dataesr/scientific-paragraphs-categorization huggingface dataset:
FACS Coding As we wanted to associate short video extracts to attributions made by laypersons, it was important to code all the facial actions that could have an impact on the observers. The ANVIL software was used for the annotation, with 61 tracks for the face (FACS;  Ekman et al. , 2002)  and 22 for the bodily action coding in time. The analysis of the latter coding is outside the scope of this article. The FACS coding was performed by a certified FACS coder and was verified by a second certified FACS coder. The second coder annotated 12 % of the videos (randomly assigned). Both coders used the FACS manual as a constant reference criterion. In assessing the precision of scoring, we looked at the frameby-frame agreement by computing Cohen's Kappa (k) for face action coding  (Cohen, 1960).
Data Analysis For each video (n = 39), the annotation in terms of FACS units was quantified by computing the total duration of this AU in a video. We selected this measure as the length of videos was dependent on the duration of present AUs leading to the decoding of a mental state, and therefore the percentage of time an action is present in a video clip is not informative. Stepwise regression analyses with backward selection were performed using SPSS 16. 0J (SPSS Japan, Tokyo, Japan). Stepwise regression analyses are techniques for selecting a subset of predictor variables  (Ruengvirayudh and Brooks, 2016). By conducting the analyses, we tested whether and how the subset of AUs could predict the decoding of specific emotions/appraisals. Individual regression analyses were conducted for each emotion/appraisal as the dependent variable.
Arm length was measured on a different day (due to some participants' unavailability, arm length was measured for 30 participants out of 41; 15 for each group). Data on forearm length was missing for 3 participants out of 41. Statistical Analysis. Two participants who changed posture between the pre and post tool use sessions and three who exceeded (>2. 5 standard deviation) population mean values in at least two kinematic parameters, were excluded from analyses. Additionally, two participants of group 1 did not correctly understand the length estimation task instructions (they estimated the elbow-middle fingertip distance instead of the elbow-wrist distance); their data for this task have been discarded from analysis. Hence, analyses were conducted on data from 22 participants from group 1 (11 males; mean age ± SD: 22.
The entire experiment was video-recorded. Kinematic recording and analysis. Kinematic recordings and analyses were as in Experiment 1. We excluded two participants from analyses: one because he systematically touched his left hand before grasping the object with his right hand, and one because he presented mean values exceeding 2. 5 standard deviation of the mean population  for at least two parameters. The data from the remaining 18 participants who took part in this within design (10 males; mean age ± SD: 22. 2 ± 2. 5; range from 18 to 26. 6) were submitted to a full factorial design permutation analysis on pre and post session kinematic parameters with Order ("Short-distance reach" or "Long-distance reach" first) as a between-subject factor and Session (pre/post short-distance reach/post long-distance reach) as a within-subject factor to investigate (1) if the order of the retracted posture sessions had an influence on the kinematics, and (2) if the starting posture had an influence on the kinematics of free-hand movements.
In addition to kinematics analysis, we reported the number of misreaches. All data analyzed during this study are included in this published article (and its Supplementary Information files). Results. Dysmetric reaching. Following the repeated execution of reach to grasp movements performed with either scapular retraction posture, 5 out of 18 participants undershot the object in one or two trials at the beginning of the post session (4 participants after the long reach condition and 1 after the short reach one). Misreaching movements when going back to the starting-point were also observed, affecting 10 out of 18 participants (10 after the short-distance reach condition, and 4 after the long-distance reach one). By replicating the newly reported finding that people misreach objects when a retracted arm position is imposed during proprioceptively guided actions, we provide support to the hypothesis that misreaching can occur independently of tool use.
When considering the whole dataset of the current study (somatosensation only available, group 1&2), our conservative permutation-based approach made clear that somatosensation is sufficient for tool-use to modify both latencies and amplitude peaks of the transport phase. This pattern of kinematic changes is similar to that reported when participants used the same tool under visual guidance  5, 34. When statistically comparing part of the current data (somatosensation only available in group 1) with those from Cardinali and coworkers 5 (somatosensation and vision available), it appeared that the multisensory condition may further enhance tool-induced effects on the peaks amplitude of the transport parameters. Moreover, Cardinali and colleagues  34  reported that longer-armed participants tended to move with longer latencies and smaller peaks as compared to short-armed group.
