conformed much more closely to an exponential function than to a power function. Functional forms of change provide indications about the underlying processes of change: exponential functions tend to imply a simpler learning mechanism than power functions  (Dosher & Lu, 2007; Newell & Rosenbloom, 1981).
Acknowledgements This work has been funded by the  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie grant  no. 661667 , LearningDeterminants, as well as a  Junior grant  from  Institut Universitaire de France  to I. A. and the  NCCR Evolving Language   SNF 51NF40_180888 ,  SNF 100014_178814 , as well as the  Office  of Naval Research  N00014-20-1-2074  to D. B.
Procedure Participants were invited to accept or decline storage of their responses for research purposes. When using the app, participants were asked to complete the ASSIST  (Heslop et al. , 2013)  for screening and assessing the severity of their cannabis use. The app also includes the Marijuana use Motives Measure (MMM;  Simons et al. , 1998). Participants then received a computer-tailored personal feedback report about their motives for cannabis consumption. Assessments and data werereported anonymously. In this context, the collection was not submitted to an ethics committee. The Swiss Human Research Act does not apply to this survey (Chapter 1, Section 1, Article 2, Scope: 2c) inasmuch as it involves anonymously collected health-related personal data (https://www. fedlex. admin. ch/eli/cc/2013/617/en).
Finally, to examine the relationship between motives, sex and problematic cannabis use as assessed by the ASSIST, a multiple linear regression analysis was computed. The dependent variable was the ASSIST score, while the independent variables were the five motivation scales, sex, and age categories. Interaction between sex and motives for cannabis use were also examined. Since residuals were not normally distributed due to outliers, robust methods have been used  (Heritier et al. , 2009). Predictors in the regression analysis were considered statistically significant at p <. 05, corrected for multiple tests by using  Benjamini and Hochberg's (1995)  false discovery rate (FDR) procedure. Analyses were performed with R version 4. 1. 2 (R Core Team, 2021). EFA was performed with the psych package  (Revelle, 2022) , whereas CFA and Multigroup confirmatory analyses (MGCFA) were performed with the lavaan package  (Rosseel, 2012).
Participants The study received ethical approval (filed under Id 2017-A03614-49 from the regional ethical committee, Comité de protection des personnes sud Méditerranée I). Twenty-six (17 females) right-handed native speakers of French participated in exchange for monetary compensation. Two participants (1 female, 1 male) were excluded from further analyses due to excessive head movements during data acquisition. The average age of the remaining 24 participants was 26. 2 (SD 3. 5). No participant reported any history of language, neurological, or hearing disorders.
Materials Target stimuli were 36 CCVCC syllables (C = consonant, V = vowel) composed of French phonemes. In one half of the stimuli, these phonemes were combined in a phonotactically legal way in French, and their production therefore required less monitoring, even if the syllable was meaningless. The other half of the stimuli were syllables consisting of novel combinations of phonemes (phonotactically illegal), requiring increased monitoring as their production had to be learned (e. g. ,  Segawa et al. , 2015). Two-consonant clusters were selected from the French language database lexique. org (New et al. , 2001)  and filtered for their frequency of occurrence at the beginning or at the end of the syllable  (New & Spinelli, 2013). The clusters with high frequency (141. 6 ± 251. 6) were used to form low monitoring load stimuli, and those with a frequency close to zero (0.