Acknowledgements This work was supported by the  Fondation pour la Recherche sur le Cerveau , the  Labex/Idex  ( ANR-11-LABX-0042 ), the  James S. McDonnell Foundation , the  ANR Samenta   ASD-BARN 01502  and the and the  ANR-16-CE28-0015-01. MM was supported by a grant from the  French Ministry of Higher Education and Research. LC was supported by the  Fondation Aveugles de France  and the  Fyssen Foundation.
Statistical analysis All fGCM and fAM data was tested for normality using histograms and the Shapiro-Wilk test, and data were log 10 transformed where not normally distributed. For EIA validation, differences in fGCM concentrations before and after the SIR-procedure were analysed with a paired sample t-test, while differences in fAM concentrations between male and female samples were analysed using an independent sample t-test. For the sample storage experiment, normalised relative fAM and fGCM levels were calculated by dividing every individual value across all time and temperature treatments by the mean hormone value (n = 3) at t = 0 h. Separate generalised linear models (glms) were run to analyse the levels of fGCM and fAM in samples over time (0-72 h and subsequently 0-24 h) and across the different temperature treatments (refrigeration vs.
Normalised relative data were then grouped by intervention stage for each treatment according to (a) all pre-SIR-procedure samples (pre-SIR); (b) the first faecal sample after the SIR-procedure, reflecting hormone levels associated with the procedure (SIR); (c) the first following sample (SIR+1); and (d) all subsequent samples (SIR�2; Fig  1 ). Differences in hormone levels between DAP vs. placebo treatment were then evaluated using an independent sample t-test. Differences between intervention stages were evaluated using oneway ANOVA and post-hoc Bonferroni. Hourly rates of each behaviour type were calculated for each pack each day. Normalised relative hourly rates of behaviour were calculated by dividing every hourly rate per day across all observations days and pheromone treatments by the mean pre-SIR hourly rate (comprising the 3 observation days pre-SIR;  Fig 1)  for each pack each season.
Regurgitation Expulsion of undigested food from the mouth, pharynx, or oesophagus. (Continued ) distributed, differences in rates of behaviour between DAP vs. placebo treatment, as well as differences in rates of behaviour between Day 1 and mean pre-SIR baseline levels were then evaluated using a Mann-Whitney U test. Changes over time were evaluated using Friedman test combined with post-hoc Wilcoxon signed-rank test. Statistical analysis was performed with SPSS Statistics 23 (IBM SPSS Statistics 23, SPSS Inc. , IBM, Armonk, New York, USA). P � 0. 05 was considered significant.
findsounds. com/, http://www. sound-effects-library. com/, and http://www. sounddogs. com/. When necessary, sounds were re-sampled at 44 kHz on 16 bits and edited to last 300 ms (5ms rise-time and 20ms fall-time), with Adobe Audition software (Adobe). All sounds were then RMS normalized with MATLAB (Mathworks). Finally, loudness was subjectively equalized by two listeners. Sounds were delivered at an intensity level judged comfortable by the participants at the beginning of the experiment.
Behavioral data A button press before target onset was considered as a false alarm (FA). A trial with no button press after target onset and before the next cue onset was considered as a missed trial. A trial with no FA and with a button press after target onset was counted as a correct trial. Reaction-times (RTs) to targets were analyzed in the correct trials only. Based on previous results  (Bidet-Caulet et al. , 2015) , the difference in RTs between the NoDIS and DIS1 conditions can be considered as a measure of the facilitation effect triggered by distracting sounds whereas the difference in RTs between the 9 DIS3 and DIS1 conditions can be considered as a good approximation of the attentional capture effect.
Statistical Analysis of Behavioral data Statistical analyses were conducted using the software JASP  (Wagenmakers et al. , 2018). RTs and percentages of correct trials were submitted to a Bayesian repeated-measure ANOVA with DISTRACTOR (four levels: NoDIS, DIS1, DIS2, DIS3) and MODALITY (three levels: AA, VA, VV) as within-participant factors. Post-hoc comparisons (based on the default t-test with a Cauchy prior) between levels of the MODALITY and of the DISTRACTOR factors were also conducted using the software JASP. Effects of MODALITY on the facilitation effect (RT NoDIS -RT DIS1 ) and the attentional capture effect (RT DIS3 -RT DIS1 ) were investigated as planned post-hoc analyses of the MODALITY by DISTRACTOR interaction, using Bayesian one-way ANOVA with MODALITY (three levels: AA, VA, VV) as the within-participant factor.
Statistical Analysis of Behavioral data RTs and percentages of correct trials were submitted to a Bayesian repeated-measure ANOVA with DISTRACTOR (four levels: NoDIS, DIS1, DIS2, DIS3) and DELAY (two levels: VA, VA-long) as within-participant factors. Post-hoc comparisons (based on the default t-test with a Cauchy prior) between levels of the DELAY factor were also conducted using the software JASP. Effects of DELAY on the facilitation effect (RT NoDIS -RT DIS1 ) and the attentional capture effect (RT DIS3 -RT DIS1 ) were investigated as planned post-hoc analyses of the DELAY by DISTRACTOR interaction, using Bayesian t-tests. Bayesian t-tests were also used to investigate the difference between the attentional capture effect and zero in each task. Detailed output of the Bayesian ANOVA are reported in the Table  1.
Statistical Analysis of Behavioral data RTs and percentages of correct trials were submitted to a Bayesian repeated-measure ANOVA with DISTRACTOR (four levels: NoDIS, DIS1, DIS2, DIS3) and AROUSAL (two levels: HA, LA) as within-participant factors. Post-hoc comparisons (based on the default t-test with a Cauchy prior) between levels of the DISTRACTOR factor were also conducted using the software JASP. Detailed output of the Bayesian ANOVA are reported in the Table  1. Unless stated otherwise, in the ''Results'' section, mean values and standard errors of the mean are indicated. ERP scalp topographies were computed using spherical spline interpolation  (Perrin et al. , 1989). ERPs were analyzed using the software package for electrophysiological analysis To explore the effect of the arousing content on the ERPs to distracting sound (HA vs.
, 1986). To increase sensitivity while taking into account multiple testing in the time dimension, we used a randomization procedure proposed by  Blair and Karniski (1993). For each permutation of the dataset, the maximum number of consecutive significant time-samples in the entire 0-350 ms time-window was computed. Over all the permutations, we thus obtained the distribution of this maximum number under the null-hypothesis. In this distribution, the 95th percentile corresponds to the maximum number of consecutive significant samples one can obtain by chance with a risk of 5%. We required all the effects to last at least this number of samples. This procedure was run separately for each channel. As the three electrodes were tested independently, a Bonferroni correction was applied on computed p-values to correct for family-wise errors  (Weisstein, 2004).
Recall Profiles For each participant and each type of unit, we calculated the ratio of the number of units in a given category (e. g. , Anc) to the length of recall (i. e. , total number of idea units recalled). Performances were thus related to the length of the texts produced and were expressed, for each reading condition and for each age, out of 1 (or 100%; see Table  2 ). The data highlighted differences in recall profiles between the young and older adults. A 2 (age group) * 3 (reading condition) * 3 (unit status: Anc, Ad, F) ANOVA computed on these weighted results 4 revealed (a) a significant effect of unit status, F(2, 180) = 244. 86, p <. 001 (65. 62% Anc units > 29. 17% Ad units > 5. 21% F units); and (b) a significant Unit status * Age group interaction, F(2, 180) = 56. 18, p <.
Acknowledgements In memory of  Professor Brigitte Le Bouëdec , founder of the  Psychology Laboratory of the University of Angers , who encouraged and supported our work on the effects of reading perspectives. The authors sincerely thank the anonymous reviewers whose comments and suggestions allowed us to improve this paper. et ses tableaux, les chambres à l'étage et l'armoire de sa mère remplie de fourrures. Marc dit qu'il a de l'argent de poche tant qu'il veut, il est prétentieux. Marc montre aussi sa propre salle de bain. Ses soeurs en ont une autre près de leur chambre. Dans la chambre de Marc, la lumière pénètre par un trou dans le plafond car le toit est vieux et pourri » Pierre et Marc font du vélo (F) jusqu'à la maison de Marc (IL) Ils rentrent dans la maison (IL) Il y a de grandes barrières (A) Il  y a trois portes pour rentrer dans la maison.
The wedge plot in Fig. 2b  shows these similarity values, with the radius of each wedge proportional to the correlation strength and area proportional to variance explained. Pain-predictive activity independent of manipulations was concentrated in somatomotor, ventral attention, and visual networks (see Supplementary Tables  1, 6 ). We further calculated the similarity between the Path b map and association test z-score brain maps of 525 terms from the Neurosynth meta-analytic database  49. While relationships with terms should be interpreted with caution and are only suggestive, they can be useful for contextualizing findings and provide a more fine-grained comparison with existing large-scale databases than the canonical seven-network parcellation. The top ten unique terms associated with pain-related (path b) effects were themed around pain processing (e.
Methods Participants. Thirty-eight healthy volunteers with no history of psychiatric, neurological, or pain disorders participated in the experiment. The data from two participants were excluded from all analyses due to large movements artifacts and delays in the experiment, leaving a final sample of 36 participants (20 female and 16 male, mean age = 27. 1 years, range 18-50 years). All participants provided informed consent, and were paid for their time. The study was conducted according to the Declaration of Helsinki and approved by the Institutional Review Board of the Department of Psychology and Neuroscience at the University of Colorado Boulder. Materials and procedures. At the beginning of each of 96 trials, participants saw two types of cues (see Fig. 1 ): Simple visual displays of putatively social information indicated the pain ratings of ten fictitious previous participants.
Psychophysiological measures. Electrodermal (skin conductance) activity was measured at the middle and ring finger of the (non-dominant) left hand. Pulse rate (not analyzed here) was measured at the left index finger. Both signals were recorded using a BIOPAC MP150 system and Acknowledge software at a sampling rate of 2000Hz. The data were low-pass filtered offline with a cutoff of 1 Hz. fMRI data acquisition and preprocessing. Functional brain activity was acquired using a Siemens TrioTim 3T scanner, covering the brain in 26 interleaved transversal slices (3. 4 -mm isotropic voxels), with a T2* weighted EPI GRAPPA sequence (TR = 1. 3, TE = 25 ms, flip angle = 50°, FOV = 220 mm). Prior to preprocessing of functional data, time points that are potential global outliers (spikes) were identified based on meeting any of the following criteria: (a) absolute value of global signal > 10 median absolute deviations (m.
All statistical tests used a significance threshold of p < 0. 05 (two-sided), and tests were planned contrasts with paired t tests unless otherwise specified. Code for all analyses is available at https://github. com/canlab. Single-trial SCR estimates were obtained using the SCRAlyze toolbox  109  , which uses a GLM approach similar to standard event-related fMRI analysis. Each participant's skin conductance time series was low-pass filtered (1 Hz cutoff) and normalized. Regressors for single-trial pain onsets were convolved with a canonical SCR function  110  and fitted to the time series data, yielding single-trial beta estimates for SCRs. Parallel to the behavioral data analysis, multilevel GLMs were employed to test the effects of experimental conditions on SCRs in 49 °C trials. Each participant's functional imaging data was submitted to a GLM that contained single-trials regressors for each heat pain stimulus (modeled as a stick function).
The GLM further included event-related regressors for the four different cue conditions (2 Social × 2 CS), expectations ratings, and pain ratings (boxcar regressors with the duration of the response time). A total of 24 movement regressors (movement estimates for displacement and rotation in three dimensions, their derivatives, squared movement estimates, and derivatives of squared movement estimates) and spike regressors (coded as 1 for the outlier time point and zero for all other time points) were added as regressor of no interest to control for motion artifacts. Single-trial regressors that had variance inflation factors > 2. 5 (indicating potential multicollinearity) were excluded from subsequent analyses. SPM8 and Matlab2014b were used for parameter estimation. Single-trial beta estimates of pain-evoked functional BOLD activity in 49 °C trials (to control for temperature) were submitted to multilevel brain mediation analysis (code available at https://github.
Further, thresholded maps were compared with the Neurosynth database (525 term-based meta-analytic images, 2013 data release from neurosynth. org, Yarkoni, et al. 49  ) to identify the ten unique most positively correlated terms associated with the activation maps. The Matlab text mining toolbox was used to create wordcloud plots of the decoded terms, with color brightness reflecting increasing correlation strength of terms with maps (arbitrarily scaled). To investigate the contribution of different parts of the frontoparietal network, we first normalized (z-scored) individual (unthresholded) path a beta images and then computed the average activation in the frontoparietal network and in its three subnetworks on each side (right and left Control A, B, and C) for each subject, using a 17-network parcellation  51.
Data availability The source data underlying Figs. 1c-e, 3a-b, 4a, 5a-b, and Supplementary Figs. 2a-c, 5 , and 6b are provided as a Source Data file. Contrast images are available on Neurovault (https://identifiers. org/neurovault. collection:5677). Other data can be obtained from the authors upon reasonable request.
One of the challenges in breaking up diagnostic borders in favour of more homogenous clusters of symptoms sharing common neural mechanisms, is that A c c e p t e d M a n u s c r i p t biological and behavioral data need to be combined in a meaningful way. A suitable method for this purpose is canonical correlation analysis (CCA), which is formulated to maximize the correlation between variables in two views of a dataset. This technique has previously been used to link complex behavioural datasets with functional brain networks  10. However, CCA has a number of limitations: It cannot be applied to data with more features than samples, results are difficult to interpret owing to a lack of localizability, and it is only possible to find relations between two sets of variables. The first two of these issues can be addressed using sparse canonical correlation analysis (sCCA)  11, 12  , which has been used to find modes of shared variation between resting state functional connectivity MRI, and behavioral measures in adolescents and young adults  12.
We carried out model selection in a training dataset of 70% of the data (n=467), and model validation in the testing dataset of the remaining 30% (n=199). To enhance stability we resampled the data and retained only variables that contributed to the model in 90% of resamples (see methods under the sub-heading: Stability Selection, and Supplementary Figure  1 )  21. Demographic information on the full sample, training and testing sets is given in Supplementary Figure  2. The msCCA-regression procedure we used in this investigation is designed to maximise associations between variable-sets. For this reason, all msCCA-regression significance values reported in the text are one-sided. Using msCCA-regression, we found a significant relation between a subset of six DAWBA symptoms (see Figure  1 ), and VBM, TBSS and rs-fMRI measures (r=0.
59(465), p<0. 001). The behavioural correlates derived from DAWBA covered symptoms linked to feelings of depression, anxiety and somatic problems, as well as temper and attentional problems (Figure  1 ). The model was also significant when applied to the test dataset (r=0. 23(197), p<0. 001, 95% CIs=0. 13, ∞) (Figure  1 ), explaining 5. 30% of the variance between psychiatric symptoms and the brain. Brain A c c e p t e d M a n u s c r i p t correlates derived from VBM, TBSS and rs-fcMRI measures were associated with this anxiety/depression symptom group with correlation values of: r=0. 16(197), p=0. 017, 95% CIs=0. 040, ∞; r =0. 14(197), p=0. 040, 95% CIs=0. 037, ∞ and r=0. 15(197), p=0. 029, 95% CIs=0. 041, ∞ respectively (with all p-values FWEcorrected for multiple comparisons, see methods under the sub-heading: Analysis Design, and Supplementary Figure  3 ).
Study Protocol We developed a method, termed msCCA-regression to find multivariate relationships between psychiatric symptoms, and multiple neuroimaging modalities simultaneously; In this case, voxel-based morphometry (VBM)  18  measures of grey matter volume, fractional anisotropy (FA) derived from DTI data, and normalized using tract based spatial statistics (TBSS)  19  , and resting state functional connectivity neuroimaging measures  43. msCCA-regression analysis was carried out in the general population IMAGEN sample, when participants were aged 19. Additional analyses were then applied in order to localize associations between psychiatric symptoms, and neuroimaging measures of brain structure and function. We then analyzed neuroimaging and symptom data at age 14 in order to determine whether this multivariate relationship already existed at this earlier time-point.